An integrated model combined conventional radiomics and deep learning features to predict early recurrence of hepatocellular carcinoma eligible for curative ablation: a multicenter cohort study

Aim This study aimed to develop and validate a model (DLRR) that incorporates deep learning radiomics and traditional radiomics features to predict ER following curative ablation for HCC. Backround Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving prognosis. Methods We retrospectively analysed the data of 288 eligible patients from three hospitals—one primary cohort (centre 1, n=222) and two external test cohorts (centre 2, n=32 and centre 3, n=34)—from April 2008 to March 2022. 3D ResNet-18 and PyRadiomics were applied to extract features from contrast-enhanced computed tomography (CECT) images. The three-step (ICC-LASSO-RFE) method was used for feature selection, and six machine learning methods were used to construct models. Performance was compared via the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Calibration and clinical applicability were assessed via calibration curves and decision curve analysis (DCA), respectively. Kaplan-Meier (K-M) curves were generated to stratify patients based on progression-free survival (PFS) and overall survival (OS). Results The DLRR model had the best performance, with AUCs of 0.981, 0.910 and 0.851 in the training, internal validation, and external validation sets, respectively. NRI and IDI tests indicated that the DLRR model outperformed the DLR model (AUCs of 0.910 and 0.874; P < 0.05) and the Rad model (AUCs of 0.910 and 0.772; P < 0.05). Although the AUC of DLRR was slightly lower than that of the combined model (incorporating DLRR and clinico-radiological features), there was no significant difference (AUCs of 0.910 and 0.914; P > 0.05). Additionally, the calibration curve and DCA curve revealed that the DLRR model had good calibration ability and clinical applicability. The K-M curve indicated that the DLRR model provided risk stratification for progression-free survival (PFS) and overall survival (OS) in HCC patients. Conclusion The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.