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

Author

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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)

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.

Suggested Citation

  • Hsiang-Chun Dong & Hsiang-Kai Dong & Mu-Hsien Yu & Yi-Hsin Lin & Cheng-Chang Chang, 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, MDPI, vol. 17(16), pages 1-18, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5993-:d:400492
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    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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    1. Chee Keong Wee & Xujuan Zhou & Ruiliang Sun & Raj Gururajan & Xiaohui Tao & Yuefeng Li & Nathan Wee, 2022. "Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques," IJERPH, MDPI, vol. 19(12), pages 1-13, June.

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