基于 F-FDG PET/CT 的生境放射组学结合堆叠集成学习预测肝细胞癌预后的多中心研究。

F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

机构信息

Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.

National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.

出版信息

BMC Cancer. 2024 Nov 27;24(1):1457. doi: 10.1186/s12885-024-13206-5.

DOI:10.1186/s12885-024-13206-5

PMID:39604895

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11600565/

Abstract

BACKGROUND

This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in F-FDG PET/CT images.

METHODS

A total of 137 HCC patients from two institutions were retrospectively included. First, intratumoral habitats were achieved by a two-step unsupervised clustering process based on k-means clustering. Second, a total of 4032 radiomic features were extracted based on each habitat, including 2016 PET-based and 2016 CT-based radiomic features. Then, after feature selection, the stacking ensemble learning approach which combined six machine learning classifiers as the first-level learners with Cox proportional hazards regression as the second-level learner, was employed to build multiple radiomic models. Finally, the optimal model was selected based on the calculation of the C-index, and a combined model integrating with a clinical model was also constructed to identify the potentially complementary effect.

RESULTS

Three spatially distinct habitats were identified in the two cohorts. Among a total of 30 stacking ensemble learning models established based on different combinations of 5 types of segmented volumes of interest (VOIs) with 6 types of classifiers, the MLP-Cox-habitat-2 model was selected as the optimal radiomic model with a C-index of 0.702 in the external validation cohort. Furthermore, the combined model integrating the optimal radiomic model with the clinical model achieved an improved C-index of 0.747. Consistently, the combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.835, 0.828, and 0.800 in the 1-year, 2-year, and 3-year OS, respectively.

CONCLUSION

F-FDG PET/CT-based habitat radiomics outperformed traditional radiomics in OS prediction for HCC, with a further improved predictive power by integrating with the clinical model. The optimal combined habitat model was potentially promising in guiding individualized treatment for HCC.

TRIAL REGISTRATION

This study was a retrospective study, so it was free from registration.

摘要

背景

本研究旨在基于 F-FDG PET/CT 图像中反映的肿瘤内异质性特征，开发预测肝细胞癌（HCC）总生存率（OS）的基于肿瘤栖息地的放射组学模型。

方法

共纳入来自两个机构的 137 例 HCC 患者进行回顾性分析。首先，基于 k-均值聚类的两步无监督聚类过程获得肿瘤内栖息地。其次，基于每个栖息地提取了总共 4032 个放射组学特征，包括 2016 个基于 PET 的和 2016 个基于 CT 的放射组学特征。然后，在特征选择后，采用结合了六个机器学习分类器作为第一层学习者，Cox 比例风险回归作为第二层学习者的堆叠集成学习方法构建多个放射组学模型。最后，基于计算 C 指数选择最优模型，并构建结合临床模型的联合模型以识别潜在的互补效应。

结果

在两个队列中识别出三个具有明显空间差异的栖息地。在基于 5 种分割感兴趣区（VOI）类型和 6 种分类器的不同组合建立的总共 30 个堆叠集成学习模型中，MLP-Cox-habitat-2 模型被选为最优放射组学模型，在外部验证队列中的 C 指数为 0.702。此外，将最优放射组学模型与临床模型相结合的联合模型达到了 0.747 的改善 C 指数。一致地，联合模型在 OS 预测方面优于其他模型，在 1 年、2 年和 3 年 OS 时的时间依赖性 AUC 分别为 0.835、0.828 和 0.800。