Huang Xinyi, Zheng Shuang, Li Shuqi, Huang Yu, Zhang Wenhui, Liu Fang, Cao Qinghua
Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; Department of Pathology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
Am J Pathol. 2025 Mar;195(3):561-574. doi: 10.1016/j.ajpath.2024.12.005. Epub 2024 Dec 31.
Angiopoietin-2 (ANGPT2) shows promise as prognostic marker and therapeutic target in hepatocellular carcinoma (HCC). However, assessing ANGPT2 expression and prognostic potential using histopathology images viewed with naked eye is challenging. Herein, machine learning was employed to develop a pathomics model for analyzing histopathology images to predict ANGPT2 status. HCC cases obtained from The Cancer Genome Atlas (TCGA-HCC; n = 267) were randomly assigned to the training or testing set, and cases from a single center were employed as a validation set (n = 91). In the TCGA-HCC cohort, the group with high ANGPT2 expression had a significantly lower overall survival compared with the group with low ANGPT2. Histopathologic features in the training set were extracted, screened, and incorporated into a gradient-boosting machine model that generated a pathomics score, which successfully predicted ANGPT2 expression in the three data sets and showed remarkable risk stratification for overall survival in both the TCGA-HCC (P < 0.0001) and single-center cohorts (P = 0.001). Multivariate analysis suggested that the pathomics score could serve as a predictor of prognosis (P < 0.001). Bioinformatics analysis illustrated a distinction in tumor growth and development related gene-enriched pathways, vascular endothelial growth factor-related gene expression, and immune cell infiltration between high and low pathomics scores. This study indicates that the use of histopathology image features can enhance the prediction of molecular status and prognosis in HCC. The integration of image features with machine learning may improve prognosis prediction in HCC.
血管生成素-2（ANGPT2）有望成为肝细胞癌（HCC）的预后标志物和治疗靶点。然而，使用肉眼观察组织病理学图像来评估ANGPT2表达和预后潜力具有挑战性。在此，采用机器学习开发了一种病理组学模型，用于分析组织病理学图像以预测ANGPT2状态。从癌症基因组图谱（TCGA-HCC；n = 267）获得的HCC病例被随机分配到训练集或测试集，并将来自单一中心的病例用作验证集（n = 91）。在TCGA-HCC队列中，ANGPT2高表达组的总生存期明显低于ANGPT2低表达组。提取、筛选训练集中的组织病理学特征，并将其纳入梯度提升机模型，该模型生成一个病理组学评分，该评分成功预测了三个数据集中的ANGPT2表达，并在TCGA-HCC（P < 0.0001）和单中心队列（P = 0.001）中均显示出显著的总生存期风险分层。多变量分析表明，病理组学评分可作为预后的预测指标（P < 0.001）。生物信息学分析表明，高、低病理组学评分在肿瘤生长和发育相关基因富集途径、血管内皮生长因子相关基因表达以及免疫细胞浸润方面存在差异。本研究表明，使用组织病理学图像特征可以增强对HCC分子状态和预后的预测。图像特征与机器学习的整合可能会改善HCC的预后预测。
