Development and External Validation of a Multimodal Machine Learning Model Integrating Preoperative Contrast-Enhanced CT Radiomics and Clinicopathological Features for Predicting Early Extrahepatic Recurrence After Hepatectomy for Hepatocellular Carcinoma.
Authors
Affiliations (4)
Affiliations (4)
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
- Laboratory of Automatic Software Generation and Intelligent Service, Chengdu University of Information Technology, Chendu, People's Republic of China.
- Section for Day Surgery, Department of General Surgery, The Third People's Hospital of Chengdu & The Affiliated Hospital of Southwest Jiaotong University, Chengdu, People's Republic of China.
- Department of Hepatobiliary and Pancreatic Surgery, People's Hospital of Leshan, Leshan, People's Republic of China.
Abstract
Early extrahepatic recurrence (eEHR) after hepatectomy for hepatocellular carcinoma (HCC) significantly affects patient prognosis; however, an accurate predictive framework integrating radiomics with clinicopathological features is currently lacking. This study aims to develop and validate a multimodal machine learning model for prediction of early postoperative extrahepatic recurrence risk, thereby offering a basis for individualized postoperative intervention strategies. A total of 208 patients who received surgical resection and had a postoperative pathological diagnosis of HCC were retrospectively enrolled from two centers between 2018 and 2023. The cohort was divided into a training set and an internal validation set at a 7:3 ratio. An external validation set was also established. Preoperative enhanced CT radiomic features and clinicopathological characteristics were extracted. After screening for key features using regression analysis, multiple machine learning prediction models were constructed via nested k-fold cross validation. The performance of the models was evaluated, and survival analysis was subsequently conducted. Among the 208 patients, 55 (26.4%) had postoperative eEHR. The most common site of recurrence was the lung (26 cases, 47.3%), followed by lymph nodes (12 cases, 21.8%) and bone (10 cases, 18.2%). Three core clinicopathological features and four key radiomic features were identified. A multimodal predictive model for HCC-eEHR was developed using the Random Forest algorithm. This model achieved an AUC of 0.852 (sensitivity 0.511, specificity 0.959) in the training cohort and an AUC of 0.836 (sensitivity 0.750, specificity 0.875, accuracy 0.850) in the external validation cohort. A 24-month landmark analysis (n=156) confirmed that patients developing eEHR within 24 months had significantly worse post-landmark overall survival (median 16.3 vs 47.7 months; HR=3.342, 95%CI: 1.570-7.112, P<0.001). Furthermore, the model demonstrated effective risk stratification in both cohorts (Southwest OS: HR=3.549, 95%CI: 2.097-6.006, P<0.001; Leshan OS: HR=3.839, 95%CI: 1.171-8.369, P=0.026). The developed multimodal prediction model may assist in assessing the risk of eEHR following HCC hepatectomy, thereby facilitating the timely identification of high-risk patients and informing personalized postoperative surveillance and treatment strategies.