Zheng Zongtai, Dai Fazhong, Liu Ji, Zhang Yongqiang, Wang Zhenwei, Wang Bangqi, Qiu Xiaofu
Department of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China.
Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
BMC Cancer. 2025 Feb 20;25(1):310. doi: 10.1186/s12885-025-13688-x.
Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subtypes having different prognostic and therapeutic outcomes. Traditional methods for molecular subtyping are often time-consuming and resource-intensive. This study aims to develop machine learning models using deep learning features from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) to predict basal and luminal subtypes in BLCA.
RNA sequencing data and clinical outcomes were downloaded from seven public BLCA databases, including TCGA, GEO datasets, and the IMvigor210C cohort, to assess the prognostic value of BLCA molecular subtypes. WSIs from TCGA were used to construct and validate the machine learning models, while WSIs from Shanghai Tenth People's Hospital (STPH) and The Affiliated Guangdong Second Provincial General Hospital of Jinan University (GD2H) were used as external validations. Deep learning models were trained to obtained tumor patches within WSIs. WSI level deep learning features were extracted from tumor patches based on the RetCCL model. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were developed using these features to classify basal and luminal subtypes.
Kaplan-Meier survival and prognostic meta-analyses showed that basal BLCA patients had significantly worse overall survival compared to luminal BLCA patients (hazard ratio = 1.47, 95% confidence interval: 1.25-1.73, P < 0.001). The LR model based on tumor patch features selected by Resnet50 model demonstrated superior performance, achieving an area under the curve (AUC) of 0.88 in the internal validation set, and 0.81 and 0.64 in the external validation sets from STPH and GD2H, respectively. This model outperformed both junior and senior pathologists in the differentiation of basal and luminal subtypes (AUC: 0.85, accuracy: 74%, sensitivity: 66%, specificity: 82%).
This study showed the efficacy of machine learning models in predicting the basal and luminal subtypes of BLCA based on the extraction of deep learning features from tumor patches in H&E-stained WSIs. The performance of the LR model suggests that the integration of AI tools into the diagnostic process could significantly enhance the accuracy of molecular subtyping, thereby potentially informing personalized treatment strategies for BLCA patients.
膀胱癌（BLCA）存在深刻的分子多样性，基底型和管腔型亚型具有不同的预后和治疗结果。传统的分子亚型分类方法通常耗时且资源密集。本研究旨在利用苏木精和伊红（H&E）染色的全切片图像（WSIs）中的深度学习特征开发机器学习模型，以预测BLCA中的基底型和管腔型亚型。
从七个公共BLCA数据库下载RNA测序数据和临床结果，包括TCGA、GEO数据集和IMvigor210C队列，以评估BLCA分子亚型的预后价值。来自TCGA的WSIs用于构建和验证机器学习模型，而来自上海第十人民医院（STPH）和暨南大学附属广东第二人民医院（GD2H）的WSIs用作外部验证。训练深度学习模型以在WSIs中获取肿瘤切片。基于RetCCL模型从肿瘤切片中提取WSI水平的深度学习特征。使用这些特征开发支持向量机（SVM）、随机森林（RF）和逻辑回归（LR）来对基底型和管腔型亚型进行分类。
Kaplan-Meier生存分析和预后荟萃分析表明，与管腔型BLCA患者相比，基底型BLCA患者的总生存期明显更差（风险比=1.4
