دورية أكاديمية
A Multiparametric Fusion Deep Learning Model Based on DCE-MRI for Preoperative Prediction of Microvascular Invasion in Intrahepatic Cholangiocarcinoma.
| العنوان: | A Multiparametric Fusion Deep Learning Model Based on DCE-MRI for Preoperative Prediction of Microvascular Invasion in Intrahepatic Cholangiocarcinoma. |
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| المؤلفون: | Gao W; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China., Wang W; Department of Radiology, Cancer center, Zhongshan Hospital, Fudan University, China.; Shanghai Institute of Medical Imaging, Shanghai, China., Song D; Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.; Department of Interventional Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China., Wang K; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China., Lian D; Department of Radiology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China., Yang C; Department of Radiology, Cancer center, Zhongshan Hospital, Fudan University, China., Zhu K; Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China., Zheng J; Department of Interventional Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China., Zeng M; Department of Radiology, Cancer center, Zhongshan Hospital, Fudan University, China.; Shanghai Institute of Medical Imaging, Shanghai, China., Rao SX; Department of Radiology, Cancer center, Zhongshan Hospital, Fudan University, China.; Shanghai Institute of Medical Imaging, Shanghai, China., Wang M; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China. |
| المصدر: | Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2022 Oct; Vol. 56 (4), pp. 1029-1039. Date of Electronic Publication: 2022 Feb 22. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9105850 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2586 (Electronic) Linking ISSN: 10531807 NLM ISO Abbreviation: J Magn Reson Imaging Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: <2005-> : Hoboken , N.J. : Wiley-Liss Original Publication: Chicago, IL : Society for Magnetic Resonance Imaging, c1991- |
| مواضيع طبية MeSH: | Bile Duct Neoplasms*/diagnostic imaging , Bile Duct Neoplasms*/surgery , Cholangiocarcinoma*/diagnostic imaging , Cholangiocarcinoma*/surgery , Deep Learning*, Bile Ducts, Intrahepatic ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Retrospective Studies ; Sensitivity and Specificity |
| مستخلص: | Background: Assessment of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) by using a noninvasive method is an unresolved issue. Deep learning (DL) methods based on multiparametric fusion of MR images have the potential of preoperative assessment of MVI. Purpose: To investigate whether a multiparametric fusion DL model based on MR images can be used for preoperative assessment of MVI in ICC. Study Type: Retrospective. Population: A total of 519 patients (200 females and 319 males) with a single ICC were categorized as a training (n = 361), validation (n = 90), and an external test cohort (n = 68). Field Strength/sequence: A 1.5 T and 3.0 T; axial T2-weighted turbo spin-echo sequence, diffusion-weighted imaging with a single-shot spin-echo planar sequence, and dynamic contrast-enhanced (DCE) imaging with T1-weighted three-dimensional quick spoiled gradient echo sequence. Assessment: DL models of multiparametric fusion convolutional neural network (CNN) and late fusion CNN were both constructed for evaluating MVI in ICC. Gradient-weighted class activation mapping was used for visual interpretation of MVI status in ICC. Statistical Tests: The DL model performance was assessed through the receiver operating characteristic curve (ROC) analysis, and the area under the ROC curve (AUC) with the accuracy, sensitivity, and specificity were measured. P value < 0.05 was considered as statistical significance. Results: In the external test cohort, the proposed multiparametric fusion DL model achieved an AUC of 0.888 with an accuracy of 86.8%, sensitivity of 85.7%, and specificity of 87.0% for evaluating MVI in ICC, and the positive predictive value and negative predictive value were 63.2% and 95.9%, respectively. The late fusion DL model achieved a lower AUC of 0.866, with an accuracy of 83.8%, sensitivity of 78.6%, specificity of 85.2% for evaluating MVI in ICC. Data Conclusion: Our DL model based on multiparametric fusion of MRI achieved a good diagnostic performance in the evaluation of MVI in ICC. Level of Evidence: 3 TECHNICAL EFFICACY: Stage 2. (© 2022 International Society for Magnetic Resonance in Medicine.) |
| التعليقات: | Comment in: J Magn Reson Imaging. 2022 Oct;56(4):"1040-1041". (PMID: 35188693) |
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| فهرسة مساهمة: | Keywords: deep learning; intrahepatic cholangiocarcinoma; magnetic resonance imaging; microvascular invasion |
| تواريخ الأحداث: | Date Created: 20220222 Date Completed: 20220916 Latest Revision: 20221108 |
| رمز التحديث: | 20240513 |
| DOI: | 10.1002/jmri.28126 |
| PMID: | 35191550 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1522-2586 |
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| DOI: | 10.1002/jmri.28126 |