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.
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 institutio...
| Publicado en: | European Radiology Vol. 30; no. 9; pp. 4985 - 4995 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145263155&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145263155 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2020 vid: 30 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145263155 143971568 145263155 NLM32337640 145263155 10.1007/s00330-020-06870-1 NLM32337640 145263155 ppf: 4985 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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. aug: au: Chen, Xiaojun Wang, Yida Shen, Minhua Yang, Bingyi Zhou, Qing Yi, Yinqiao Liu, Weifeng Zhang, Guofu Yang, Guang Zhang, He affil: Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China sug: subj: Myometrium Pathology Magnetic Resonance Imaging Methods Endometrial Neoplasms Diagnosis Algorithms Neoplasm Invasiveness Middle Age Female Retrospective Design Funding Source Human Middle Aged: 45-64 years Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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