ArticleJournal for immunotherapy of cancer2024
Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer.
Article in Journal for immunotherapy of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Consolidated Evidence and New Frontiers of Liquid Biopsy in Lung Cancer: A Narrative Review.Cells · 2026Review
- Integrating WGCNA and machine learning algorithm to identify ACSM5 as a prognostic biomarker and therapeutic target for predicting immunotherapy efficacy in non-small cell lung cancer.Translational cancer research · 2026Article
- Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.Frontiers in artificial intelligence · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Advancing prognostic and therapeutic prediction in lung squamous cell carcinoma through integrated multi-omics analysis and 117 machine learning combinations.Journal of thoracic disease · 2025Article
- Emerging Techniques of Translational Research in Immuno-Oncology: A Focus on Non-Small Cell Lung Cancer.Cancers · 2025Review
- Development and validation of machine learning models based on molecular features for estimating the probability of multiple primary lung carcinoma versus intrapulmonary metastasis in patients presenting multiple non-small cell lung cancers.Translational lung cancer research · 2025Article
- Article
- A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients.Cancer immunology, immunotherapy : CII · 2024Article
- The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition.NPJ precision oncology · 2024Review
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
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20 authors.
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Abstract
backgroundIn small-cell lung cancer (SCLC), the tumor immune microenvironment (TIME) could be a promising biomarker for immunotherapy, but objectively evaluating TIME remains challenging. Hence, we aimed to develop a predictive biomarker of immunotherapy efficacy through a machine learning analysis of the TIME.
methodsWe conducted a biomarker analysis in a prospective study of patients with extensive-stage SCLC who received chemoimmunotherapy as the first-line treatment. We trained a model to predict 1-year progression-free survival (PFS) using pathological images (H&E, programmed cell death-ligand 1 (PD-L1), and double immunohistochemical assay (cluster of differentiation 8 (CD8) and forkhead box P3 (FoxP3)) and patient information. The primary outcome was the mean area under the curve (AUC) of machine learning models in predicting the 1-year PFS.
resultsWe analyzed 100,544 patches of pathological images from 78 patients. The mean AUC values of patient information, pathological image, and combined models were 0.789 (range 0.571-0.982), 0.782 (range 0.750-0.911), and 0.868 (range 0.786-0.929), respectively. The PFS was longer in the high efficacy group than in the low efficacy group in all three models (patient information model, HR 0.468, 95% CI 0.287 to 0.762; pathological image model, HR 0.334, 95% CI 0.117 to 0.628; combined model, HR 0.353, 95% CI 0.195 to 0.637). The machine learning analysis of the TIME had better accuracy than the human count evaluations (AUC of human count, CD8-positive lymphocyte: 0.681, FoxP3-positive lymphocytes: 0.626, PD-L1 score: 0.567).
conclusionsThe spatial analysis of the TIME using machine learning predicted the immunotherapy efficacy in patients with SCLC, thus supporting its role as an immunotherapy biomarker.
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