增殖性肝细胞癌（HCC）是一类预后不良的侵袭性肿瘤。我们的目的是构建基于计算机断层扫描 (CT) 的放射组学列线图来预测增殖性 HCC、对临床结果进行分层并探索肿瘤微环境。从两个医疗中心回顾性收集肝切除术后病理诊断为 HCC 的患者。使用训练队列 (n = 184) 构建基于 CT 的放射组学列线图，结合放射组学模型和临床放射学特征来预测增殖性 HCC，并使用内部测试队列 (n = 80) 和外部测试队列 (n = 89) 进行验证。对接受手术 (n = 201) 或接受经动脉化疗栓塞 (TACE, n = 104) 的 HCC 患者评估列线图对临床结果的预测性能。使用癌症成像档案数据库中的 RNA 测序数据和组织学组织切片进行转录组学和病理组学分析。在训练、内部预测中，预测增殖性 HCC 的放射组学列线图的受试者工作特征曲线下面积分别为 0.84、0.87 和 0.85。分别是测试和外部测试队列。放射组学列线图可以对手术结果队列中的早期无复发生存率（风险比[HR] = 2.25；P < 0.001）和TACE结果队列中的无进展生存率（HR = 2.21；P = 0.03）进行分层。转录组学和病理组学分析表明，放射组学列线图与碳代谢、免疫细胞浸润、TP53 突变和肿瘤细胞异质性相关。基于 CT 的放射组学列线图可以预测增殖性 HCC、对临床结果进行分层并测量促肿瘤微环境.© 2024。作者。

Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment.Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis.The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells.The CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.© 2024. The Author(s).