基于CT的放射组学列线图预测增殖性肝细胞癌及其肿瘤微环境探索

CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment

Journal of Translational Medicine

影响因子:7.5

分区:医学2区 Top / 医学：研究与实验2区

发表日期:2024 Sep 02

作者: Gongzheng Wang, Feier Ding, Kaige Chen, Zhuoshuai Liang, Pengxi Han, Linxiang Wang, Fengyun Cui, Qiang Zhu, Zhaoping Cheng, Xingzhi Chen, Chencui Huang, Hongxia Cheng, Ximing Wang, Xinya Zhao

DOI: 10.1186/s12967-024-05393-3

摘要

增殖性肝细胞癌（HCC）是一类具有侵袭性且预后较差的肿瘤。我们旨在构建一种基于CT的放射组学列线图，以预测增殖性HCC，区分临床结局，并探索肿瘤微环境。回顾性收集了两家医疗中心的经病理诊断的肝细胞癌患者资料，所有患者在肝切除术后确诊。利用训练队列（n=184）建立了结合放射组学模型和临床放射学特征的CT放射组学列线图，用于预测增殖性HCC，并在内部测试队列（n=80）和外部测试队列（n=89）中验证其性能。通过RNA测序数据和癌症影像档案数据库（TCIA）中的组织切片进行转录组学和组织学分析。放射组学列线图在训练、内部测试和外部测试队列中的受试者工作特征曲线下面积（AUC）分别为0.84、0.87和0.85。该列线图能区分手术后早期无复发生存（HR=2.25，P<0.001）和TACE治疗后无进展生存（HR=2.21，P=0.03）。转录组学和组织学分析显示，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。该CT放射组学列线图能够预测增殖性HCC，区分临床结局，并反映促肿瘤微环境。

Abstract

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.