Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection

PurposeThe prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients.MethodsWe recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore.ResultsFrom January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7 months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5 cm, and alpha-fetoprotein (AFP) > 400 ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance.ConclusionMachine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.

Automated summary

This study aimed to construct models using radiomics derived from enhanced computed tomography (CT) imaging to predict the risk of postoperative recurrence in early-stage hepatocellular carcinoma patients. Clinical, radiomic, and combined models were built using four algorithms and evaluated through cross-validation. The results showed that the combined models outperformed those relying solely on clinical or radiomic features. This research provides clinicians with a valuable tool to predict postoperative recurrence and inform early preventive strategies.