Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study

Hsiang-Chun Dong, Hsiang-Kai Dong, Mu-Hsien Yu, Yi-Hsin Lin and Cheng-Chang Chang
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Hsiang-Chun Dong: Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Hsiang-Kai Dong: Department of Public Administration & Taiwan Institute for Governance and Communication Research, National Chengchi University, Taipei 116, Taiwan
Mu-Hsien Yu: Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Yi-Hsin Lin: Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Cheng-Chang Chang: Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan

IJERPH, 2020, vol. 17, issue 16, 1-18

Abstract: 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’ 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.

Keywords: artificial intelligence; endometrial neoplasms; magnetic resonance imaging (MRI); neoplasm staging; neural networks (computer) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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