Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble

Computers in Biology and Medicine - Tập 134 - Trang 104487 - 2021
Xueliang Zhu1, Jie Ying2, Haima Yang1, Le Fu3, Boyang Li2, Bin Jiang1
1School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
2School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
3Department of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China

Tài liệu tham khảo

Sung, 2021, Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA A Cancer J. Clin., 10.3322/caac.21660 Morice, 2016, Endometrial cancer, 1094 Amant, 2018, Cancer of the corpus uteri, Int. J. Gynecol. Obstet., 143, 37, 10.1002/ijgo.12612 Wu, 2013, The accuracy of magnetic resonance imaging for preoperative deep myometrium assessment in endometrial cancer, Taiwan, J. Obstet. Gynecol., 52, 210 Quan, 2020, The prominent value of apparent diffusion coefficient in assessing high-risk factors and prognosis for patients with endometrial carcinoma before treatment, Acta Radiol. Reyes-Pérez, 2020, The apparent diffusion coefficient (ADC) on 3-T MRI differentiates myometrial invasion depth and histological grade in patients with endometrial cancer, Acta Radiol., 61, 1277, 10.1177/0284185119898658 Cai, 2020, MR volumetry in predicting the aggressiveness of endometrioid adenocarcinoma: correlation with final pathological results, Acta Radiol., 61, 705, 10.1177/0284185119877331 Yan, 2019, Preoperative prediction of deep myometrial invasion and tumor grade for stage I endometrioid adenocarcinoma: a simple method of measurement on DWI, Eur. Radiol., 29, 838, 10.1007/s00330-018-5653-2 Song, 2020, Quantitative assessment of diffusion kurtosis imaging depicting deep myometrial invasion: a comparative analysis with diffusion-weighted imaging, Diagnostic Interv. Radiol., 26, 74, 10.5152/dir.2019.18366 Ghosh, 2020, DTI histogram parameters correlate with the extent of myoinvasion and tumor type in endometrial carcinoma: a preliminary analysis, Acta Radiol., 61, 675, 10.1177/0284185119875019 Ueno, 2017, Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification - a preliminary analysis1, Radiology, 284, 748, 10.1148/radiol.2017161950 Arnaldo, 2020, Deep myometrial infiltration of endometrial cancer on MRI: a radiomics-powered machine learning pilot study, Acad. Radiol. Ytre-Hauge, 2018, Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer, J. Magn. Reson. Imag., 48, 1637, 10.1002/jmri.26184 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 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, Int. J. Environ. Res. Publ. Health, 17, 1, 10.3390/ijerph17165993 Watanabe, 2015, Automated detection and measurement of uterine peristalsis in cine MR images, J. Magn. Reson. Imag., 42, 644, 10.1002/jmri.24817 Kurata, 2019, Automatic segmentation of the uterus on MRI using a convolutional neural network, Comput. Biol. Med., 114, 103438, 10.1016/j.compbiomed.2019.103438 Beddy, 2012, FIGO staging system for endometrial cancer: added benefits of MR imaging, Radiographics, 32, 241, 10.1148/rg.321115045 Peungjesada, 2009, Magnetic resonance imaging of endometrial carcinoma, J. Comput. Assist. Tomogr., 33, 601, 10.1097/RCT.0b013e31818d4279 Otsu, 1979, Threshold selection method from gray-level histograms, IEEE Trans Syst Man Cybern. SMC-, 9, 62, 10.1109/TSMC.1979.4310076 Liang, 1993, Hierarchical algorithms for morphological image processing, Pattern Recogn., 26, 10.1016/0031-3203(93)90107-8 Fleischer, 1987, vol. 162 Gordon, 1990, Preoperative assessment of myometrial invasion of endometrial adenocarcinoma by sonography (US) and magnetic resonance imaging (MRI), Int. J. Gynecol. Obstet., 32, 96 Guyon, 2002, Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 10.1023/A:1012487302797 Lu, 2017, A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning, Comput. Biol. Med., 10.1016/j.compbiomed.2017.03.002 Ji-Jiang, 2016, Exploiting ensemble learning for automatic cataract detection and grading - ScienceDirect, Comput. Methods Progr. Biomed., 124, 45, 10.1016/j.cmpb.2015.10.007 Shakeel, 2020, Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier, Neural Comput. Appl., 10.1007/s00521-018-03972-2 Burges, 1998, A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Discov., 2, 121, 10.1023/A:1009715923555 Platt, 1999, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Adv. Large Margin Classif, 10, 61 Hsu C.W., Chang C.C., Lin C.J., A Practical Guide to Support Vector Classification, https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (2003), Accessed 10th Jul 2020. Pedregosa, 2011, Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825 Fernandez-Delgado, 2014, Do we need hundreds of classifiers to solve real world classification problems?, J. Mach. Learn. Res., 15, 3133 Probst, 2019, Tunability: importance of hyperparameters of machine learning algorithms, J. Mach. Learn. Res., 20 Probst, 2019, Hyperparameters and tuning strategies for random forest, Wiley Interdiscip. Rev. Data Min. Knowl. Discov, 9, 10.1002/widm.1301 Haldorsen, 2012, Standard 1.5-T MRI of endometrial carcinomas: modest agreement between radiologists, Eur. Radiol., 22, 1601, 10.1007/s00330-012-2400-y Woo, 2017, Assessment of deep myometrial invasion of endometrial cancer on MRI: added value of second-opinion interpretations by radiologists subspecialized in gynaecologic oncology, Eur. Radiol., 27, 10.1007/s00330-016-4582-1 Masood, 2020, Automated decision support system for lung cancer detection and classification via enhanced RFCN with multilayer fusion RPN, IEEE Trans. Ind. Informatics., 16, 7791, 10.1109/TII.2020.2972918 Cruz, 2020, Kidney segmentation from computed tomography images using deep neural network, Comput. Biol. Med., 123, 103906, 10.1016/j.compbiomed.2020.103906 Buda, 2019, Deep learning-based segmentation of nodules in thyroid ultrasound: improving performance by utilizing markers present in the images, Ultrasound Med. Biol., 46, 415, 10.1016/j.ultrasmedbio.2019.10.003