Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC10796367 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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

Zhang Yibo Y Yang Zijian Z Chen Ruanqi R Zhu Yanli Y Liu Li L Dong Jiyan J Zhang Zicheng Z Sun Xujie X Ying Jianming J Lin Dongmei D Yang Lin L Zhou Meng M

NPJ digital medicine 20240118 1

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 pathomi ...[more]

PMID: 38238410

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Dataset Information

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

Publications

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

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