دورية أكاديمية
A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images.
| العنوان: | A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images. |
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| المؤلفون: | Cheng D; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Zhuo Z; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Du J; Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China., Weng J; Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, P.R. China., Zhang C; Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, P.R. China., Duan Y; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Sun T; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Wu M; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Guo M; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Hua T; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Jin Y; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Peng B; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China., Li Z; BioMind Inc., Beijing, P.R. China., Zhu M; Department of Radiology, Sanbo Brain Hospital, Capital Medical University, Beijing, P.R. China., Imami M; Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland., Bettegowda C; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland., Sair H; Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland., Bai HX; Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland., Barkhof F; Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom.; Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands., Liu X; Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China., Liu Y; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China. |
| المصدر: | Clinical cancer research : an official journal of the American Association for Cancer Research [Clin Cancer Res] 2024 Jan 05; Vol. 30 (1), pp. 150-158. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: The Association Country of Publication: United States NLM ID: 9502500 Publication Model: Print Cited Medium: Internet ISSN: 1557-3265 (Electronic) Linking ISSN: 10780432 NLM ISO Abbreviation: Clin Cancer Res Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Denville, NJ : The Association, c1995- |
| مواضيع طبية MeSH: | Deep Learning* , Ependymoma*/diagnostic imaging , Ependymoma*/genetics, Humans ; Retrospective Studies ; Area Under Curve ; Clinical Decision-Making ; Phenylphosphonothioic Acid, 2-Ethyl 2-(4-Nitrophenyl) Ester ; Magnetic Resonance Imaging |
| مستخلص: | Purpose: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images. Experimental Design: We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. Results: For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95). Conclusions: A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making. (©2023 American Association for Cancer Research.) |
| معلومات مُعتمدة: | 81870958 National Science Foundation of China; 81571631 National Science Foundation of China; JQ20035 Beijing Municipal Natural Science Foundation for Distinguished Young Scholars; XTYB201831 Beijing Youth Scholar, and the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospital Authority |
| المشرفين على المادة: | 2104-64-5 (Phenylphosphonothioic Acid, 2-Ethyl 2-(4-Nitrophenyl) Ester) |
| تواريخ الأحداث: | Date Created: 20231102 Date Completed: 20240108 Latest Revision: 20240314 |
| رمز التحديث: | 20240315 |
| DOI: | 10.1158/1078-0432.CCR-23-1461 |
| PMID: | 37916978 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1557-3265 |
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| DOI: | 10.1158/1078-0432.CCR-23-1461 |