{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T11:50:48Z","timestamp":1790509848911,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010425","name":"Tri-Service General Hospital","doi-asserted-by":"publisher","award":["TSGH-C108-115, TSGH-D109-106, TSGH-D109-107 and TSGH-E-109229"],"award-info":[{"award-number":["TSGH-C108-115, TSGH-D109-106, TSGH-D109-107 and TSGH-E-109229"]}],"id":[{"id":"10.13039\/501100010425","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 107-2314-B-016-036"],"award-info":[{"award-number":["MOST 107-2314-B-016-036"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists\u2019 diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.<\/jats:p>","DOI":"10.3390\/ijerph17165993","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T11:15:27Z","timestamp":1597749327000},"page":"5993","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3642-3251","authenticated-orcid":false,"given":"Hsiang-Chun","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiang-Kai","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Public Administration &amp; Taiwan Institute for Governance and Communication Research, National Chengchi University, Taipei 116, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu-Hsien","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Hsin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7356-446X","authenticated-orcid":false,"given":"Cheng-Chang","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,18]]},"reference":[{"key":"ref_1","unstructured":"American Cancer Society (2018). Global Cancer Facts & Figures, American Cancer Society. [4th ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/jnci\/djx214","article-title":"International patterns and trends in endometrial cancer incidence, 1978\u20132013","volume":"110","author":"Ferlay","year":"2018","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"SGO Clinical Practice Endometrial Cancer Working Group, Burke, W.M., Orr, J., Leitao, M., Salom, E., Gehrig, P., Olawaiye, A.B., Brewer, M., Boruta, D., and Herzog, T.J. (2014). Endometrial cancer: A review and current management strategies: Part I. Gynecol. Oncol., 134, 385\u2013392.","DOI":"10.1016\/j.ygyno.2014.05.018"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"SGO Clinical Practice Endometrial Cancer Working Group, Burke, W.M., Orr, J., Leitao, M., Salom, E., Gehrig, P., Olawaiye, A.B., Brewer, M., Boruta, D., and Herzog, T.J. (2014). Endometrial cancer: A review and current management strategies: Part II. Gynecol. Oncol., 134, 393\u2013402.","DOI":"10.1016\/j.ygyno.2014.06.003"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1097\/IGC.0000000000000609","article-title":"ESMO-ESGO-ESTRO consensus conference on endometrial cancer: Diagnosis, treatment and follow-up","volume":"26","author":"Colombo","year":"2016","journal-title":"Int. J. Gynecol. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40644-016-0069-1","article-title":"MRI of endometrium cancer\u2014How we do it","volume":"16","author":"Meissnitzer","year":"2016","journal-title":"Meissnitzer Forstner Cancer Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/0029-7844(96)00161-5","article-title":"Prognostic significance of gross myometrial invasion with endometrial cancer","volume":"88","author":"Larson","year":"1996","journal-title":"Obstet. Gynecol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"301","DOI":"10.3802\/jgo.2014.25.4.301","article-title":"Lymphadenectomy can be omitted for low-risk endometrial cancer based on preoperative assessments","volume":"25","author":"Mitamura","year":"2014","journal-title":"J. Gynecol. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e86","DOI":"10.3802\/jgo.2017.28.e86","article-title":"Transvaginal ultrasound versus magnetic resonance imaging for preoperative assessment of myometrial infiltration in patients with endometrial cancer: A systematic review and meta-analysis","volume":"28","author":"Navarro","year":"2017","journal-title":"J. Gynecol. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1148\/radiology.179.3.2028000","article-title":"MR imaging evaluation of endometrial carcinoma: Results of an NCI cooperative study","volume":"179","author":"Hricak","year":"1991","journal-title":"Radiology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.3802\/jgo.2016.27.e38","article-title":"Long-term outcomes of magnetic resonance imaging-invisible endometrial cancer","volume":"27","author":"Choi","year":"2016","journal-title":"J. Gynecol. Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3322\/caac.21552","article-title":"Artificial intelligence in cancer imaging: Clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"ref_13","first-page":"e2475","article-title":"Artificial intelligence in medicine and radiation oncology","volume":"10","author":"Weidlich","year":"2018","journal-title":"Cureus"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"293","DOI":"10.2214\/AJR.18.20532","article-title":"Artificial intelligence in breast imaging\u2014Potentials and limitations","volume":"212","author":"Mendelson","year":"2019","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"232","DOI":"10.7150\/thno.28447","article-title":"Artificial intelligence-based decision-making for age-related macular degeneration","volume":"9","author":"Hwang","year":"2019","journal-title":"Theranostics"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1007\/s00330-016-4582-1","article-title":"Assessment of deep myometrial invasion of endometrial cancer on MRI: Added value of second-opinion interpretations by radiologists subspecialized in gynaecologic oncology","volume":"27","author":"Woo","year":"2017","journal-title":"Eur. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1148\/radiol.11110984","article-title":"Evaluation of depth of myometrial invasion and overall staging in endometrial cancer: Comparison of diffusion-weighted and dynamic contrast-enhanced MR imaging","volume":"262","author":"Beddy","year":"2012","journal-title":"Radiology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1109\/JBHI.2019.2944977","article-title":"Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms","volume":"24","author":"Sun","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yasaka, K., and Abe, O. (2018). Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002707"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. arXiv, Available online: https:\/\/arxiv.org\/abs\/1505.04597.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_24","unstructured":"Iglovikov, V., and Shvets, A. (2018). TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation. arXiv, Available online: https:\/\/arxiv.org\/abs\/1801.05746."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shvets, A.A., Iglovikov, V.I., Rakhlin, A., and Kalinin, A.A. (2018, January 17\u201320). Angiodysplasia detection and localization using deep convolutional neural networks. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications, Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00098"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv, Available online: https:\/\/arxiv.org\/abs\/1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","unstructured":"Iglovikov, V., Mushinskiy, S., and Osin, V. (2017). Satellite imagery feature detection using deep convolutional neural network: A Kaggle competition. arXiv, Available online: https:\/\/arxiv.org\/abs\/1706.06169."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Iglovikov, V., Rakhlin, A., Kalinin, A., and Shvets, A. (2017). Pediatric bone age assessment using deep convolutional neural networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/1712.05053.","DOI":"10.1101\/234120"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.-M., Zietz, M., and Hoffman, M.M. (2017). Opportunities and obstacles for deep learning in biology and medicine. bioRxiv, 142760.","DOI":"10.1101\/142760"},{"key":"ref_30","unstructured":"Wang, G., Li, W., Ourselin, S., and Vercauteren, T. (2017). Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. arXiv, Available online: https:\/\/arxiv.org\/pdf\/1709.00382.pdf."},{"key":"ref_31","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). ENet: A deep neural network architecture for real-time semantic segmentation. arXiv, Available online: https:\/\/arxiv.org\/abs\/1606.02147."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"259","DOI":"10.2214\/AJR.18.20391","article-title":"A review of the role of augmented intelligence in breast imaging: From automated breast density assessment to risk stratification","volume":"212","author":"Arieno","year":"2019","journal-title":"AJR"},{"key":"ref_33","unstructured":"Yuheng, S., and Hao, Y. (2017). Image segmentation algorithms overview. arXiv, Available online: https:\/\/arxiv.org\/abs\/1707.02051."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1148\/radiol.10101147","article-title":"The value of MR imaging when the site of uterine cancer origin is uncertain","volume":"258","author":"Vargas","year":"2011","journal-title":"Radiology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1200\/JCO.2011.38.8645","article-title":"Recurrence and survival after random assignment to laparoscopy versus laparotomy for comprehensive surgical staging of uterine cancer: Gynecologic Oncology Group LAP2 Study","volume":"30","author":"Walker","year":"2012","journal-title":"J. Clin. Oncol."},{"key":"ref_36","first-page":"CD006655","article-title":"Laparoscopy versus laparotomy for the management of early stage endometrial cancer","volume":"10","author":"Khadra","year":"2018","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1067\/mob.2000.107335","article-title":"Low-risk corpus cancer: Is lymphadenectomy or radiotherapy necessary?","volume":"182","author":"Andrea","year":"2000","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1148\/radiol.2017161950","article-title":"Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis","volume":"284","author":"Ueno","year":"2017","journal-title":"Radiology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1002\/jmri.26184","article-title":"Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer","volume":"48","author":"Dybvik","year":"2018","journal-title":"J. Magn. Reson. Imaging"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/17\/16\/5993\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:02:33Z","timestamp":1760176953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/17\/16\/5993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,18]]},"references-count":39,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["ijerph17165993"],"URL":"https:\/\/doi.org\/10.3390\/ijerph17165993","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,18]]}}}