A novel deep learning and radiomics approach based on DCE-MRI for predicting the P53 mutation status in hepatocellular carcinoma.

Background: P53-mutated hepatocellular carcinoma (HCC) is an aggressive subtype with poor prognosis, currently diagnosed only by invasive biopsy. Noninvasive preoperative prediction could guide personalized treatment. This study aimed to develop and evaluate a preoperative method for predicting P53-mutated HCC using deep learning (DL) and radiomics model based on magnetic resonance imaging (MRI). Methods: In this retrospective study, we included 320 consecutive patients who underwent surgical resection for HCC between January 2020 and February 2021, had postoperative P53 immunohistochemistry results, and showed no evidence of extrahepatic metastases preoperatively. Patients were randomly assigned to a Training cohort (n=224) and a Validation cohort (n=96). Clinical risk factors were identified through stepwise regression analysis, and a clinical prediction model was built. We developed a radiomics model and multiple convolutional neural network (CNN) models using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of HCC. Additionally, clinical models were constructed based on clinical and imaging features. Two fusion strategies were employed to build DL fusion models for the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP): the multi-channel (MC) DL model and the feature fusion (FF) DL model. Ultimately, we integrated the optimal DL model with the radiomics and clinical models into a combined model, developed nomograms to predict P53-mutated HCC, and evaluated its performance using receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis. Results: Of 320 patients, 259 were men and 61 were women (mean age, 58±11 years). There were 183 cases of P53-mutated HCC and 137 cases of non-P53-mutated HCC. In the MC DL model, ResNet34 achieved area under the curve (AUC) values of 0.731 [95% confidence interval (CI): 0.654-0.808] in the training cohort (TC) and 0.652 (95% CI: 0.447-0.858) in the validation cohort (VC), outperforming other CNNs. In the FF DL model, ResNet101 demonstrated an AUC of 0.779 (95% CI: 0.719-0.839) for TC and 0.663 (95% CI: 0.552-0.774) for VC, showing superior performance compared to other CNNs. Integration of the clinical, radiomics, and DL models improved the AUC to 0.838 (95% CI: 0.786-0.891) in the TC and 0.702 (95% CI: 0.598-0.807) in the VC. Conclusions: The combination of clinical, radiomics, and DL models provides an effective tool for preoperative prediction of P53 mutation status in patients with HCC.