Dynamic contrast-enhanced MRI deep learning model combined with clinical model for noninvasive differentiation of hepatocellular carcinoma from mass-forming intrahepatic cholangiocarcinoma.
Authors
Affiliations (9)
Affiliations (9)
- Department of Medical Imaging, Xinxiang Central Hospital, Xinxiang, China.
- Department of Nuclear Medicine, The First Affiliated Hospital of Henan Medical University, Weihui, China.
- Department of Magnetic Resonance, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China.
- Gansu Medical MRI Equipment Application Industry Technology Center, Lanzhou, China.
- Department of Magnetic Resonance, The First Affiliated Hospital of Henan Medical University, Weihui, China.
- Interventional Diagnostic and Therapeutic Center, Zhongnan Hospital of Wuhan University, Wuhan, China.
- Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology and Medical Imaging, West China Hospital of Sichuan University, Chengdu, China.
- Psychoradiology Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
Abstract
This study aims to develop a framework integrating dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based multi-channel deep learning combined with clinical modeling for the non-invasive preoperative discrimination between hepatocellular carcinoma (HCC) and mass-forming intrahepatic cholangiocarcinoma (MF-ICC). The internal development cohort comprised 334 surgically confirmed patients, including 231 patients with HCC and 103 with MF-ICC, and was divided at the patient level into a training cohort (<i>n</i> = 233) and an internal validation cohort (<i>n</i> = 101). The independent external validation cohort included 50 patients, comprising 40 with HCC and 10 with MF-ICC. We constructed separate two-dimensional deep learning models based on arterial phase, portal venous phase, and delayed phase images and then developed fusion models (2D model). A multi-channel deep learning model was further developed by directly inputting the three-phase images stacked along the channel dimension. Moreover, a clinical model was built based on independent predictors identified through multivariate analysis, which was ultimately integrated with the best-performing deep learning model. Model performance was evaluated and compared. Deep learning model directly feeding the three-phase images as a multi-channel input achieved better discrimination (area under the curve [AUC] = 0.814 in the internal validation cohort) than all single-phase and corresponding fusion models. The comprehensive 2D + multi-channel + clinical model showed the best performance for the differentiation of HCC from MF-ICC, achieving an AUC of 0.887 (95% CI, 0.819-0.954) in the internal validation cohort, and generalized well to the external validation cohort (AUC = 0.908; 95% CI, 0.811-1.000). However, no significant differences in AUCs were found among the models after correction for multiple comparisons. The fusion model integrating clinical predictors with DCE-MRI multi-phase deep learning feature may provide a valuable tool for preoperatively differentiating HCC from MF-ICC.