Han Guoda, Liu Xu, Gao Tian, Zhang Lei, Zhang Xiaoling, Wei Xiaonan, Lin Yecheng, Yin Bohong
First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China.
First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China.
Mol Cell Probes. 2024 Dec;78:101983. doi: 10.1016/j.mcp.2024.101983. Epub 2024 Sep 30.
In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics.
Transcriptome data, pathological images, and clinical data from 443 cases were retrieved from TCGA (The Cancer Genome Atlas Program) for survival analysis. The images were segmented using the Otsu algorithm, and features were extracted using the PyRadiomics package. Subsequently, the cases were randomly divided into a training cohort of 165 cases and a validation cohort of 69 cases. Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves. Additionally, the correlation between the Pathomics score (PS) and immune genes was examined.
In the multivariate analysis, heightened infiltration of activated CD4 memory T cells was strongly associated with improved overall survival (HR = 0.505, 95 % CI = 0.342-0.745, P < 0.001). The pathomic model, exhibiting robust predictive capability, demonstrated impressive AUC values of 0.844 and 0.750 in both study cohorts. The Decision Curve Analysis (DCA) unequivocally underscored the model's exceptional clinical utility. In a subsequent multivariate analysis, heightened infiltration of the PS also emerged as a significant protective factor for overall survival (HR = 0.506, 95 % CI = 0.329-0.777, P = 0.002).
The pathomic model based on H&E slides for predicting the infiltration degree of activated CD4 memory T cells, along with integrated bioinformatics analysis elucidating potential molecular mechanisms, offers novel prognostic indicators for the precise stratification and individualized prognosis of gastric cancer patients.
在本研究中，我们旨在开发一种基于苏木精-伊红（H&E）染色结果并结合机器学习病理组学的胃癌精准预测模型。
从癌症基因组图谱（TCGA）项目中获取443例病例的转录组数据、病理图像和临床数据用于生存分析。使用大津算法对图像进行分割，并使用PyRadiomics软件包提取特征。随后，将病例随机分为165例的训练队列和69例的验证队列。通过最小冗余-最大相关（mRMR）-递归特征消除（RFE）筛选选择的特征用于使用梯度提升机（GBM）算法训练模型。使用受试者操作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线评估模型的性能。此外，还研究了病理组学评分（PS）与免疫基因之间的相关性。
在多变量分析中，活化的CD4记忆T细胞浸润增加与总生存期改善密切相关（HR = 0.505，95%CI = 0.342 - 0.745，P < 0.001）。病理组学模型具有强大的预测能力，在两个研究队列中均表现出令人印象深刻的AUC值，分别为0.844和0.750。决策曲线分析（DCA）明确强调了该模型卓越的临床实用性。在随后的多变量分析中，PS浸润增加也成为总生存期的一个重要保护因素（HR = 0.506，95%CI = 0.329 - 0.777，P = 0.002）。
基于H&E切片预测活化CD4记忆T细胞浸润程度的病理组学模型，以及阐明潜在分子机制的综合生物信息学分析，为胃癌患者的精准分层和个体化预后提供了新的预后指标。
