Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

作者：Yibo Zhang, Zijian Yang, Ruanqi Chen, Yanli Zhu, Li Liu, Jiyan Dong, Zicheng Zhang, Xujie Sun, Jianming Ying, Dongmei Lin, Lin Yang, Meng Zhou · 发表于：npj Digital Medicine · 年份：2024 · DOI：10.1038/s41746-024-01003-0 · 被引用次数：128 · 研究领域：Lung Cancer Research Studies、AI in cancer detection、Radiomics and Machine Learning in Medical Imaging

Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patients who may benefit from postoperative or preoperative chemoradiotherapy. The predictive power of PathoSig was validated in independent multicenter cohorts. Furthermore, PathoSig can provide comprehensive prognostic information beyond the current TNM staging system and molecular subtyping. Overall, our study highlights the significant potential of utilizing histopathology images-based deep learning in improving prognostic predictions and evaluating therapeutic response in SCLC. PathoSig represents an effective tool that aids clinicians in making informed decisions and selecting personalized treatment strategies for SCLC patients.