ArticleNPJ digital medicine2024
Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 2 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
- Artificial intelligence and radiomics in small cell lung cancer: a narrative review of imaging and multisource data integration.Journal of thoracic disease · 2026Review
- Multimodal radiopathomics model predicts postoperative metachronous liver metastasis in gastric cancer.Nature communications · 2026Article
- Association of Epithelial-to-Mesenchymal Transition Related Markers With Clinical Outcome in Non-Muscle Invasive and Muscle Invasive Bladder Cancer Patients.Cell biochemistry and biophysics · 2026Article
- Article
- BraMARS: An Interpretable Histopathology-Driven Deep Learning Model for Brain Metastasis Risk Stratification in Surgically Resected Limited-Stage SCLC.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.Scientific reports · 2026Article
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- Development of a Prediction Model for Progression Risk in High-Grade Gliomas Based on Habitat Radiomics and Pathomics.Annals of clinical and translational neurology · 2026Article
- Swarm intelligence-guided ROI selection for deep learning assessment of HER2 in colorectal cancer.Scientific reports · 2026Article
- Meta-encoder: a unified integration framework for multiple pathological foundation models in cancer detection.Nature communications · 2026Article
- Graphicalized vision-language modeling for comprehensive lung nodule analysis and risk stratification.NPJ digital medicine · 2026Article
- Review
- Diagnostic accuracy of an AI-based pathologic response assessment in locally advanced non-small cell lung cancer after neoadjuvant chemo-immunotherapy.BMC cancer · 2026Article
- Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.Biochemistry and biophysics reports · 2026Article
- Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.Journal of advanced research · 2026Article
- Prognostic model for pancreatic cancer based on machine learning of routine slides and transcriptomic tumor analysis.British journal of cancer · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Distinction between primary and metastatic mucinous ovarian carcinoma from histopathology images using deep learning.NPJ digital medicine · 2026Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.