A <scp>Multiparametric</scp> Fusion Deep Learning Model Based on <scp>DCE‐MRI</scp> for Preoperative Prediction of Microvascular Invasion in Intrahepatic Cholangiocarcinoma
Wenyu Gao, Wentao Wang, Danjun Song, Kang Wang, Danlan Lian, Chun Yang, Kai Zhu, Jiaping Zheng, Mengsu Zeng, Shengxiang Rao, Manning Wang
Abstract
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.