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Comput Med Imaging Graph. 2010;34:46–54.\nTran HH, Matsumiya K, Masamune K, Sakuma I, Dohi T, Liao H. Interactive 3-D navigation system for image-guided surgery. Int J Virtual Real. 2009;8:9–16.\nSuenaga H, Hoang Tran H, Liao H, Masamune K, Dohi T, Hoshi K, et al. Real-time in situ three-dimensional integral videography and surgical navigation using augmented reality: a pilot study. Int J Oral Sci. 2013;5:98–102.\nWang J, Suenaga H, Liao H, Hoshi K, Yang L, Kobayashi E, et al. Real-time computer-generated integral imaging and 3D image calibration for augmented reality surgical navigation. Comput Med Imaging Graph. 2015;40:147–59.\nWidmann G, Stoffner R, Bale R. Errors and error management in image-guided craniomaxillofacial surgery. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2009;107:701–15.\nNoh H, Nabha W, Cho JH, Hwang HS. Registration accuracy in the integration of laser-scanned dental images into maxillofacial cone-beam computed tomography images. Am J Orthod Dentofacial Orthop. 2011;140:585–91.\nFitzpatrick JM, West JB. The Distribution of Target Registration Error in Rigid-Body Point-Based Registration. IEEE Trans Med Imaging. 2001;20:917–27.\nCasap N, Wexler A, Eliashar R. Computerized navigation for surgery of the lower jaw: comparison of 2 navigation systems. J Oral Maxillofac Surg. 2008;66:1467–75.\nEggers G, Kress B, Muhling J. Fully automated registration of intraoperative computed tomography image data for image-guided craniofacial surgery. J Oral Maxillofac Surg. 2008;66:1754–60.\nZhu M, Chai G, Zhang Y, Ma X, Gan J. Registration strategy using occlusal splint based on augmented reality for mandibular angle oblique split osteotomy. J Craniofac Surg. 2011;22:1806–9.\nKang SH, Kim MK, Kim JH, Park HK, Park W. Marker-free registration for the accurate integration of CT images and the subject's anatomy during navigation surgery of the maxillary sinus. Dentomaxillofac Radiol. 2012;41:679–85.\nBouchard C, Magill JC, Nikonovskiy V, Byl M, Murphy BA, Kaban LB, et al. Osteomark: a surgical navigation system for oral and maxillofacial surgery. Int J Oral Maxillofac Surg. 2012;41:265–70.\nMarmulla R, Luth T, Muhling J, Hassfeld S. Markerless laser registration in image-guided oral and maxillofacial surgery. J Oral Maxillofac Surg. 2004;62:845–51.\nShamir RR, Joskowicz L. Geometrical analysis of registration errors in point-based rigid-body registration using invariants. Med Image Anal. 2011;15:85–95.\nKhadem R, Yeh CC, Sadeghi-Tehrani M, Bax MR, Johnson JA, Welch JN. Comparative tracking error analysis of five different optical tracking systems. Comput Aided Surg. 2000;5:98–107.",{"EN":382},"This study evaluated the use of an augmented reality navigation system that provides a markerless registration system using stereo vision in oral and maxillofacial surgery. A feasibility study was performed on a subject, wherein a stereo camera was used for tracking and markerless registration. The computed tomography data obtained from the volunteer was used to create an integral videography image and a 3-dimensional rapid prototype model of the jaw. The overlay of the subject’s anatomic site and its 3D-IV image were displayed in real space using a 3D-AR display. Extraction of characteristic points and teeth matching were done using parallax images from two stereo cameras for patient-image registration. Accurate registration of the volunteer’s anatomy with IV stereoscopic images via image matching was done using the fully automated markerless system, which recognized the incisal edges of the teeth and captured information pertaining to their position with an average target registration error of \u003C 1 mm. These 3D-CT images were then displayed in real space with high accuracy using AR. Even when the viewing position was changed, the 3D images could be observed as if they were floating in real space without using special glasses. Teeth were successfully used for registration via 3D image (contour) matching. This system, without using references or fiducial markers, displayed 3D-CT images in real space with high accuracy. The system provided real-time markerless registration and 3D image matching via stereo vision, which, combined with AR, could have significant clinical applications.",{"EN":384},"Vision-based markerless registration using stereo vision and an augmented reality surgical navigation system: a pilot study",{"VOID":386},"10.1186\u002Fs12880-015-0089-5","http:\u002F\u002Fbmcmedimaging.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12880-015-0089-5",[389,406,421,448,463,479,502],{"id":390,"sortIndex":111,"researcher":18,"roles":391,"affiliations":393,"properties":403},"fe5ccd25-2e13-42d9-8b99-dde20864a632",[392],"AUTHOR",[394],{"id":18,"sortIndex":19,"affiliation":395,"properties":18},{"id":396,"createTime":397,"updateTime":397,"relativeEntities":398,"slug":399,"properties":400,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"5aeb75ee-965a-452d-b83d-2bfaa5764719","2024-04-16T14:13:21.238+00:00",[],"Department-of-Mechano-Informatics-Graduate-School-of-Information-Science-and-Technology-The-University-of-Tokyo-Tokyo-Japan",{"title":401},{"EN":402},"Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan",{"title":404},{"VI":405},"Huy Hoang Tran",{"id":407,"sortIndex":112,"researcher":18,"roles":408,"affiliations":409,"properties":418},"1d18e36e-661d-4343-8b38-4481acc03a9e",[392],[410],{"id":18,"sortIndex":19,"affiliation":411,"properties":18},{"id":412,"createTime":413,"updateTime":413,"relativeEntities":414,"slug":18,"properties":415,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"cf2743a0-153b-4334-907b-678dc1395677","2024-02-09T05:35:47.964+00:00",[],{"title":416},{"VI":417},"Department of Mechanical Engineering, School of Engineering, Tokyo Denki University, Tokyo, Japan",{"title":419},{"VI":420},"Takeyoshi Dohi",{"id":422,"sortIndex":110,"researcher":18,"roles":423,"affiliations":424,"properties":445},"0405157f-453b-44e2-bbb6-460cc15705d4",[392],[425,437],{"id":426,"sortIndex":111,"affiliation":427,"properties":436},"75884065-23f5-4260-bbeb-07ae7620ff34",{"id":428,"createTime":429,"updateTime":430,"relativeEntities":431,"slug":432,"properties":433,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"007deb45-99c4-44f9-b64a-f6e6ce41e84e","2024-01-16T15:38:54.454+00:00","2025-06-11T13:59:24.863+00:00",[],"Department-of-Biomedical-Engineering-School-of-Medicine-Tsinghua-University-Beijing-China",{"title":434},{"VI":435},"Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China",{},{"id":18,"sortIndex":19,"affiliation":438,"properties":18},{"id":439,"createTime":440,"updateTime":440,"relativeEntities":441,"slug":18,"properties":442,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"6243f94f-fc71-4dd5-8f83-c13c98af653d","2024-01-16T22:05:00.173+00:00",[],{"title":443},{"VI":444},"Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan",{"title":446},{"VI":447},"Hongen Liao",{"id":449,"sortIndex":307,"researcher":18,"roles":450,"affiliations":451,"properties":460},"24b80e81-e0ad-4afb-bbfd-57bb37c63e12",[392],[452],{"id":18,"sortIndex":19,"affiliation":453,"properties":18},{"id":454,"createTime":455,"updateTime":455,"relativeEntities":456,"slug":18,"properties":457,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"9ae0b0cd-ea42-4b83-ae70-d35350c6ef33","2023-12-28T18:41:13.653+00:00",[],{"title":458},{"VI":459},"Department of Oral-Maxillofacial Surgery, Dentistry and Orthodontics, The University of Tokyo Hospital, Tokyo, Japan",{"title":461},{"VI":462},"Kazuto Hoshi",{"id":464,"sortIndex":465,"researcher":18,"roles":466,"affiliations":467,"properties":476},"3ca7eb6e-9408-4439-a3b8-99c0250417df",6,[392],[468],{"id":18,"sortIndex":19,"affiliation":469,"properties":18},{"id":470,"createTime":471,"updateTime":471,"relativeEntities":472,"slug":18,"properties":473,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"df647d93-c6d3-4c36-b4c4-7291df91b62b","2023-12-27T16:17:52.998+00:00",[],{"title":474},{"VI":475},"Department of Oral–Maxillofacial Surgery, Dentistry and Orthodontics, The University of Tokyo Hospital, Tokyo, Japan",{"title":477},{"VI":478},"Tsuyoshi Takato",{"id":480,"sortIndex":155,"researcher":18,"roles":481,"affiliations":482,"properties":499},"12d5c82d-5ebd-4918-a570-bf53cf0f4b4c",[392],[483,488],{"id":18,"sortIndex":19,"affiliation":484,"properties":18},{"id":396,"createTime":397,"updateTime":397,"relativeEntities":485,"slug":399,"properties":486,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":487},{"EN":402},{"id":489,"sortIndex":111,"affiliation":490,"properties":498},"7eb05bff-0681-48e6-81b3-b29f48fedd22",{"id":491,"createTime":492,"updateTime":492,"relativeEntities":493,"slug":494,"properties":495,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"83feb19b-137f-49a2-84ec-da886f2a63ff","2023-11-29T21:46:51.810+00:00",[],"Faculty-of-Advanced-Technology-and-Surgery-Institute-of-Advanced-Biomedical-Engineering-and-Science-Tokyo-Women-s-Medical-University-Tokyo-Japan",{"title":496},{"VI":497},"Faculty of Advanced Technology and Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan",{},{"title":500},{"VI":501},"Ken Masamune",{"id":503,"sortIndex":19,"researcher":18,"roles":504,"affiliations":505,"properties":511},"5afbf853-964f-4a5e-9ae9-8bd8111f28d7",[392],[506],{"id":18,"sortIndex":19,"affiliation":507,"properties":18},{"id":454,"createTime":455,"updateTime":455,"relativeEntities":508,"slug":18,"properties":509,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":510},{"VI":459},{"title":512},{"VI":513},"Hideyuki 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JH, Lee HS, Kim EK, Moon HJ, Kwak JY. Malignancy risk stratification of thyroid nodules: comparison between the thyroid imaging reporting and data system and the 2014 American Thyroid Association management guidelines. Radiology. 2016;278(3):917–24.\nHegedus L, Bonnema SJ, Bennedbaek FN. Management of simple nodular goiter: current status and future perspectives [J]. Endocr Rev. 2003;24(1):102–32.\nKhizer AT, Raza S, Slehria AU. Diffusion-weighted MR imaging and ADC mapping in differentiating benign from malignant thyroid nodules. J Coll Physicians Surg Pak. 2015;25(11):785–8.\nKo SY, Lee HS, Eun-Kyung KKim EK, Kwak JY. Application of the thyroid imaging reporting and data system in thyroid ultrasonography interpretation by less experienced physicians. Ultrasonography. 2014;33(1):49–57.\nNoda Y, Kanematsu M, Goshima S, Kondo H, Watanabe H, Kawada H, et al. MRI of the thyroid for differential diagnosis of benign thyroid nodules and papillary carcinomas. AJR Am J Roentgenol. 2015;204(3):332–5.\nKwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, et al. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology. 2011;260(3):892–9.\nCheng PW, Chou HW, Wang CT, Lo WC, Liao LJ. Evaluation and development of a real-time predictive model for ultrasound investigation of malignant thyroid nodules. Eur Arch Otorhinoaryngol. 2014;271(5):1199–206.\nSasaki M, Sumi M, Kaneko K, Ishimaru K, Takahashi H, Nakamura T. Multiparametric MR imaging for differentiating between benign and malignant thyroid nodules: initial experience in 23 patients. J Magn Reason Imaging. 2013;38(1):64–71.\nOta H, Ito YF, Matsuzuka F, Kuma S, Fukata S, Morita S, et al. Usefulness of ultrasonography for diagnosis of malignant lymphoma of the thyroid. Thyroid. 2006;16(10):983–7.\nWang H, Wei R, Liu W, Chen Y, Song B. Diagnostic efficacy of multiple MRI parameters in differentiating benign vs. malignant thyroid nodules. BMC Med Imaging. 2018;18(1):50.\nWu Y, Yue X, Shen W, Du Y, Yuan Y, Tao X, et al. Diagnostic value of diffusion-weighted MR imaging in thyroid disease: application in differentiating benign from malignant disease. BMC Med Imaging. 2013;13(1):23.\nSchueller-Weidekamm C, Kaserer K, Schueller G, Scheuba C, Ringl H, Weber M, et al. Can quantitative diffusion-weighted MR imaging differentiate benign and malignant cold thyroid nodules? Initial results in 25 patients. AJNR Am J Neuroradiol. 2009;30(2):417–22.13.\nLiu J, Zheng D, Li Q, Tang X, Luo Z, Yuan Z, et al. A predictive model of thyroid malignancy using clinical, biochemical and sonographic parameters for patients in a multi-center setting. BMC Endocr Disord. 2018;18(1):17.\nAbdel Razek AA, Soliman NY, Elkhamary S, Alsharaway MK, Tawfik A. Role of diffusion-weighted MR imaging in cervical lymphadenopathy. Eur Radiol. 2006;16(7):1468–77.\nWang Q, Guo Y, Zhang J, Ning H, Zhang X, Lu Y, et al. Diagnostic value of high b-value (2000s\u002Fmm2) DWI for thyroid micronodules. Medicine (Baltimore). 2019;98(10):e 14298.",{"EN":557},"Diffusion-weighted imaging (DWI) and ultrasound are commonly used methods to examine thyroid nodules, but their comparative value is rarely studied. We evaluated the utility of DWI and ultrasound in differentiating benign and malignant thyroid nodules. A total of 100 patients with 137 nodules who underwent both DWI and ultrasound before operation were enrolled. The T1 and T2 signal intensity ratio (SIR) of each thyroid nodule was calculated by measuring the mean signal intensity divided by that of paraspinal muscle. The apparent diffusion coefficient (ADC) value and the SIR of benign and malignant thyroid nodules were analyzed by two-sample independent t tests. The sensitivity, specificity, and accuracy of DWI and ultrasound were compared with chi-square tests. There was no significant difference in the SIR between benign and malignant thyroid nodules. The ADC value was significantly different. At the threshold value was 1.12 × 10− 3 mm2\u002Fs, the maximum area under the curve was 0.944. The sensitivity, specificity, and accuracy were 84.9, 92.2, and 87.6% respectively. The corresponding values of ultrasound diagnosis were 90.1, 80.4, and 86.9%. Ultrasound has high sensitivity in differentiating benign and malignant thyroid nodules, and the ADC value has high specificity, but there is no statistical difference in sensitivity or specificity between the two modalities. DWI and ultrasound each have their own advantages in differentiating benign and malignant thyroid nodules.",{"EN":559},"A comparative analysis of diffusion-weighted imaging and ultrasound in thyroid nodules",{"VOID":561},"10.1186\u002Fs12880-019-0381-x","https:\u002F\u002Fbmcmedimaging.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12880-019-0381-x",[564,581,593,605],{"id":565,"sortIndex":110,"researcher":18,"roles":566,"affiliations":567,"properties":578},"f6196f12-2c20-49f0-93fd-7e03eac8dee5",[392],[568],{"id":18,"sortIndex":19,"affiliation":569,"properties":18},{"id":570,"createTime":571,"updateTime":572,"relativeEntities":573,"slug":574,"properties":575,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"48082c26-3f1f-4753-9d6f-e47d61d29089","2024-01-14T03:31:29.018+00:00","2024-09-24T15:12:18.004+00:00",[],"Department-of-Radiology-Shanghai-Ninth-People-s-Hospital-Shanghai-Jiao-Tong-University-School-of-Medicine-Shanghai-China",{"title":576},{"VI":577},"Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China",{"title":579},{"VI":580},"Jiliang Ren",{"id":582,"sortIndex":155,"researcher":18,"roles":583,"affiliations":584,"properties":590},"f83f424c-4a6b-45aa-9c9c-63da3df47b53",[392],[585],{"id":18,"sortIndex":19,"affiliation":586,"properties":18},{"id":570,"createTime":571,"updateTime":572,"relativeEntities":587,"slug":574,"properties":588,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":589},{"VI":577},{"title":591},{"VI":592},"Xiaofeng 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D, Fan W, Wong ND. Epidemiology of diabetes Mellitus and Cardiovascular Disease. Curr Cardiol Rep. p. 21, Mar 4 2019;21(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11886-019-1107-y.\nGroenewegen A, Rutten FH, Mosterd A, Hoes AW. “Epidemiology of heart failure,“ Eur J Heart Fail, vol. 22, no. 8, pp. 1342–1356, Aug 2020, doi: https:\u002F\u002Fdoi.org\u002F10.1002\u002Fejhf.1858.\nSchulz-Menger J, et al. Standardized image interpretation and post-processing in cardiovascular magnetic resonance – 2020 update: Society for Cardiovascular magnetic resonance (SCMR): Board of Trustees Task Force on standardized post-processing. J Cardiovasc Magn Reson. Mar 12 2020;22(1):19. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12968-020-00610-6.\nSlomka PJ et al. “Patient motion correction for multiplanar, multi-breath-hold cardiac cine MR imaging,“ J Magn Reson Imaging, vol. 25, no. 5, pp. 965 – 73, May 2007, doi: https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjmri.20909.\nSwingen C, Seethamraju RT, Jerosch-Herold M. 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Learning Deep Features for Discriminative Localization,“ (in English). Proc Cvpr Ieee. 2016;2921–9. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCVPR.2016.319.\nKingma DP, Ba J. “Adam: A method for stochastic optimization,“ arXiv preprint arXiv:1412.6980, 2014.\nPedregosa F, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.\nHo N, Kim YC. “Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model,“ Tomography, vol. 8, no. 6, pp. 2749–2760, Nov 14 2022, doi: https:\u002F\u002Fdoi.org\u002F10.3390\u002Ftomography8060229.\nHo N, Kim YC. Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification. Sci Rep. Jan 19 2021;11(1):1839. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-021-81525-9.\nLiew YM et al. “Motion corrected LV quantification based on 3D modelling for improved functional assessment in cardiac MRI,“ Phys Med Biol, vol. 60, no. 7, pp. 2715-33, Apr 7 2015, doi: https:\u002F\u002Fdoi.org\u002F10.1088\u002F0031-9155\u002F60\u002F7\u002F2715.",{"VI":662,"EN":663},"Nghiên cứu này nhằm phát triển và xác thực một phương pháp dựa trên học sâu để phát hiện chuyển động giữa các lần nín thở từ hình ảnh trục dài tim được ước tính, tái tạo từ một chồng hình ảnh cắt ngang tim. Dữ liệu hình ảnh cộng hưởng từ tim cine từ tất cả các lát cắt cắt ngang và các lát cắt trục dài 2-\u002F3-\u002F4 buồng được xem xét cho nghiên cứu này. Dữ liệu từ 740 đối tượng được sử dụng để phát triển mô hình và dữ liệu từ 491 đối tượng được sử dụng để thử nghiệm. Phương pháp này tận dụng thông tin định hướng lát cắt để tính toán đường giao giữa mặt phẳng cắt ngang và mặt phẳng trục dài. Một hình ảnh trục dài ước tính được trình bày cùng với một hình ảnh trục dài như một hình ảnh tham chiếu không có chuyển động, cho phép đánh giá trực quan chuyển động giữa các lần nín thở từ hình ảnh trục dài ước tính. Hình ảnh trục dài ước tính được gán nhãn là hình ảnh bị nhiễu chuyển động hoặc hình ảnh không có chuyển động. Các mô hình mạng nơ-ron tích chập sâu (CNN) được phát triển và xác thực bằng cách sử dụng dữ liệu đã được gán nhãn. Phương pháp hoàn toàn tự động trong việc lấy hình ảnh trục dài được định dạng lại từ một chồng 3D các lát cắt cắt ngang và dự đoán sự hiện diện\u002Fkhông hiện diện của chuyển động giữa các lần nín thở. Mô hình CNN sâu với EfficientNet-B0 làm bộ chiết xuất đặc trưng đạt hiệu quả cao trong việc phát hiện chuyển động với diện tích dưới đường cong đặc trưng nhận dạng người (AUC) đạt 0.87 cho dữ liệu thử nghiệm. Phương pháp đề xuất có thể tự động đánh giá chuyển động giữa các lần nín thở trong một chồng hình ảnh cắt ngang tim cine. Phương pháp này có thể giúp thu hồi lại những lát cắt cắt ngang gặp vấn đề hoặc sửa chữa chuyển động theo cách hồi cứu.","This study aimed to develop and validate a deep learning-based method that detects inter-breath-hold motion from an estimated cardiac long axis image reconstructed from a stack of short axis cardiac cine images. Cardiac cine magnetic resonance image data from all short axis slices and 2-\u002F3-\u002F4-chamber long axis slices were considered for the study. Data from 740 subjects were used for model development, and data from 491 subjects were used for testing. The method utilized the slice orientation information to calculate the intersection line of a short axis plane and a long axis plane. An estimated long axis image is shown along with a long axis image as a motion-free reference image, which enables visual assessment of the inter-breath-hold motion from the estimated long axis image. The estimated long axis image was labeled as either a motion-corrupted or a motion-free image. Deep convolutional neural network (CNN) models were developed and validated using the labeled data. The method was fully automatic in obtaining long axis images reformatted from a 3D stack of short axis slices and predicting the presence\u002Fabsence of inter-breath-hold motion. The deep CNN model with EfficientNet-B0 as a feature extractor was effective at motion detection with an area under the receiver operating characteristic (AUC) curve of 0.87 for the testing data. The proposed method can automatically assess inter-breath-hold motion in a stack of cardiac cine short axis slices. The method can help prospectively reacquire problematic short axis slices or retrospectively correct motion.",{"VI":665,"EN":666},"Đánh giá các mạng nơ-ron tích chập cho việc phát hiện chuyển động giữa các lần nín thở từ một chồng hình ảnh cắt ngang tim","Evaluation of convolutional neural networks for the detection of inter-breath-hold motion from a stack of cardiac short axis slice images",{"VOID":668},"10.1186\u002Fs12880-023-01070-x",{"VI":670},"học sâu, mạng nơ-ron tích chập, chuyển động giữa các lần nín thở, hình ảnh cắt ngang tim, cộng hưởng từ tim cine","2025-01-23T06:45:21.665+00:00",[673],"VI","https:\u002F\u002Fbmcmedimaging.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12880-023-01070-x",[676,691],{"id":677,"sortIndex":111,"researcher":18,"roles":678,"affiliations":679,"properties":688},"626be56a-084b-4ea2-89fc-4fe762ec79bf",[392],[680],{"id":18,"sortIndex":19,"affiliation":681,"properties":18},{"id":682,"createTime":683,"updateTime":683,"relativeEntities":684,"slug":18,"properties":685,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a7893eca-1709-41e5-8a50-51d3c96d66c2","2024-02-10T14:18:55.589+00:00",[],{"title":686},{"VI":687},"Department of Computer Science and Engineering, Sogang University, Seoul, South Korea",{"title":689},{"VI":690},"Min Woo Kim",{"id":692,"sortIndex":19,"researcher":18,"roles":693,"affiliations":694,"properties":703},"40915aa8-de0b-422f-80f2-10951b05f79f",[392],[695],{"id":18,"sortIndex":19,"affiliation":696,"properties":18},{"id":697,"createTime":698,"updateTime":698,"relativeEntities":699,"slug":18,"properties":700,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"f3194635-6a4e-4b4d-9ad8-7dbbfb4e0b74","2024-01-12T01:46:28.856+00:00",[],{"title":701},{"VI":702},"Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea",{"title":704},{"VI":705},"Yoon-Chul Kim",{"url":674,"publisher":707,"properties":734},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":708,"slug":10,"properties":709,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":712,"manageAffiliations":713,"indexDatabases":714,"url":91,"thumbnailPath":18,"statistic":729,"gsStatistic":18,"type":165,"analyzePriority":18},[],{"issn":710,"title":711},{"VOID":13},{"EN":15},[],[],[715,722],{"id":54,"indexDatabase":716,"url":67,"indexYears":68,"academicFieldIds":721,"indexDatabaseRanking":71},{"id":56,"createTime":57,"updateTime":58,"relativeEntities":717,"label":718,"description":719,"key":64,"publicationTags":720,"standard":18},[],{"EN":61,"VI":61},{"EN":61,"VI":63},[66],[70],{"id":73,"indexDatabase":723,"url":88,"indexYears":18,"academicFieldIds":728,"indexDatabaseRanking":18},{"id":75,"createTime":76,"updateTime":77,"relativeEntities":724,"label":725,"description":726,"key":84,"publicationTags":727,"standard":18},[],{"EN":80,"VI":80},{"VI":82,"EN":83},[86,87],[90],{"impactFactor":19,"impactFactorByYear":730,"i10Index":106,"i10IndexLast5Year":107,"totalPublication":108,"totalPublicationByYear":731,"totalCitation":128,"totalCitationByYear":732,"totalCitationPerPublication":146,"totalCitationPerPublicationByYear":733,"hindexLast5Year":132,"hindex":132},{"2012":94,"2013":95,"2014":96,"2015":97,"2016":98,"2017":99,"2018":100,"2019":101,"2020":102,"2021":103,"2022":104,"2023":105},{"2002":110,"2003":111,"2004":111,"2005":112,"2006":50,"2007":112,"2008":113,"2009":40,"2010":114,"2011":115,"2012":107,"2013":116,"2014":117,"2015":118,"2016":119,"2017":120,"2018":121,"2019":122,"2020":123,"2021":124,"2022":125,"2023":126,"2024":127},{"2002":130,"2005":131,"2006":132,"2007":113,"2008":133,"2009":134,"2010":135,"2011":136,"2012":137,"2013":138,"2014":139,"2015":140,"2016":141,"2017":135,"2018":106,"2019":142,"2020":143,"2021":144,"2022":145},{"2002":148,"2005":149,"2006":150,"2007":151,"2008":152,"2009":153,"2010":154,"2011":155,"2012":156,"2013":157,"2014":158,"2015":100,"2016":159,"2017":160,"2018":161,"2019":162,"2020":158,"2021":163,"2022":164},{"volume":735,"pages":737},{"VOID":736},"23",{"VOID":738},"1-10","2023-08-24",2023,{"id":742,"createTime":743,"updateTime":744,"relativeEntities":745,"slug":746,"properties":747,"entityType":191,"verifyStatus":192,"verifyTime":744,"verifyNote":193,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":758,"fullTextUrl":18,"authors":759,"publicationType":278,"publisherRelationship":866,"citationCount":18,"citationInfo":18,"publishDate":894,"publishYear":895,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":371},"7429f8f4-59f5-4d7c-a3db-955ed5766b89","2024-04-06T16:54:34.929+00:00","2025-01-28T23:54:54.544+00:00",[],"Preoperative-prediction-of-microsatellite-instability-status-in-colorectal-cancer-based-on-a-multiphasic-enhanced-CT-radiomics-nomogram-model",{"references":748,"keywords":750,"abstract":752,"title":754,"doi":756},{"VOID":749},"Sung 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Phenotype of microsatellite unstable colorectal carcinomas: well-differentiated and focally mucinous tumors and the absence of dirty necrosis correlate with microsatellite instability. Am J Surg Pathol. 2003;27:563–70.",{"EN":751},"",{"EN":753},"To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.",{"EN":755},"Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram 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               \u003Cjats:title>Background\u003C\u002Fjats:title>\n                \u003Cjats:p>The acceptance of coronary CT angiogram (CCTA) scans in the management of stable angina has led to an exponential increase in studies performed and reported incidental findings, including pulmonary nodules (PN). Using low-dose CT scans, volumetry tools are used in growth assessment and risk stratification of PN between 5 and 8 mm in diameter. Volumetry of PN could also benefit from the increased temporal resolution of CCTA scans, potentially expediting clinical decisions when an incidental PN is first detected on a CCTA scan, and allow for better resource management and planning in a Radiology department. This study aims to investigate how cardiopulmonary hemodynamic factors impact the volumetry of PN using CCTA scans. These factors include the cardiac phase, vascular distance from the main pulmonary artery (MPA) to the nodule, difference of the MPA diameter between systole and diastole, nodule location, and cardiomegaly presence.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Materials and methods\u003C\u002Fjats:title>\n                \u003Cjats:p>Two readers reviewed all CCTA scans performed from 2016 to 2019 in a tertiary hospital and detected PN measuring between 5 and 8 mm in diameter. Each observer measured each nodule using two different software packages and in systole and diastole. A multiple linear regression model was applied, and inter-observer and inter-software agreement were assessed using intraclass correlation.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>A total of 195 nodules from 107 patients were included in this retrospective, cross-sectional and observational study. The regression model identified the vascular distance (p < 0.001), the difference of the MPA diameter between systole and diastole (p < 0.001), and the location within the lower or posterior thirds of the field of view (p < 0.001 each) as affecting the volume measurement. The cardiac phase was not significant in the model. There was a very high inter-observer agreement but no reasonable inter-software agreement between measurements.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n                \u003Cjats:p>PN volumetry using CCTA scans seems to be sensitive to cardiopulmonary hemodynamic changes independently of the cardiac phase. These might also be relevant to non-gated scans, such as during PN follow-up. The cardiopulmonary hemodynamic changes are a new limiting factor to PN volumetry. In addition, when a patient experiences an acute or deteriorating cardiopulmonary disease during PN follow-up, these hemodynamic changes could affect the PN growth estimation.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>",{"EN":909},"The impact of cardiopulmonary hemodynamic factors in volumetry for pulmonary nodule 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Coronary computed tomography angiography improving outcomes in patients with chest pain. Curr Cardiovasc Imaging Rep. 2019;12(5):1–10.",{"doi":1087},"10.1007\u002Fs12410-019-9492-6",{"id":18,"text":1089,"url":18,"identifiers":1090},"Moss AJ, Williams MC, Newby DE, Nicol ED. The Updated NICE Guidelines: cardiac CT as the first-line test for coronary artery disease. Curr Cardiovasc Imaging Rep. 2017;10(5):15.",{"doi":1091},"10.1007\u002Fs12410-017-9412-6",{"id":18,"text":1093,"url":18,"identifiers":1094},"Scholtz J-E, Lu MT, Hedgire S, Meyersohn NM, Oliveira GR, Prabhakar AM, et al. Incidental pulmonary nodules in emergent coronary CT angiography for suspected acute coronary syndrome: impact of revised 2017 Fleischner Society Guidelines. J Cardiovasc Comput Tomogr. 2018;12(1):28–33.",{"doi":1095},"10.1016\u002Fj.jcct.2017.11.005",{"id":18,"text":1097,"url":18,"identifiers":1098},"Ramanathan S, Ladumor SB, Francis W, Allam AA, Alkuwari M. Incidental non-cardiac findings in coronary computed tomography angiography: is it worth reporting? J Clin Imaging Sci. 2019;9(40):40.",{"doi":1099},"10.25259\u002FJCIS_41_2019",{"id":18,"text":1101,"url":18,"identifiers":1102},"Goehler A, McMahon PM, Lumish HS, Wu CC, Munshi V, Gilmore M, et al. Cost-effectiveness of follow-up of pulmonary nodules incidentally detected on cardiac computed tomographic angiography in patients with suspected coronary artery disease. Circulation. 2014;130(8):668–75.",{"doi":1103},"10.1161\u002FCIRCULATIONAHA.113.007306",{"id":18,"text":1105,"url":18,"identifiers":1106},"Goo JM. MTE 27.02 pulmonary nodule guidelines: how do we decide between the IELCAP, ACCP, NCCN, Fleischner Society, BTS, and lung-RADS? J Thorac Oncol. 2017;12(11):S1654–5.",{"doi":1107},"10.1016\u002Fj.jtho.2017.09.178",{"id":18,"text":1109,"url":18,"identifiers":1110},"Callister MEJ, Baldwin DR, Akram AR, Barnard S, Cane P, Draffan J, et al. 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Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat\u002Fwater contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.",{"EN":1188},"Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study",{"VOID":1190},"10.1186\u002Fs12880-023-01056-9","https:\u002F\u002Fbmcmedimaging.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12880-023-01056-9",[1193,1208,1223,1238,1255,1268,1284,1300,1313,1328,1340,1362,1375,1391,1409,1425,1437],{"id":1194,"sortIndex":112,"researcher":18,"roles":1195,"affiliations":1196,"properties":1205},"713a555f-8d71-49ec-b78f-1a00e6804617",[392],[1197],{"id":18,"sortIndex":19,"affiliation":1198,"properties":18},{"id":1199,"createTime":1200,"updateTime":1200,"relativeEntities":1201,"slug":18,"properties":1202,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"746dce4a-a20a-45b7-833e-025db31452d1","2024-01-14T06:55:30.668+00:00",[],{"title":1203},{"VI":1204},"Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück-Center for Molecular Medicine, Berlin, Germany",{"title":1206},{"VI":1207},"Tobias 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Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-2 infection diagnosis. J Med Virol. 2020;92(9):1518–24.\nDai WC, Zhang HW, Yu J, Xu HJ, Chen H, Luo SP, et al. CT imaging and differential diagnosis of COVID-19. Can Assoc Radiol J. 2020;71(2):195–200. https:\u002F\u002Fdoi.org\u002F10.1177\u002F0846537120913033.\nAi T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020;296(2):E32-40.\nShi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20(4):425–34. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS1473-3099(20)30086-4.\nXie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J. Chest CT for Typical 2019-nCoV Pneumonia: relationship to Negative RT-PCR testing. Radiology. 2020;200343.\nFang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, et al. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. 2020;296(2):E115–7.\nXiong Y, Sun D, Liu Y, Fan Y, Zhao L, Li X, et al. Clinical and high-resolution CT features of the COVID-19 infection: comparison of the initial and follow-up changes. Invest Radiol. 2020;55(6):332–9.\nRai P, Kumar BK, Deekshit VK, Karunasagar I, Karunasagar I. Detection technologies and recent developments in the diagnosis of COVID-19 infection. Appl Microbiol Biotechnol. 2021;105(2):441–55. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00253-020-11061-5.\nWang G-Q, Zhao L, Wang X, Jiao Y-M, Wang F-S. Diagnosis and treatment protocol for COVID-19 patients (tentative 8th edition): interpretation of updated key points. Infect Dis Immun. 2021;1(1):17–9.\nGeneral Office of National Health Committee. Office of State Administration of Traditional Chinese Medicine. Diagnosis and Treatment Protocol for COVID-19 Patients (Trial Version 8) (2020–08–19) [Internet]. Available from: https:\u002F\u002Fcovid19.alliancebrh.com\u002Fcovid19en\u002Fc100036\u002F202008\u002F12b9b42813a94755bbf442008fe86f63\u002Ffiles\u002Fb0ae9b6c1d9a47bf81d7dc1f5e7ddda5.pdf\nHansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology. 2008;246(3):697–722.\nGeneral Office of National Health Committee. Office of State Administration of Traditional Chinese Medicine. Notice on the issuance of a program for the diagnosis and treatment of novel coronavirus (2019-nCoV) infected pneumonia (trial sixth edition)(2020 [Internet]. 2020. Available from: http:\u002F\u002Fbgs.satcm.gov.cn\u002Fzhengcewenjian\u002F2020-03-04\u002F13594.html\nTo KK, Sridhar S, Chiu KH, Hung DL, Li X, Hung IF, Tam AR, Chung TW, Chan JF, Zhang AJ, Cheng VC, Yuen KY. Lessons learned 1 year after SARS-CoV-2 emergence leading to COVID-19 pandemic. Emerg Microbes Infect. 2021;10(1):507–35. https:\u002F\u002Fdoi.org\u002F10.1080\u002F22221751.2021.1898291.\nPontone G, Scafuri S, Mancini ME, et al. Role of computed tomography in COVID-19. J Cardiovasc Comput Tomogr. 2021;15(1):27–36.\nSharma A, Ahmad Farouk I, Lal SK. COVID-19: A review on the novel coronavirus disease evolution, transmission, detection, control and prevention. Viruses. 2021;13(2):202. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fv13020202.PMID:33572857;PMCID:PMC7911532.\nWang W, Tang J, Wei F. Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol. 2020;92:441–7.\nWu J, Liu J, Zhao X, Liu C, Wang W, Wang D, et al. Clinical characteristics of imported cases of COVID-19 in Jiangsu Province: a multicenter descriptive study. Clin Infect Dis. 2020;71:706.\nLi YC, Bai WZ, Hashikawa T. The neuroinvasive potential of SARS-CoV2 may play a role in the respiratory failure of COVID-19 patients. J Med Virol. 2020;92(6):552–5.\nLu Y, Li X, Geng D, Mei N, Wu PY, Huang CC, et al. Cerebral micro-structural changes in COVID-19 patients – An MRI-based 3-month follow-up study: a brief title: cerebral changes in COVID-19. EClinicalMedicine. 2020;25(2):100484.\nJohn C. Smulian Sonja A. Rasmussen MD MS. Liver injury in COVID-19: management and challenges. Ann Oncol. 2020;19–21.\nXu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome [published correction appears in Lancet Respir Med. 2020 Feb 25]. Lancet Respir Med. 2020;8(4):420–422. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS2213-2600(20)30076-X\nHuang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China Lancet. 2020;395(10223):497–506.\nZu ZY, Di Jiang M, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. 2020;296(2):E15-25.\nLi L, Huang T, Wang Y, Wang Z, Liang Y, Huang T, et al. COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(6):577–83.\nYang W, Cao Q, Qin L, Wang X, Cheng Z, Pan A, et al. Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-center study in Wenzhou city, Zhejiang, China. J Infect. 2020;80:388.\nChung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT Imaging features of 2019 novel coronavirus (2019-nCoV). Radiology. 2020;200230.\nPan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol. 2020;30(6):3306–9.\nBernheim A. Chest CT findings in coronavirus disease-19: relationship to duration of infection. Radiology. 2020;19:200463.\nShi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;3099(20):1–10.\nPan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes on chest CT During Recovery From 2019 Novel Coronavirus (COVID-19) Pneumonia. Radiology. 2020;200370.\nXu X, Yu C, Qu J, Zhang L, Jiang S, Huang D, et al. Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur J Nucl Med Mol Imaging. 2020;613:2–7.\nXu X, Yu C, Zhang L, Luo L, Liu J. Imaging features of 2019 novel coronavirus pneumonia. Eur J Nucl Med Mol Imaging. 2020;613:1–2.\nZhang B, Wang X, Tian X, Zhao X, Liu B, Wu X, et al. Differences and prediction of imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia: a multicenter study. Medicine (Baltimore). 2020;99(42):e22747.\nAdams HJA, Kwee TC, Yakar D, Hope MD, Kwee RM. Chest CT Imaging signature of coronavirus disease 2019 infection: in pursuit of the scientific evidence. Chest. 2020;158(5):1885–95. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.chest.2020.06.025\nGuan CS, Wei LG, Xie RM, Lv ZB, Yan S, Zhang ZX, et al. CT findings of COVID-19 in follow-up: Comparison between progression and recovery. Diagnostic Interv Radiol. 2020;26(4):301–7.\nKarimian M, Azami M. Chest computed tomography scan findings of coronavirus disease 2019 (Covid-19) patients: a comprehensive systematic review and meta-analysis. Polish J Radiol. 2021;86(1):e31-49.\nBao C, Liu X, Zhang H, Li Y, Liu J. Coronavirus disease 2019 (COVID-19) CT findings: a systematic review and meta-analysis. J Am Coll Radiol. 2020;17(6):701–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jacr.2020.03.006.\nGuarnera A, Podda P, Santini E, Paolantonio P, Laghi A. Differential diagnoses of COVID-19 pneumonia: the current challenge for the radiologist—a pictorial essay. Insights Imaging. 2021. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13244-021-00967-x.\nHani C, Trieu NH, Saab I, Dangeard S, Bennani S, Chassagnon G, et al. COVID-19 pneumonia: a review of typical CT findings and differential diagnosis. Diagn Interv Imaging. 2020;101(5):263–8. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.diii.2020.03.014.\nBordi L, Nicastri E, Scorzolini L, Di Caro A, Capobianchi MR, Castilletti C, et al. Differential diagnosis of illness in patients under investigation for the novel coronavirus (SARS-CoV-2), Italy, February 2020. Eurosurveillance. 2020;25(8):2–5. https:\u002F\u002Fdoi.org\u002F10.2807\u002F1560-7917.ES.2020.25.8.2000170.\nBai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, et al. Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology. 2020;296(2):E46-54.\nNakanishi H, Suzuki M, Maeda H, Nakamura Y, Ikegami Y, Takenaka Y, et al. Differential diagnosis of COVID-19: importance of measuring blood lymphocytes, serum electrolytes, and olfactory and taste functions. Tohoku J Exp Med. 2020;252(2):109–19.\nChen X, Tang Y, Mo Y, Li S, Lin D, Yang Z, et al. A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study. Eur Radiol. 2020;30(9):4893–902.",{"EN":1492},"To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases. The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally. 239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation. In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.",{"EN":1494},"Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases",{"VOID":1496},"10.1186\u002Fs12880-022-00780-y","https:\u002F\u002Fbmcmedimaging.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12880-022-00780-y",[1499,1515,1527,1539,1551,1563,1578,1590,1602,1617,1629],{"id":1500,"sortIndex":19,"researcher":18,"roles":1501,"affiliations":1502,"properties":1512},"1095ecca-115a-4ead-bf7f-c7a3a1ebe219",[392],[1503],{"id":18,"sortIndex":19,"affiliation":1504,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1507,"slug":1508,"properties":1509,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"63cd27b5-1051-4704-85d8-cf1f89897f39","2023-11-29T17:51:24.357+00:00",[],"Department-of-Radiology-Huashan-Hospital-Fudan-University-Shanghai-China",{"title":1510},{"VI":1511},"Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China",{"title":1513},{"VI":1514},"Xuanxuan Li",{"id":1516,"sortIndex":112,"researcher":18,"roles":1517,"affiliations":1518,"properties":1524},"3a486f71-adc5-4e34-aaeb-d857a2f47672",[392],[1519],{"id":18,"sortIndex":19,"affiliation":1520,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1521,"slug":1508,"properties":1522,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1523},{"VI":1511},{"title":1525},{"VI":1526},"Nan Mei",{"id":1528,"sortIndex":111,"researcher":18,"roles":1529,"affiliations":1530,"properties":1536},"d9f9e477-600b-4b7f-8f70-614d8fb726a0",[392],[1531],{"id":18,"sortIndex":19,"affiliation":1532,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1533,"slug":1508,"properties":1534,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1535},{"VI":1511},{"title":1537},{"VI":1538},"Yajing Zhao",{"id":1540,"sortIndex":465,"researcher":18,"roles":1541,"affiliations":1542,"properties":1548},"aaefbdc1-9947-4a72-b544-e11b19bca3ac",[392],[1543],{"id":18,"sortIndex":19,"affiliation":1544,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1545,"slug":1508,"properties":1546,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1547},{"VI":1511},{"title":1549},{"VI":1550},"Zhuoying Ruan",{"id":1552,"sortIndex":40,"researcher":18,"roles":1553,"affiliations":1554,"properties":1560},"e784dd8f-f43c-4667-8d5e-6fd546b14aa6",[392],[1555],{"id":18,"sortIndex":19,"affiliation":1556,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1557,"slug":1508,"properties":1558,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1559},{"VI":1511},{"title":1561},{"VI":1562},"Bo Yin",{"id":1564,"sortIndex":1364,"researcher":18,"roles":1565,"affiliations":1566,"properties":1575},"49c205cc-398e-44b5-b5d9-69d6c9282582",[392],[1567],{"id":18,"sortIndex":19,"affiliation":1568,"properties":18},{"id":1569,"createTime":1570,"updateTime":1570,"relativeEntities":1571,"slug":18,"properties":1572,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"9cb5f6aa-a7ee-4929-9894-5a436e4cc91e","2024-01-09T11:24:43.744+00:00",[],{"title":1573},{"VI":1574},"Department of Radiology, Bozhou People’s Hospital, Bozhou, China",{"title":1576},{"VI":1577},"Xiaohui Qiu",{"id":1579,"sortIndex":307,"researcher":18,"roles":1580,"affiliations":1581,"properties":1587},"9dabb27a-1f4f-465a-8214-1114b1b9bdeb",[392],[1582],{"id":18,"sortIndex":19,"affiliation":1583,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1584,"slug":1508,"properties":1585,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1586},{"VI":1511},{"title":1588},{"VI":1589},"Qiuyue Han",{"id":1591,"sortIndex":155,"researcher":18,"roles":1592,"affiliations":1593,"properties":1599},"4cce2e0a-f690-4e6d-a02d-f417377e0872",[392],[1594],{"id":18,"sortIndex":19,"affiliation":1595,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1596,"slug":1508,"properties":1597,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1598},{"VI":1511},{"title":1600},{"VI":1601},"Yingyan Zheng",{"id":1603,"sortIndex":140,"researcher":18,"roles":1604,"affiliations":1605,"properties":1614},"70883a20-db58-4d6f-a81f-21e950a5c504",[392],[1606],{"id":18,"sortIndex":19,"affiliation":1607,"properties":18},{"id":1608,"createTime":1609,"updateTime":1609,"relativeEntities":1610,"slug":18,"properties":1611,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"6acab065-5058-4f14-9d4b-f1690ce84086","2024-01-09T11:24:43.732+00:00",[],{"title":1612},{"VI":1613},"Department of Radiology, Fu Yang No. 2 People’s Hospital, Fuyang, China",{"title":1615},{"VI":1616},"Anling Xiao",{"id":1618,"sortIndex":110,"researcher":18,"roles":1619,"affiliations":1620,"properties":1626},"afd9f9e9-198c-4e68-b5f3-8f7899a2a449",[392],[1621],{"id":18,"sortIndex":19,"affiliation":1622,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1623,"slug":1508,"properties":1624,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1625},{"VI":1511},{"title":1627},{"VI":1628},"Yiping Lu",{"id":1630,"sortIndex":50,"researcher":18,"roles":1631,"affiliations":1632,"properties":1638},"7cb69bbd-bd77-4881-ae49-722652f34e5f",[392],[1633],{"id":18,"sortIndex":19,"affiliation":1634,"properties":18},{"id":1505,"createTime":1506,"updateTime":1506,"relativeEntities":1635,"slug":1508,"properties":1636,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1637},{"VI":1511},{"title":1639},{"VI":1640},"Dongdong Wang",{"url":1497,"publisher":1642,"properties":1669},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1643,"slug":10,"properties":1644,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1647,"manageAffiliations":1648,"indexDatabases":1649,"url":91,"thumbnailPath":18,"statistic":1664,"gsStatistic":18,"type":165,"analyzePriority":18},[],{"issn":1645,"title":1646},{"VOID":13},{"EN":15},[],[],[1650,1657],{"id":54,"indexDatabase":1651,"url":67,"indexYears":68,"academicFieldIds":1656,"indexDatabaseRanking":71},{"id":56,"createTime":57,"updateTime":58,"relativeEntities":1652,"label":1653,"description":1654,"key":64,"publicationTags":1655,"standard":18},[],{"EN":61,"VI":61},{"EN":61,"VI":63},[66],[70],{"id":73,"indexDatabase":1658,"url":88,"indexYears":18,"academicFieldIds":1663,"indexDatabaseRanking":18},{"id":75,"createTime":76,"updateTime":77,"relativeEntities":1659,"label":1660,"description":1661,"key":84,"publicationTags":1662,"standard":18},[],{"EN":80,"VI":80},{"VI":82,"EN":83},[86,87],[90],{"impactFactor":19,"impactFactorByYear":1665,"i10Index":106,"i10IndexLast5Year":107,"totalPublication":108,"totalPublicationByYear":1666,"totalCitation":128,"totalCitationByYear":1667,"totalCitationPerPublication":146,"totalCitationPerPublicationByYear":1668,"hindexLast5Year":132,"hindex":132},{"2012":94,"2013":95,"2014":96,"2015":97,"2016":98,"2017":99,"2018":100,"2019":101,"2020":102,"2021":103,"2022":104,"2023":105},{"2002":110,"2003":111,"2004":111,"2005":112,"2006":50,"2007":112,"2008":113,"2009":40,"2010":114,"2011":115,"2012":107,"2013":116,"2014":117,"2015":118,"2016":119,"2017":120,"2018":121,"2019":122,"2020":123,"2021":124,"2022":125,"2023":126,"2024":127},{"2002":130,"2005":131,"2006":132,"2007":113,"2008":133,"2009":134,"2010":135,"2011":136,"2012":137,"2013":138,"2014":139,"2015":140,"2016":141,"2017":135,"2018":106,"2019":142,"2020":143,"2021":144,"2022":145},{"2002":148,"2005":149,"2006":150,"2007":151,"2008":152,"2009":153,"2010":154,"2011":155,"2012":156,"2013":157,"2014":158,"2015":100,"2016":159,"2017":160,"2018":161,"2019":162,"2020":158,"2021":163,"2022":164},{"volume":1670,"pages":1672},{"VOID":1671},"22",{"VOID":1673},"1-14","2022-03-26",{"id":1676,"createTime":1677,"updateTime":1678,"relativeEntities":1679,"slug":1680,"properties":1681,"entityType":191,"verifyStatus":192,"verifyTime":1678,"verifyNote":193,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1690,"fullTextUrl":18,"authors":1691,"publicationType":278,"publisherRelationship":1784,"citationCount":18,"citationInfo":18,"publishDate":1817,"publishYear":1818,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":371},"0f274733-2702-447b-902f-05d98ed8635a","2024-01-28T03:45:31.101+00:00","2024-12-26T23:49:57.331+00:00",[],"Correlation-between-the-area-of-high-signal-intensity-on-SPIO-enhanced-MR-imaging-and-the-pathologic-size-of-sentinel-node-metastases-in-breast-cancer-patients-with-positive-sentinel-nodes",{"references":1682,"abstract":1684,"title":1686,"doi":1688},{"VOID":1683},"Giuliano AE, Kirgan DM, Guenther JM, Morton DL: Lymphatic mapping and sentinel lymphadenectomy for breast cancer. Ann Surg. 1994, 220: 391-401.\nGiuliano AE, Jones RC, Brennan M, Statman R: Sentinel lymphadenectomy in breast cancer. J Clin Oncol. 1997, 15: 2345-2350.\nMotomura K, Inaji H, Komoike Y, Kasugai T, Nagumo S, Noguchi S, Koyama H: Sentinel node biopsy in breast cancer patients with clinically negative lymph-nodes. Breast Cancer. 1999, 6: 259-262.\nKrag D, Weaver D, Ashikaga T, Moffat F, Klimberg VS, Shriver C, Feldman S, Kusminsky R, Gadd M, Kuhn J, Harlow S, Beitsch P: The sentinel node in breast cancer: a multicenter validation study. N Engl J Med. 1998, 339: 941-946.\nVeronesi U, Paganelli G, Galimberti V, Viale G, Zurrida S, Bedoni M, Costa A, de Cicco C, Geraghty JG, Luini A, Sacchini V, Veronesi P: Sentinel-node biopsy to avoid axillary dissection in breast cancer with clinically negative lymph-nodes. Lancet. 1997, 349: 1864-1867.\nWilke LG, McCall LM, Posther KE, Whitworth PW, Reintgen DS, Leitch AM, Gabram SG, Lucci A, Cox CE, Hunt KK, Herndon JE, Giuliano AE: Surgical complications associated with sentinel lymph node biopsy: results from a prospective international cooperative group trial. Ann Surg Oncol. 2006, 13: 491-500.\nLucci A, McCall LM, Beitsch PD, Whitworth PW, Reintgen DS, Blumencranz PW, Leitch AM, Saha S, Hunt KK, Giuliano AE: American College of Surgeons Oncology Group: Surgical complications associated with sentinel lymph node dissection (SLND) plus axillary lymph node dissection compared with SLND alone in the American College of Surgeons Oncology Group Trial Z0011. J Clin Oncol. 2007, 25: 3657-3663.\nMcLaughlin SA, Wright MJ, Morris KT, Sampson MR, Brockway JP, Hurley KE, Riedel ER, Van Zee KJ: Prevalence of lymphedema in women with breast cancer 5 years after sentinel lymph node biopsy or axillary dissection: objective measurements. J Clin Oncol. 2008, 26: 5213-5219.\nHarisinghani MG, Barentsz J, Hahn PF, Deserno WM, Tabatabaei S, van de Kaa CH, de la Rosette J, Weissleder R: Noninvasive detection of clinically occult lymph-node metastases in prostate cancer. N Engl J Med. 2003, 348: 2491-2499.\nRockall AG, Sohaib SA, Harisinghani MG, Babar SA, Singh N, Jeyarajah AR, Oram DH, Jacobs IJ, Shepherd JH, Reznek RH: Diagnostic performance of nanoparticle-enhanced magnetic resonance imaging in the diagnosis of lymph node metastases in patients with endometrial and cervical cancer. J Clin Oncol. 2005, 23: 2813-2821.\nStets C, Brandt S, Wallis F, Buchmann J, Gilbert FJ, Heywang-Köbrunner SH: Axillary lymph node metastases: a statistical analysis of various parameters in MRI with USPIO. J Magn Reson Imaging. 2002, 16: 60-68.\nWill O, Purkayastha S, Chan C, Athanasiou T, Darzi AW, Gedroyc W, Tekkis PP: Diagnostic precision of nanoparticle-enhanced MRI for lymph-node metastases: a meta-analysis. Lancet Oncol. 2006, 7: 52-60.\nMotomura K, Ishitobi M, Komoike Y, Koyama H, Noguchi A, Sumino H, Kumatani Y, Inaji H, Horinouchi T, Nakanishi K: SPIO-enhanced magnetic resonance imaging for the detection of metastases in sentinel nodes localized by computed tomography lymphography in patients with breast cancer. Ann Surg Oncol. 2011, 18: 3422-3429.\nOhno Y, Hatabu H, Takenaka D, Higashino T, Watanabe H, Ohbayashi C, Sugimura K: CT-guided transthoracic needle aspiration biopsy of small (\u003C or = 20 mm) solitary pulmonary nodules. AJR Am J Roentgenol. 2003, 180: 1665-1669.\nHudgins PA, Anzai Y, Morris MR, Lucas MA: Ferumoxtran-10, a superparamagnetic iron oxide as a magnetic resonance enhancement agent for imaging lymph nodes: a phase 2 dose study. AJNR Am J Neuroradiol. 2002, 23: 649-656.\nAnzai Y, Piccoli CW, Outwater EK, Stanford W, Bluemke DA, Nurenberg P, Saini S, Maravilla KR, Feldman DE, Schmiedl UP, Brunberg JA, Francis IR, Harms SE, Som PM, Tempany CM, Group: Evaluation of neck and body metastases to nodes with ferumoxtran 10-enhanced MR imaging: phase III safety and efficacy study. Radiology. 2003, 228: 777-788.\nMotomura K, Inaji H, Komoike Y, Hasegawa Y, Kasugai T, Noguchi S, Koyama H: Combination technique is superior to dye alone in identification of the sentinel node in breast cancer patients. J Surg Oncol. 2001, 76: 95-99.\nMotomura K, Komoike Y, Hasegawa Y, Kasugai T, Inaji H, Noguchi S, Koyama H: Intradermal radioisotope injection is superior to subdermal injection for the identification of the sentinel node in breast cancer patients. J Surg Oncol. 2003, 82: 91-96.\nMotomura K, Nagumo S, Komoike Y, Koyama H, Inaji H: Accuracy of imprint cytology for intraoperative diagnosis of sentinel node metastases in breast cancer. Ann Surg. 2008, 247: 839-842.\nAmerican Joint Committee on Cancer, et al: Breast. AJCC Cancer Staging Handbook. Edited by: Greene FL, Page DL, Fleming ID, Fritz AG, Balch CM, Haller DG. 2002, New York: Springer, 155-181. 6\nLahaye MJ, Engelen SM, Kessels AG, de Bruïne AP, von Meyenfeldt MF, van Engelshoven JM, van de Velde CJ, Beets GL, Beets-Tan RG: USPIO-enhanced MR imaging for nodal staging in patients with primary rectal cancer: predictive criteria. Radiology. 2008, 246: 804-811.\nde Boer M, van Deurzen CH, van Dijck JA, Borm GF, van Diest PJ, Adang EM, Nortier JW, Rutgers EJ, Seynaeve C, Menke-Pluymers MB, Bult P, Tjan-Heijnen VC: Micrometastases or isolated tumor cells and the outcome of breast cancer. N Engl J Med. 2009, 361: 653-663.\nde Boer M, van Dijck JA, Bult P, Borm GF, Tjan-Heijnen VC: Breast cancer prognosis lymph node metastases, isolated tumor cells, and micrometastases. J Natl Cancer Inst. 2010, 102: 410-425.\nHansen NM, Grube B, Ye X, Turner RR, Brenner RJ, Sim MS, Giuliano AE: Impact of in the sentinel node of patients with invasive breast cancer. J Clin Oncol. 2009, 27: 4679-4684.\nThe pre-publication history for this paper can be accessed here:http:\u002F\u002Fwww.biomedcentral.com\u002F1471-2342\u002F13\u002F32\u002Fprepub",{"EN":1685},"We previously demonstrated that superparamagnetic iron oxide (SPIO)-enhanced MR imaging is promising for the detection of metastases in sentinel nodes localized by CT-lymphography in patients with breast cancer. The purpose of this study was to determine the predictive criteria of the size of nodal metastases with SPIO-enhanced MR imaging in breast cancer, with histopathologic findings as reference standard. This study included 150 patients with breast cancer. The patterns of SPIO uptake for positive sentinel nodes were classified into three; uniform high-signal intensity, partial high-signal intensity involving ≥50% of the node, and partial high-signal intensity involving \u003C50% of the node. Imaging results were correlated with histopathologic findings. Thirty-three pathologically positive sentinel nodes from 30 patients were evaluated. High-signal intensity patterns that were uniform or involved ≥50% of the node were observed in 23 nodes that contained macro-metastases and no node that contained micro-metastases, while high-signal intensity patterns involving \u003C50% of the node were observed in 2 nodes that contained macro-metastases and 8 nodes that contained micro-metastases. When the area of high-signal intensity was compared with the pathological size of the metastases, a pathologic >2 mm sentinel node metastases correlated with the area of high-signal intensity, however, a pathologic ≤2 mm sentinel node metastases did not. High-signal intensity patterns that are uniform or involve ≥50% of the node are features of nodes with macro-metastases. The area of high-signal intensity correlated with the pathological size of metastases for nodes with metastases >2 mm in this series.",{"EN":1687},"Correlation between the area of high-signal intensity on SPIO-enhanced MR imaging and the pathologic size of sentinel node metastases in breast cancer patients with positive sentinel nodes",{"VOID":1689},"10.1186\u002F1471-2342-13-32","https:\u002F\u002Fbmcmedimaging.biomedcentral.com\u002Farticles\u002F10.1186\u002F1471-2342-13-32",[1692,1709,1724,1736,1748,1760,1772],{"id":1693,"sortIndex":19,"researcher":18,"roles":1694,"affiliations":1695,"properties":1706},"c9ea617e-354f-4746-8224-d0e21aaa91b5",[392],[1696],{"id":18,"sortIndex":19,"affiliation":1697,"properties":18},{"id":1698,"createTime":1699,"updateTime":1700,"relativeEntities":1701,"slug":1702,"properties":1703,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"190479d7-b36a-4cb9-849d-32a7e731382d","2024-04-17T13:10:53.033+00:00","2024-11-26T08:31:54.200+00:00",[],"Department-of-Surgery-Osaka-Medical-Center-for-Cancer-and-Cardiovascular-Diseases-Osaka-Japan",{"title":1704},{"EN":1705},"Department of Surgery Osaka Medical Center for Cancer and Cardiovascular Diseases Osaka Japan",{"title":1707},{"VI":1708},"Kazuyoshi Motomura",{"id":1710,"sortIndex":465,"researcher":18,"roles":1711,"affiliations":1712,"properties":1721},"fd5b9f9e-ddac-4682-a824-dd16a4f72313",[392],[1713],{"id":18,"sortIndex":19,"affiliation":1714,"properties":18},{"id":1715,"createTime":1716,"updateTime":1716,"relativeEntities":1717,"slug":18,"properties":1718,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"54e5c2c8-07d8-46e6-9c0d-6e6988cbf75f","2023-12-18T12:07:50.804+00:00",[],{"title":1719},{"VI":1720},"Department of Radiology, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka, Japan",{"title":1722},{"VI":1723},"Katsuyuki 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C, Horby PW, Hayden FG, et al. A novel coronavirus outbreak of global health concern. Lancet. 2020;395(10223):470–3. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0140-6736(20)30185-9.\nGulati A, Pomeranz C, Qamar ZPJ, et al. A comprehensive review of manifestations of novel coronaviruses in the context of deadly COVID-19 global pandemic. Am J Med Sci. 2020;360(1):5–34. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.amjms.2020.05.006.\nChoudhary J, Dheeman S, Sharma V, et al. Insights of severe acute respiratory syndrome coronavirus (SARS-CoV-2) pandemic: a current review. Biol Proced Online. 2021;23(1):5. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12575-020-00141-5.\nTorjesen I. Covid-19: Delta variant is now UK’s most dominant strain and spreading through schools. BMJ Clin Res. 2021;373:n1445. https:\u002F\u002Fdoi.org\u002F10.1136\u002Fbmj.n1445.\nEUROPEAN CENTRE FOR DISEASE PREVENTION AND CONTROL. Assessing SARS-CoV-2 circulation, variants of concern, non-pharmaceutical interventions and vaccine rollout in the EU\u002FEEA, 15th update-10 June 2021.ECDC: Stockholm; 2021. https:\u002F\u002Fwww.ecdc.europa.eu\u002Fen\u002Fpublications-data\u002Frapid-risk-assessment-sars-cov-2-circulation-variants-concern.\nKannan SR, Spratt AN, Cohen AR, et al. Evolutionary analysis of the delta and delta plus variants of the SARS-CoV-2 viruses. J Autoimmun. 2021;124:102715. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jaut.2021.102715.\nWang Y, Chen R, Hu F, et al. Transmission, viral kinetics and clinical characteristics of the emergent SARS-CoV-2 Delta VOC in Guangzhou China. EClinicalMedicine. 2021;40:101129. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eclinm.2021.101129.\nXia S, Zhang Y, Wang Y, et al. Safety and immunogenicity of an inactivated SARS-CoV-2 vaccine, BBIBP-CorV: a randomised, double-blind, placebo-controlled, phase 1\u002F2 trial. Lancet Infect Dis. 2021;21(1):39–51. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS1473-3099(20)30831-8.\nYang S, Li Y, Dai L, et al. Safety and immunogenicity of a recombinant tandem-repeat dimeric RBD-based protein subunit vaccine (ZF2001) against COVID-19 in adults: two randomised, double-blind, placebo-controlled, phase 1 and 2 trials. Lancet Infect Dis. 2021;21(8):1107–19. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS1473-3099(21)00127-4.\nHalperin SA, Ye L, MacKinnon-Cameron D, et al. Final efficacy analysis, interim safety analysis, and immunogenicity of a single dose of recombinant novel coronavirus vaccine (adenovirus type 5 vector) in adults 18 years and older: an international, multicentre, randomised, double-blinded, placebo-controlled phase 3 trial. Lancet. 2022;399(10321):237–48. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0140-6736(21)02753-7.\nRubin GD, Ryerson CJ, Haramati LB, et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner society. Chest. 2020;158(1):106–16. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.chest.2020.04.003.\nLambin P, Leijenaar R, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnrclinonc.2017.141.\nLubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483–503. https:\u002F\u002Fdoi.org\u002F10.1148\u002Frg.2017170056.\nLi Z, Zhong Z, Li Y, et al. From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans. Eur Radiol. 2020;30(12):6828–37. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00330-020-07042-x.\nLiu H, Ren H, Wu Z, et al. CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS. J Transl Med. 2021;19(1):29. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12967-020-02692-3.\nNational Health Commission of the People's Republic of China. Diagnosis and Treatment Protocols of Coronavirus Disease 2019 (trial 8 edition revision). 2021. Available via http:\u002F\u002Fwww.nhc.gov.cn\u002Fxcs\u002Fzhengcwj\u002F202104\u002F7de0b3837c8b4606a0594aeb0105232b.shtml. Accessed 20 May 2022.\nChung M, Bernheim A, Mei X, et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology. 2020;295(1):202–7. https:\u002F\u002Fdoi.org\u002F10.1148\u002Fradiol.2020200230.\nCheng Z, Qin L, Cao Q, et al. Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia. Radiol Infect Dis. 2020;7(2):55–61. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jrid.2020.04.004.\nvan Griethuysen J, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Can Res. 2017;77(21):e104–7. https:\u002F\u002Fdoi.org\u002F10.1158\u002F0008-5472.CAN-17-0339.\nShu J, Tang Y, Cui J, et al. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. Eur J Radiol. 2018;109:8–12. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ejrad.2018.10.005.\nLeoni MLG, Lombardelli L, Colombi D, et al. Prediction of 28-day mortality in critically ill patients with COVID-19: development and internal validation of a clinical prediction model. PLoS ONE. 2021;16(7):e0254550. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0254550.\nLi T, Qiu Z, Zhang L, et al. Significant changes of peripheral T lymphocyte subsets in patients with severe acute respiratory syndrome. J Infect Dis. 2004;189(4):648–51. https:\u002F\u002Fdoi.org\u002F10.1086\u002F381535.\nZheng HY, Zhang M, Yang CX, et al. Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patients. Cell Mol Immunol. 2020;17(5):541–3. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41423-020-0401-3.\nWang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan China. JAMA. 2020;323(11):1061–9. https:\u002F\u002Fdoi.org\u002F10.1001\u002Fjama.2020.1585.\nZeng G, Wu Q, Pan H, et al. Immunogenicity and safety of a third dose of CoronaVac, and immune persistence of a two-dose schedule, in healthy adults: interim results from two single-centre, double-blind, randomised, placebo-controlled phase 2 clinical trials. Lancet Infect Dis. 2022;22(4):483–95. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS1473-3099(21)00681-2.\nJara A, Undurraga EA, Zubizarreta JR, et al. Effectiveness of homologous and heterologous booster doses for an inactivated SARS-CoV-2 vaccine: a large-scale prospective cohort study. Lancet Glob Health. 2022;10(6):e798–806. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS2214-109X(22)00112-7.\nPhan LT, Nguyen TV, Luong QC, et al. Importation and human-to-human transmission of a novel coronavirus in vietnam. N Engl J Med. 2020;382(9):872–4. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMc2001272.\nDepeursinge A, Foncubierta-Rodriguez A, Van De Ville D, et al. Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal. 2014;18(1):176–96. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.media.2013.10.005.\nJara A, Undurraga EA, González C, et al. Effectiveness of an Inactivated SARS-CoV-2 Vaccine in Chile. N Engl J Med. 2021;385(10):875–84. https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMoa2107715.\nVassallo M, Clement N, Lotte L, et al. Prevalence and main clinical characteristics of fully vaccinated patients admitted to hospital for delta variant COVID-19. Front Med. 2022;9:809154. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmed.2022.809154.\nRavindra Naik B, Anil Kumar S, Rachegowda N, et al. Severity of COVID-19 infection using chest computed tomography severity score index among vaccinated and unvaccinated COVID-19-positive healthcare workers: an analytical cross-sectional study. Cureus. 2022;14(2):e22087. https:\u002F\u002Fdoi.org\u002F10.7759\u002Fcureus.22087.\nSheikh A, McMenamin J, Taylor B, et al. SARS-CoV-2 Delta VOC in Scotland: demographics, risk of hospital admission, and vaccine effectiveness. Lancet. 2021;397(10293):2461–2. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0140-6736(21)01358-1.\nLi XN, Huang Y, Wang W, et al. Effectiveness of inactivated SARS-CoV-2 vaccines against the Delta variant infection in Guangzhou: a test-negative case-control real-world study. Emerg Microbes Infect. 2021;10(1):1751–9. https:\u002F\u002Fdoi.org\u002F10.1080\u002F22221751.2021.1969291.",{"EN":1827},"To explore the characteristics of peripheral blood, high resolution computed tomography (HRCT) imaging and the radiomics signature (RadScore) in patients infected with delta variant virus under different coronavirus disease (COVID-19) vaccination status.\n 123 patients with delta variant virus infection collected from November 1, 2021 to March 1, 2022 were analyzed retrospectively. According to COVID-19 vaccination Status, they were divided into three groups: Unvaccinated group, partially vaccinated group and full vaccination group. The peripheral blood, chest HRCT manifestations and RadScore of each group were analyzed and compared.\n The mean lymphocyte count 1.22 ± 0.49 × 10^9\u002FL, CT score 7.29 ± 3.48, RadScore 0.75 ± 0.63 in the unvaccinated group; The mean lymphocyte count 1.55 ± 0.70 × 10^9\u002FL, CT score 5.27 ± 2.72, RadScore 1.03 ± 0.46 in the partially vaccinated group; The mean lymphocyte count 1.87 ± 0.70 × 10^9\u002FL, CT score 3.59 ± 3.14, RadScore 1.23 ± 0.29 in the fully vaccinated group. There were significant differences in lymphocyte count, CT score and RadScore among the three groups (all p \u003C 0.05); Compared with the other two groups, the lung lesions in the unvaccinated group were more involved in multiple lobes, of which 26 cases involved the whole lung. Through the analysis of clinical features, pulmonary imaging features and radiomics, we confirmed the positive effect of COVID-19 vaccine on pulmonary inflammatory symptoms and lymphocyte count (immune system) during delta mutant infection.\n",{"EN":1829},"Analysis of CT signs, radiomic features and clinical characteristics for delta variant COVID-19 patients with different vaccination 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