Complete fully automatic segmentation and 3-dimensional measurement of mediastinal lymph nodes for a new response evaluation criteria for solid tumors

Kamiyoshihara, 2001, Mediastinal lymph node evaluation by computed tomographic scan in lung cancer, J Cardiovasc Surg, 42, 119 Eisenhauer, 2009, New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1), Eur J Cancer, 45, 228, 10.1016/j.ejca.2008.10.026 Schwartz, 2009, Evaluation of lymph nodes with RECIST 1.1, Eur J Cancer, 45, 261, 10.1016/j.ejca.2008.10.028 Choe, 2018, Quality of lymph node assessment and survival among patients with non–small cell lung cancer, JAMA Oncol, 4, 87, 10.1001/jamaoncol.2017.3092 Carolus, 2020, Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks. Medical Imaging, Comput-Aided Diagn, 11314, 113141B Fréchet, 2018, Diagnostic accuracy of mediastinal lymph node staging techniques in the preoperative assessment of non-small cell lung cancer patients, J Bronchol Interv Pulmonol, 25, 17, 10.1097/LBR.0000000000000425 Tantraworasin, 2018, Underperformance of mediastinal lymph node evaluation in resectable non-small cell lung cancer, Ann Thorac Surg, 105, 943, 10.1016/j.athoracsur.2017.10.007 De Leyn, 2014, Revised ESTS guidelines for preoperative mediastinal lymph node staging for non-small-cell lung cancer, Eur J Cardiothorac Surg, 45, 787, 10.1093/ejcts/ezu028 Bouget, 2019, Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging, Int J comput Assist Radiol Surg, 14, 977, 10.1007/s11548-019-01948-8 Beichel, 2012, Computer-aided lymph node segmentation in volumetric CT data, Med Phys, 39, 5419, 10.1118/1.4742845 Honea, 1999, Three-dimensional active surface approach to lymph node segmentation, Medical Imaging 1999: Image Processing, 3661, 1003, 10.1117/12.348493 Yan, 2004, Lymph node segmentation from CT images using fast marching method, Comput Med Imaging Graph, 28, 33, 10.1016/j.compmedimag.2003.09.003 Yan, 2006, Marker-controlled watershed for lymphoma segmentation in sequential CT images, Med Phys, 33, 2452, 10.1118/1.2207133 Dornheim, 2007, Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models: Segmentation of neck lymph nodes, Acad Radiol, 14, 1389, 10.1016/j.acra.2007.09.001 Steger, 2011, Lymph node segmentation in CT slices using dynamic programming, Proc IEEE Int Symp Biomed Imaging, 1990 Yu, 2015, Region-based snake with edge constraint for segmentation of lymph nodes on CT images, Comput Biol Med, 60, 86, 10.1016/j.compbiomed.2015.02.011 Du, 2020, Risk factors of lymph node metastasis in the splenic hilum of gastric cancer patients: a meta-analysis, World J Surg Oncol, 18, 1, 10.1186/s12957-020-02008-1 Kano, 2020, Therapeutic value of splenectomy to dissect splenic hilar lymph nodes for type 4 gastric cancer involving the greater curvature, compared with other types, Gastric Cancer, 23, 927, 10.1007/s10120-020-01072-6 Zhong, 2021, Reappraise role of No. 10 lymphadenectomy for proximal gastric cancer in the era of minimal invasive surgery during total gastrectomy: a pooled analysis of 4 prospective trial, Gastric Cancer, 24, 245, 10.1007/s10120-020-01110-3 Bando, 2020, Diagnostic and therapeutic value of pelvic lymph node dissection in the fossa of Marcille in patients with clinically localized high-risk prostate cancer: Histopathological and molecular analyses, Prostate, 80, 345, 10.1002/pros.23949 Sebben, 2020, Diagnostic and therapeutic value of pelvic lymph node dissection in the fossa of Marcille in patients with clinically localized high-risk prostate cancer: histological and molecular analyses, Prostate, 80, 795, 10.1002/pros.23987 Matsumoto, 2020, 5-ALA-assistant automated detection of lymph node metastasis in gastric cancer patients, Gastric Cancer, 23, 725, 10.1007/s10120-020-01044-w Pak, 2018, A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/ CT, PLoS ONE, 13, 10.1371/journal.pone.0193403 Bayanati, 2015, Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?, Eur Radiol, 25, 480, 10.1007/s00330-014-3420-6 Andersen, 2016, CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer, Acta Radiol, 57, 669, 10.1177/0284185115598808 Pham, 2017, Texture Analysis and Synthesis of Malignant and Benign Mediastinal Lymph Nodes in Patients with Lung Cancer on Computed Tomography, Sci Rep, 7, 1, 10.1038/srep43209 Tekchandani, 2020, Mediastinal lymph node malignancy detection in computed tomography images using fully convolutional network, Biocybern Biomed Eng, 40, 187, 10.1016/j.bbe.2019.05.002 Gao, 2020, A radiomics-based model for prediction of lymph node metastasis in gastric cancer, Eur J Radiol, 129, 10.1016/j.ejrad.2020.109069 Wang L, Gong J, Huang X, Lin G, Zheng B, Chen J, et al. CT-based radiomics nomogram for preoperative prediction of No. 10 lymph nodes metastasis in advanced proximal gastric cancer. Eur J Surg Oncol 2020 in press. Mirniaharikandehei, 2020, Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images, Comput Methods Programs Biomed, 200 Yang L, Sun L, Liu J, Liu Q. (2019). Role of low dose 256-slice CT perfusion imaging in predicting mediastinal lymph node metastasis of lung cancer. Rev Assoc Med Bras, 65(6) (2019), pp.761-766. Pak, 2018, A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/CT, PLoS ONE, 13, 10.1371/journal.pone.0193403 Yin, 2021, Prediction of mediastinal lymph node metastasis based on 18 F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer, Eur Radiol, 10.1007/s00330-020-07466-5 Gao, 2015, The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from 18F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer, Eur J Radiol, 84, 312, 10.1016/j.ejrad.2014.11.006 Zhong, 2018, Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma, AJR Am J Roentgenol, 211, 109, 10.2214/AJR.17.19074 Tekchandani, 2020, Performance improvement of mediastinal lymph node severity detection using GAN and Inception network, Comput Methods Programs Biomed, 194, 10.1016/j.cmpb.2020.105478 Wang, 2017, Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images, EJNMMI Res, 7, 1, 10.1186/s13550-017-0260-9 Pham TD. Complementary features for radiomic analysis of malignant and benign mediastinal lymph nodes. In: 2017 IEEE Int Conf Image Process 2017, pp. 3849-3853. Tekchandani, 2018, Severity assessment of lymph nodes in CT images using deep learning paradigm, IEEE Int Conf Comput Methodol Commun, 686 Oda H, Roth HR, Bhatia KK, Oda M, Kitasaka T, Iwano S, et al. Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images. In Medical Imaging 2018: Comput-Aided Diagn, 10575 (2018), p. 1057502 Zhao, 2020, Deep learning–based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: a multicentre study, EBioMedicine, 56, 10.1016/j.ebiom.2020.102780 Gao, 2019, Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer, Chin Med J, 132, 2804, 10.1097/CM9.0000000000000532 Barbu, 2011, Automatic detection and segmentation of lymph nodes from CT data, IEEE Trans Med Imaging, 31, 240, 10.1109/TMI.2011.2168234 Shen, 2020, An introduction to deep learning in medical physics: advantages, potential, and challenges, Phys Med Biol, 65, p. 05TR01, 10.1088/1361-6560/ab6f51 Kuo, 2020, Automatic lung nodule detection system using image processing techniques in computed tomography, Biomed Signal Process Control, 56, 10.1016/j.bspc.2019.101659 Chen CW, Luo J, Parker KJ. 1998. Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications. IEEE Trans Image Process, 7(12) (1998), pp.1673-1683. Ahmed, 2002, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans Med Imag, 21, 193, 10.1109/42.996338 Li, 2020, Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation, IEEE Trans. Med. Imag., 41, 1 Xu, Z., Chen, J., Wu, J., 2008. Clustering algorithm for intuitionistic fuzzy sets. Inf Sci. 178(19) (2008), pp.3775-3790. Ko, 1991, Center weighted median filters and their applications to image enhancement, IEEE Trans Circuits Syst, 38, 984, 10.1109/31.83870 Varade, 2013, A survey on various median filtering techniques for removal of impulse noise from digital images, Int J Adv Res Comput. Eng T, 2, 606 Kuo, 2017, Prognostic value of tumor volume for patients with advanced lung cancer treated with chemotherapy, Comput Meth Prog Bio, 144, 165, 10.1016/j.cmpb.2017.03.021 Kuo, 2020, Application of intelligent automatic segmentation and 3D reconstruction of inferior turbinate and maxillary sinus from computed tomography and analyze the relationship between volume and nasal lesion, Biomed Signal Proce, 57 Yigit H. A weighting approach for KNN classifier. In: 2013 International conference on electronics, computer and computation (2013), pp. 228-231. Shrivakshan, 2012, A comparison of various edge detection techniques used in image processing, Int J Comput Sci, 9, 269 Larson, 2008, Analysis of variance, Circulation, 117, 115, 10.1161/CIRCULATIONAHA.107.654335 Glaze, 1985, Normal mediastinal lymph nodes: number and size according to American Thoracic Society mapping, Am J Roentgenol, 144, 261, 10.2214/ajr.144.2.261 Lee CC, Lee ST, Chang CN, Pai PC, Chen YL, Hsieh TC, et al. 2011. Volumetric measurement for comparison of the accuracy between intraoperative CT and postoperative MR imaging in pituitary adenoma surgery. Am J Neuroradiol, 32(8) (2011), pp.1539-1544. Dang, 2013, Validation study of a fast, accurate, and precise brain tumor volume measurement, Comput Methods Programs Biomed, 111, 480, 10.1016/j.cmpb.2013.04.011 Turkbey, 2012, Correlation of magnetic resonance imaging tumor volume with histopathology, J Urol, 188, 1157, 10.1016/j.juro.2012.06.011 Haj Mohammad, 2017, Volume-outcome relation in palliative systemic treatment of metastatic oesophagogastric cancer, Eur J Cancer, 78, 28, 10.1016/j.ejca.2017.03.008 Janssens, 2014, Computed tomography-based tumour volume as a predictor of outcome in laryngeal cancer: results of the phase 3 ARCON trial, Eur J Cancer, 50, 1112, 10.1016/j.ejca.2013.12.012 BuganimY, 2009, p53: balancing tumour suppression and implications for the clinic, Eur J Cancer, 45, 217, 10.1016/S0959-8049(09)70037-1 Hart, 2016, Receiver operating characteristic (ROC) curve analysis: A tutorial using body mass index (BMI) as a measure of obesity, J Phys Act Res, 1, 5 Hanley, 1982, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29, 10.1148/radiology.143.1.7063747 Youden, 1950, Index for rating diagnostic tests, Cancer, 3, 32, 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3