Artificial intelligence in gynecologic cancers: Current status and future challenges – A systematic review
Tài liệu tham khảo
Chilamkurthy, 2018, Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study, Lancet., 392, 2388, 10.1016/S0140-6736(18)31645-3
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature., 542, 115, 10.1038/nature21056
Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA., 316, 2402, 10.1001/jama.2016.17216
Liberati, 2009, The PRISMA statement for reporting systematic reviews and Meta-analyses of studies that evaluate health care interventions: explanation and elaboration, PLoS Med, 6, 10.1371/journal.pmed.1000100
Wolff, 2019, PROBAST: a tool to assess the risk of Bias and applicability of prediction model studies, Ann Intern Med, 170, 51, 10.7326/M18-1376
Urushibara, 2021, Diagnosing uterine cervical cancer on a single T2-weighted image: comparison between deep learning versus radiologists, Eur J Radiol, 135, 10.1016/j.ejrad.2020.109471
Wang, 2020, MRI texture features differentiate clinicopathological characteristics of cervical carcinoma, Eur Radiol, 30, 5384, 10.1007/s00330-020-06913-7
Soumya, 2016, Cervical cancer detection and classification using texture analysis, Biomed Pharmacol J, 9, 663, 10.13005/bpj/988
Chen, 2020, Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients, Comput Methods Programs Biomed, 197, 10.1016/j.cmpb.2020.105759
Tian, 2020, Prediction of response to preoperative neoadjuvant chemotherapy in locally advanced cervical cancer using multicenter CT-based radiomic analysis, Front Oncol, 10, 77, 10.3389/fonc.2020.00077
Yan, 2020, A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma, BJR., 93, 20200358, 10.1259/bjr.20200358
Dong, 2020, Development and validation of a deep learning radiomics model predicting lymph node status in operable cervical cancer, Front Oncol, 10, 464, 10.3389/fonc.2020.00464
Wu, 2020, Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer, JAMA Netw Open, 3, 10.1001/jamanetworkopen.2020.11625
Wang, 2020, Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram, Eur Radiol, 30, 3585, 10.1007/s00330-019-06655-1
Hua, 2020, Lymph-vascular space invasion prediction in cervical cancer: exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI, Biomed Signal Process Control, 58, 10.1016/j.bspc.2020.101869
Wang, 2019, Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging, Eur J Radiol, 114, 128, 10.1016/j.ejrad.2019.01.003
Wu, 2019, Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer, Radiother Oncol, 138, 141, 10.1016/j.radonc.2019.04.035
Takada, 2020, A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions, Jpn J Radiol, 38, 265, 10.1007/s11604-019-00917-0
Park, 2020, Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis, Radiat Oncol, 15, 86, 10.1186/s13014-020-01502-w
Meng, 2018, Texture analysis as imaging biomarker for recurrence in advanced cervical cancer treated with CCRT, Sci Rep, 8, 11399, 10.1038/s41598-018-29838-0
Torheim, 2014, Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines, IEEE Trans Med Imaging, 33, 1648, 10.1109/TMI.2014.2321024
Shen, 2019, Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [F-18]-fluorodeoxyglucose positron emission tomography/computed tomography, Eur Radiol, 29, 6741, 10.1007/s00330-019-06265-x
Roman-Jimenez, 2016, Random forests to predict tumor recurrence following cervical cancer therapy using pre- and per-treatment 18F-FDG PET parameters, Conf Proc IEEE Eng Med Biol Soc, 2016, 2444
Weegar, 2020, Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations, PLoS ONE, 15, 10.1371/journal.pone.0237911
F, 2020, Supervised algorithms of machine learning for the prediction of cervical cancer, J Biomed Phys Eng, 10, 513
Ijaz, 2020, Data-driven cervical cancer prediction model with outlier detection and over-sampling methods, Sensors., 20, 2809, 10.3390/s20102809
Alsmariy, 2020, Predicting cervical cancer using machine learning methods, IJACSA [Internet], 11
Nithya, 2019, Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction, SN Appl Sci, 1, 641, 10.1007/s42452-019-0645-7
Suman, 2019, Predicting risk of cervical cancer: a case study of machine learning, J Stat Manag Syst, 22, 689
Geetha, 2019, Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier, J Med Syst, 43, 286, 10.1007/s10916-019-1402-6
Fernandes, 2018, Supervised deep learning embeddings for the prediction of cervical cancer diagnosis, PeerJ Comput Sci, 10.7717/peerj-cs.154
Abdoh, 2018, Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques, IEEE Access, 6, 59475, 10.1109/ACCESS.2018.2874063
Wu, 2017, Data-driven diagnosisof cervical cancer with support vector machine-based approaches, IEEE Access., 5, 25189, 10.1109/ACCESS.2017.2763984
Xie, 2020, Prognostic assessment of cervical cancer patients by clinical staging and surgical-pathological factor: a support vector machine-based approach, Front Oncol, 10, 1353, 10.3389/fonc.2020.01353
Matsuo, 2019, Survival outcome prediction in cervical cancer: cox models vs deep-learning model, Am J Obstet Gynecol, 220, 381.e1, 10.1016/j.ajog.2018.12.030
Matsuo, 2017, A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer, Am J Obstet Gynecol, 217, 703, 10.1016/j.ajog.2017.08.012
Obrzut, 2017, Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods, BMC Cancer, 17, 840, 10.1186/s12885-017-3806-3
Papadia, 2015, When does neoadjuvant chemotherapy really avoid radiotherapy? Clinical predictors of adjuvant radiotherapy in cervical cancer, Ann Surg Oncol, 22, S944, 10.1245/s10434-015-4799-2
Tseng, 2014, Application of machine learning to predict the recurrence-proneness for cervical cancer, Neural Comput Appl, 24, 1311, 10.1007/s00521-013-1359-1
Sun, 2020, Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms, IEEE J Biomed Health Inform, 24, 1664, 10.1109/JBHI.2019.2944977
Makris, 2017, Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions, Diagn Cytopathol, 45, 202, 10.1002/dc.23649
Neofytou, 2015, Computer-aided diagnosis in hysteroscopic imaging, IEEE J Biomed Health Inform, 19, 1129, 10.1109/JBHI.2014.2332760
Yan, 2021, Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study, Eur Radiol, 31, 411, 10.1007/s00330-020-07099-8
Dong, 2020, Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial cancer in myometrium using MR images: a pilot study, IJERPH., 17, 5993, 10.3390/ijerph17165993
Chen, 2020, Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution, Eur Radiol, 30, 4985, 10.1007/s00330-020-06870-1
Hart, 2020, Population-based screening for endometrial cancer: human vs. machine intelligence, Front Artif Intell, 3, 539879, 10.3389/frai.2020.539879
Pergialiotis, 2018, The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women, Public Health, 164, 1, 10.1016/j.puhe.2018.07.012
Juliet, 2018, Early warning system for endometrial cancer prediction in PMB women using novel ensemble model, Int J Eng Res Technol, 11, 1533
Vezzoli, 2017, RERT: a novel regression tree approach to predict extrauterine disease in endometrial carcinoma patients, Sci Rep, 7, 10528, 10.1038/s41598-017-11104-4
Akazawa, 2021, The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study, Obstet Gynecol Sci, 64, 266, 10.5468/ogs.20248
Mysona, 2020, Clinical calculator predictive of chemotherapy benefit in stage 1A uterine papillary serous cancers, Gynecol Oncol, 156, 77, 10.1016/j.ygyno.2019.10.017
Gunakan, 2019, A novel prediction method for lymph node involvement in endometrial cancer: machine learning, Int J Gynecol Cancer, 29, 320, 10.1136/ijgc-2018-000033
Malek, 2019, A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters, Eur J Radiol, 110, 203, 10.1016/j.ejrad.2018.11.009
Nakagawa, 2019, A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with 18F-FDG PET/CT, Clin Radiol, 74, 167.e1, 10.1016/j.crad.2018.10.010
Köhler, 2019, Benign uterine mass-discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study, Arch Gynecol Obstet, 300, 1719, 10.1007/s00404-019-05344-0
Wu, 2018, Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks, Biosci Rep, 29;38, 10.1042/BSR20180289
BenTaieb, 2017, A structured latent model for ovarian carcinoma subtyping from histopathology slides, Med Image Anal, 39, 194, 10.1016/j.media.2017.04.008
Zhang, 2019, Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian cancer in color ultrasound detecting system, J Med Syst, 43, 251, 10.1007/s10916-019-1356-8
Martinez-Mas, 2019, Evaluation of machine learning methods with Fourier transform features for classifying ovarian tumors based on ultrasound images, PLoS One, 14, 10.1371/journal.pone.0219388
Aramendía-Vidaurreta, 2016, Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach, Ultrasound Med Biol, 42, 742, 10.1016/j.ultrasmedbio.2015.11.014
Acharya, 2014, GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization, Technol Cancer Res Treat, 13, 529, 10.7785/tcrtexpress.2013.600273
Acharya, 2012, Ovarian tumor characterization using 3D ultrasound, Technol Cancer Res Treat, 11, 543, 10.7785/tcrt.2012.500272
Wang, 2021, Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging, Eur Radiol, 31, 4960, 10.1007/s00330-020-07266-x
Wang, 2019, Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer, Radiother Oncol, 132, 171, 10.1016/j.radonc.2018.10.019
Lu, 2020, Using machine learning to predict ovarian cancer, Int J Med Inform, 141, 10.1016/j.ijmedinf.2020.104195
Akazawa, 2020, Artificial Intelligence in Ovarian Cancer Diagnosis, Anticancer Res, 40, 4795, 10.21873/anticanres.14482
Kawakami, 2019, Application of artificial intelligence for preoperative diagnostic and prognostic prediction in epithelial ovarian cancer based on blood biomarkers, Clin Cancer Res, 25, 3006, 10.1158/1078-0432.CCR-18-3378
Gu, 2018, Postprandial increase in serum CA125 as a surrogate biomarker for early diagnosis of ovarian cancer, J Transl Med, 16, 114, 10.1186/s12967-018-1489-4
Laios, 2020, Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models, J Ovarian Res, 13, 117, 10.1186/s13048-020-00700-0
Richardson, 2020, Long term survival outcomes of stage I mucinous ovarian cancer - a clinical calculator predictive of chemotherapy benefit, Gynecol Oncol, 159, 118, 10.1016/j.ygyno.2020.07.011
Paik, 2019, Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods, J Gynecol Oncol, 30, 10.3802/jgo.2019.30.e65
Bogani, 2018, Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer, J Gynecol Oncol, 29, 10.3802/jgo.2018.29.e66
Shinagare, 2018, High-grade serous ovarian cancer: use of machine learning to predict abdominopelvic recurrence on CT on the basis of serial cancer antigen 125 levels, J Am Coll Radiol, 15, 1133, 10.1016/j.jacr.2018.04.008
Tseng, 2017, Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence, Artif Intell Med, 78, 47, 10.1016/j.artmed.2017.06.003
Enshaei, 2015, Artificial intelligence systems as prognostic and predictive tools in ovarian cancer, Ann Surg Oncol, 22, 3970, 10.1245/s10434-015-4475-6
Sun, 2014, Design and implementation of a comprehensive web-based survey for ovarian cancer survivorship with an analysis of prediagnosis symptoms via text mining, Cancer Informatics, 13, 113
William
Fernandes, 2018, Automated methods for the decision support of cervical cancer screening using digital colposcopies, IEEE Access., 6, 33910, 10.1109/ACCESS.2018.2839338