Deep multi-instance learning model based on gadoxetic acid-enhanced MRI for predicting microvascular invasion of hepatocellular carcinoma: a multicenter, retrospective study.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology), 518020, Shenzhen, Guangdong, China.
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 510280, Guangzhou, Guangdong, China.
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, 510630, Guangzhou, Guangdong, China.
- Department of Radiology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology), 518020, Shenzhen, Guangdong, China. [email protected].
Abstract
Microvascular invasion (MVI) is of great significance for the individualized treatment of hepatocellular carcinoma (HCC) and preoperative noninvasive prediction of MVI is still an urgent clinical problem. To explore the effects of different regions of interest (ROI) and image input dimensions on the performance of deep learning (DL) models, and to select the best result to develop and validate a DL model for preoperative prediction of MVI. A total of 206 patients with pathologically confirmed HCC from three hospitals were retrospectively enrolled and divided into training, internal validation and external test set. Based on hepatobiliary phase images (HBP) of gadoxetic acid-enhanced MRI, 2D DL, 3D DL and 2.5D deep multi-instance learning (MIL) models were established. The receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of the above models. Based on the optimal performance model, the T1WI-FS and T2WI-FS images were preprocessed correspondingly, and a multimodal prediction model including three sequences was constructed. The ROC, and decision curve were used to visualize the predictive ability of the model. Compared with 2D DL and 3D DL models, the 2.5D DL model based on all axial images of ROI had the highest performance, with the AUC values of 0.802 (95% CI, 0.669-0.936) and 0.759 (95% CI, 0.643-0.875) in the validation and test sets. The AUCs of the multimodal MRI model were 0.954 (95% CI, 0.920-0.989) in the training set, 0.857 (95% CI, 0.736-0.978) in the validation set, and 0.788 (95% CI, 0.681-0.895) in the test set. The DL model that selects all axial slices of intratumor and peritumor as input shows robust capability in predicting MVI, which is expected to help clinical decision-making of individualized treatment for HCC.