早期肝细胞癌（HCC）患者在根治性切除术后的预后引起了广泛关注，但可靠的预测方法缺乏。基于增强计算机断层扫描（CT）图像的放射omics为HCC患者的实用预测提供了一个潜在途径。我们招募了接受根治性切除术的早期HCC患者。进行统计分析以确定与复发相关的临床病理学和放射omic特征。使用四种算法构建了临床、放射omic和联合模型（包括临床病理学和放射omic特征）。通过五折交叉验证对这些模型的性能进行了详细检验，评估指标包括曲线下面积（AUC）、准确性（ACC）、敏感性（SEN）和特异性（SPE），并进行了计算和比较。最终，通过将独立的临床病理学预测因子与Radscore结合起来制定了一个综合诊断表。从2016年1月至2020年12月，观察到167例（64.5%）HCC复发病例，初始切除后的复发中位时间为26.7个月。联合模型胜过仅依赖于临床病理学或放射omic特征的模型。值得注意的是，在联合模型中，使用支持向量机（SVM）算法的模型表现出最有前景的预测结果（AUC：0.840（95%置信区间【0.696，0.984】），ACC：0.805，SEN：0.849，SPE：0.733）。Hepatitis B感染、肿瘤大小>5 cm和alpha-胎球蛋白（AFP）>400 ng/mL被确定为独立的复发预测因子，并随后与Radscore相结合以创建直观的诊断表，提供稳健可靠的预测性能。结合临床病理学和放射omic特征的机器学习模型为临床医生提供了一个有价值的工具，用于预测术后HCC复发，从而指导早期预防策略。© 2023作者，在Springer-Verlag GmbH Germany（Springer Nature的一部分）独家许可下发表。

The 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.We 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.From 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.Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.