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基于放射组学的早期肝细胞癌根治性切除术后复发的预后评估。

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

机构信息

Department of Hepato-Pancreato-Biliary & Gastric Medical Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.

Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, 310022, Zhejiang, China.

出版信息

J Cancer Res Clin Oncol. 2023 Nov;149(16):14983-14996. doi: 10.1007/s00432-023-05291-z. Epub 2023 Aug 22.

Abstract

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.

摘要

目的

根治性切除术后早期肝细胞癌(HCC)患者的预后受到广泛关注,但缺乏可靠的预测方法。增强计算机断层扫描(CT)成像衍生的放射组学为 HCC 患者的实际预后提供了一个潜在途径。

方法

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和放射组学特征。使用四种算法构建临床、放射组学和联合模型(包含临床病理和放射组学特征)。通过五重交叉验证来检查这些模型的性能,计算并比较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)。最终,通过将独立的临床病理预测因子与 Radscore 相结合,设计了一个综合列线图。

结果

2016 年 1 月至 2020 年 12 月,167 例(64.5%)患者观察到 HCC 复发,初始切除后复发的中位时间为 26.7 个月。联合模型优于仅依赖临床病理或放射组学特征的模型。值得注意的是,在联合模型中,采用支持向量机(SVM)算法的模型具有最有前景的预测结果(AUC:0.840(95%置信区间(CI):[0.696,0.984]),ACC:0.805,SEN:0.849,SPE:0.733)。乙型肝炎感染、肿瘤大小>5cm 和甲胎蛋白(AFP)>400ng/ml 被确定为独立的复发预测因子,并与 Radscore 相结合创建了一个直观的列线图,提供了强大而可靠的预测性能。

结论

整合临床病理和放射组学特征的机器学习模型为临床医生提供了一种预测 HCC 术后复发的有用工具,从而为早期预防策略提供信息。

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