Li Fengling, Wei Yani, Zhang Wenchuan, Zhao Yuanyuan, Fu Jing, Xiao Xiuli, Qiu Yan, Yi Yuhao, Yang Yongquan, Bu Hong
Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China.
NPJ Precis Oncol. 2025 Jul 15;9(1):239. doi: 10.1038/s41698-025-01033-1.
Breast cancer patients exhibit variable responses to neoadjuvant therapy (NAT), necessitating robust predictive biomarkers. We developed an artificial intelligence (AI)-driven integrated predictive model (IPM) combining histopathological, clinical, and immune features to address this challenge. Using whole-slide images from 1035 patients across four centers, we compared tumor epithelium (TE-score), stroma (TS-score), and whole-tumor (TR-score) deep learning biomarkers, identifying TR-score as optimal (AUC = 0.729 vs. 0.686/0.719 for TE/TS-scores). The IPM, incorporating TR-score and clinical variables, demonstrated superior NAT response prediction versus clinicopathological models (validation AUC = 0.780 vs. 0.706, p < 0.001), with 10% higher accuracy. Immune profiling further enhanced performance (AUC = 0.831 vs. 0.822, p = 0.183). These results establish the biological and clinical validity of TR-score for characterizing tumor-stroma interactions, with IPM providing a generalizable framework for precision oncology. The model's stability across multicenter cohorts (AUCs 0.781-0.816) and incremental value of immune data suggest its utility in guiding NAT decision-making and trial stratification.
乳腺癌患者对新辅助治疗（NAT）表现出不同的反应，因此需要强大的预测生物标志物。我们开发了一种人工智能（AI）驱动的综合预测模型（IPM），该模型结合了组织病理学、临床和免疫特征来应对这一挑战。我们使用来自四个中心的1035名患者的全切片图像，比较了肿瘤上皮（TE评分）、间质（TS评分）和全肿瘤（TR评分）的深度学习生物标志物，确定TR评分为最佳（TE/TS评分的AUC分别为0.729 vs. 0.686/0.719）。包含TR评分和临床变量的IPM在预测NAT反应方面优于临床病理模型（验证AUC = 0.780 vs. 0.706，p < 0.001），准确率高10%。免疫分析进一步提高了性能（AUC = 0.831 vs. 0.822，p = 0.183）。这些结果确立了TR评分在表征肿瘤-间质相互作用方面的生物学和临床有效性，IPM为精准肿瘤学提供了一个可推广的框架。该模型在多中心队列中的稳定性（AUC为0.781 - 0.816）以及免疫数据的增量价值表明其在指导NAT决策和试验分层方面的实用性。
