Xiaojun Chen1, Yida Wang2, Minhua Shen3, Bingyi Yang1, Qing Zhou3, Yinqiao Yi2, Weifeng Liu4, Guofu Zhang3, Guang Yang5, He Zhang6. 1. Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China. 2. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China. 3. Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China. 4. Department of Radiology, Guangzhou First People's Hospital, the Second Affiliated Hospital of South China University of Technology, Guangzhou, People's Republic of China. 5. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China. gyang@phy.ecnu.edu.cn. 6. Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China. dr.zhanghe@yahoo.com.
Abstract
OBJECTIVE: To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM). METHODS: We retrospectively enrolled 530 patients with pathologically proven EC at our institution between January 1, 2013, and December 31, 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both sagittal and coronal T2WI-based MR images were used for lesion area determination. All MR images were divided into two groups: deep (more than 50%) and shallow (less than 50%) MI based on their pathological diagnosis. We trained a detection model based on YOLOv3 algorithm to locate the lesion area on ECM. Then, the detected regions were fed into a classification model based on DL network to identify MI depth automatically. RESULTS: In the testing dataset, the trained model detected lesion regions with an average precision rate of 77.14% and 86.67% in both sagittal and coronal images, respectively. The classification model yielded an accuracy of 84.78%, a sensitivity of 66.67%, a specificity of 87.50%, a positive predictive value of 44.44%, and a negative predictive value of 94.59% in determining deep MI. The radiologists and trained network model together yielded an accuracy of 86.2%, a sensitivity of 77.8%, a specificity of 87.5%, a positive predictive value of 48.3%, and a negative predictive value of 96.3%. CONCLUSION: In this study, the DL network model derived from MR imaging provided a competitive, time-efficient diagnostic performance in MI depth identification. KEY POINTS: • The models established with the deep learning method could help improve the diagnostic confidence and performance of MI identification based on endometrial cancer MR imaging. • The models enabled the classification of endometrial cancer MR images to the two categories with a sensitivity of 0.67, a specificity of 0.88, and an accuracy of 0.85. • Using the detected lesion region to evaluate myometrial invasion depth could remove redundant information in the image and provide more effective features.
OBJECTIVE: To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM). METHODS: We retrospectively enrolled 530 patients with pathologically proven EC at our institution between January 1, 2013, and December 31, 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both sagittal and coronal T2WI-based MR images were used for lesion area determination. All MR images were divided into two groups: deep (more than 50%) and shallow (less than 50%) MI based on their pathological diagnosis. We trained a detection model based on YOLOv3 algorithm to locate the lesion area on ECM. Then, the detected regions were fed into a classification model based on DL network to identify MI depth automatically. RESULTS: In the testing dataset, the trained model detected lesion regions with an average precision rate of 77.14% and 86.67% in both sagittal and coronal images, respectively. The classification model yielded an accuracy of 84.78%, a sensitivity of 66.67%, a specificity of 87.50%, a positive predictive value of 44.44%, and a negative predictive value of 94.59% in determining deep MI. The radiologists and trained network model together yielded an accuracy of 86.2%, a sensitivity of 77.8%, a specificity of 87.5%, a positive predictive value of 48.3%, and a negative predictive value of 96.3%. CONCLUSION: In this study, the DL network model derived from MR imaging provided a competitive, time-efficient diagnostic performance in MI depth identification. KEY POINTS: • The models established with the deep learning method could help improve the diagnostic confidence and performance of MI identification based on endometrial cancer MR imaging. • The models enabled the classification of endometrial cancer MR images to the two categories with a sensitivity of 0.67, a specificity of 0.88, and an accuracy of 0.85. • Using the detected lesion region to evaluate myometrial invasion depth could remove redundant information in the image and provide more effective features.
Entities:
Keywords:
Deep learning; Endometrial cancer; Magnetic resonance imaging; Neoplasm staging
Authors: Erling A Hoivik; Erlend Hodneland; Julie A Dybvik; Kari S Wagner-Larsen; Kristine E Fasmer; Hege F Berg; Mari K Halle; Ingfrid S Haldorsen; Camilla Krakstad Journal: Commun Biol Date: 2021-12-06