基于CT的放射线学列图预测增殖性肝细胞癌并探索肿瘤微环境
CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment
影响因子:7.50000
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
摘要
增殖性肝细胞癌（HCCS）是一类侵略性肿瘤，预后不良。我们旨在构建基于计算机断层扫描（CT）基于基于的放射素学词法图，以预测增生性HCC，对临床结局进行分层并探索肿瘤微环境。从两个医疗中心回顾了肝切除术后病理诊断的HCC患者。使用训练队列（n = 184）构建了基于CT基于CT的放射线图，该图纳入了放射线学模型和临床原理特征，以预测增生性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。评估了接受手术（n = 201）或接受过跨性化学栓塞的HCC患者（TACE，n = 104）的HCC患者的临床结局的预测性能。来自癌症成像存档数据库的RNA测序数据和组织学组织载玻片用于执行转录组学和病原体分析。在训练，内部测试组中，放射线图的接收器工作特征命名的区域在训练，内部测试和外部测试群中，放射线图的工作特征曲线预测增殖HCC为0.84、0.87和0.85。放射学nom图可以在手术结果队列中对早期无复发的生存分层（危险比[HR] = 2.25; P <0.001）和TACE结果组中的无进展生存（HR = 2.21; P = 0.03）。转录组学和病原体分析表明，放射素学词法图与碳代谢，免疫细胞浸润，TP53突变和肿瘤细胞的异质性有关。基于CT的放射线图命名函数图可以预测增殖性HCC，分层临床临床过滤术和测量亲肿瘤的微观微观条件。
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.