Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study

IF 5 2区 医学 Q2 Medicine Translational Oncology Pub Date : 2025-02-01 DOI:10.1016/j.tranon.2024.102260
Kun Chen , Chunxiao Sui , Ziyang Wang , Zifan Liu , Lisha Qi , Xiaofeng Li
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Abstract

Background and objective

Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).

Methods

A total of 232 HCC patients were retrospectively included, including a training/validation cohort and two external testing cohorts from 4 centers. For habitat radiomics, intratumoral habitat partitioning based on CT images was first performed by using Otsu thresholding method. Second, a total of 350 habitat radiomic models were constructed to select the optimal model. Then, both ROC curve analyses and Kaplan-Meier survival curve analyses were applied to assess the predictive performances. Ultimately, an immune status profiling was conducted based on bioinformatic analyses and multiplex immunohistochemistry (mIHC) assays to reveal the potential mechanisms.

Results

A total of 4 habitats were segmented, and the corresponding habitat radiomic models were constructed based on each habitat and an integration of all the four habitats. Generally, habitat radiomic models outperformed traditional radiomic models in stratifying prognosis for HCC. The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. Distinct resting natural killer (NK) cell infiltrations significantly contributed to the prognosis stratification of HCC by the optimal habitat radiomic model.

Conclusions

The habitat radiomic model based on CT images was potentially predictive of overall survival for HCC, with a superiority over the traditional radiomic model. The prognostic power of the habitat radiomic model was partly attributed to the distinct immune status captured in the CT images.
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基于CT图像的栖息地放射组学预测肝细胞癌患者的生存和免疫状态,一项多队列验证研究。
背景和目的:虽然一些临床病理特征被确定为预后指标,但潜在的预后放射学模型有望在术前和无创地预测HCC的生存。传统的放射学模型缺乏对肿瘤内区域异质性的考虑。本研究旨在建立并验证多生境放射组学模型对肝细胞癌(HCC)预后的预测能力。方法:回顾性纳入共232例HCC患者,包括来自4个中心的培训/验证队列和两个外部测试队列。对于栖息地放射组学,首先采用Otsu阈值法对CT图像进行肿瘤内栖息地划分。其次,构建了350个生境辐射组模型,优选出最优模型;然后应用ROC曲线分析和Kaplan-Meier生存曲线分析评估预测效果。最后,基于生物信息学分析和多重免疫组织化学(mIHC)分析进行了免疫状态分析,以揭示潜在的机制。结果:共对4个生境进行了分割,并以每个生境为基础,综合4个生境构建了相应的生境放射组学模型。总体而言,生境放射组学模型在HCC预后分层方面优于传统放射组学模型。基于决策树(DT)筛选和随机森林(RF)分类器的生境辐射组学模型为最优模型,AUCmean为0.806。最佳栖息地放射学模型显示不同的静息NK细胞浸润对HCC预后分层有显著影响。结论:基于CT影像的生境放射组学模型与传统放射组学模型相比,具有预测HCC总生存期的优势。栖息地放射组学模型的预后能力部分归因于CT图像中捕获的独特免疫状态。
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来源期刊
CiteScore
8.40
自引率
2.00%
发文量
314
审稿时长
54 days
期刊介绍: Translational Oncology publishes the results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of oncology patients. Translational Oncology will publish laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer. Peer reviewed manuscript types include Original Reports, Reviews and Editorials.
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