ArticleFrontiers in immunology2025
Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Efficacy and safety of neoadjuvant chemotherapy with immunotherapy versus chemotherapy alone in esophageal squamous cell carcinoma: a meta-analysis based on randomized controlled trials.Frontiers in immunology · 2026Pooled it
- A neutrophil-to-lymphocyte ratio-centered machine learning model for predicting immunotherapy response in esophageal squamous cell carcinoma patients.Journal of thoracic disease · 2026Article
- Human biomarker navigator.iMeta · 2026Review
- CT-based deep learning predicts immunotherapy response in esophageal squamous cell cancer patients.Journal of thoracic disease · 2026Article
- Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.Fundamental research · 2026Review
- AI-driven pathology in esophageal cancer: from early screening to precision prognostics.Frontiers in oncology · 2026Review
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- ProMMF_Kron: a multimodal deep learning model for immunotherapy response prediction in stomach adenocarcinoma.Frontiers in immunology · 2026Article
- A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer.Cell reports. Medicine · 2025Article
- A nomogram integrating machine learning-derived CT radiomics and clinical characteristics for prognostic assessment in patients with locally advanced esophageal squamous cell carcinoma treated with definitive chemoradiotherapy with or without immunotherapy.Journal of translational medicine · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Machine learning-based predictive model for immune checkpoint inhibitors response in gastrointestinal cancers.Frontiers in medicine · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who benefit from ICIs remains limited. Current biomarkers have assisted in identifying potential responders to immunotherapy, yet they all have inherent limitations. In this study, two ESCC cohorts were established from the Third Affiliated Hospital of Soochow University in China. One cohort included 220 patients with PD-L1 expression levels determined by immunohistochemistry, and the other cohort included 75 patients who underwent immunotherapy. For each patient in both cohorts, we curated multimodal data encompassing Hematoxylin and Eosin-stained pathology images, longitudinal computed tomography (CT) scans, and pertinent clinical variables. Next, we introduced a novel multimodal deep learning model that integrated pathological features, radiomic features, and clinical information to predict PD-L1 levels, immunotherapy response, and overall survival. Our model achieved an AUC value of 0.836 for PD-L1 biomarker prediction, and 0.809 for immunotherapy response prediction. Furthermore, our model also achieved an AUC value of 0.8 in predicting overall survival beyond one or three years. Our findings confirmed that the multimodal integration of pathological, radiomic, and clinical features offers a powerful means to predict PD-L1 biomarker levels and immunotherapy response in esophageal cancer.
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