ArticleFrontiers in immunology2022
Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.Thoracic cancer · 2025Pooled it
- Clinical significance of CD161Journal for immunotherapy of cancer · 2026Article
- Associations between CT Radiomics Analyses and Hematopoietic Cell Mobilization in Patients with Multiple Myeloma: An Exploratory Analysis.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026Article
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- Early prediction of immune checkpoint inhibitor-related pneumonitis in advanced non-small cell lung cancer based on primary tumor delta-radiomics features.Frontiers in immunology · 2026Article
- Radiomics and deep learning for immune checkpoint inhibitor outcomes: imaging-derived immune phenotyping and clinical translation across solid tumors.Frontiers in immunology · 2026Review
- Interpretable machine learning model integrating CT radiomics, CTR, and clinical features for EGFR mutation prediction in ≤3 cm lung adenocarcinoma nodules.Annals of medicine · 2025Article
- Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy.Journal of translational medicine · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Different MRI-based radiomics machine learning models to predict CD3+ tumor-infiltrating lymphocytes in rectal cancer.Frontiers in oncology · 2025Article
- The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.Human vaccines & immunotherapeutics · 2024Article
- Combinatorial leaky probiotic for anticancer immunopotentiation and tumor eradication.Cell reports. Medicine · 2024Article
- Applications of CT-based radiomics for the prediction of immune checkpoint markers and immunotherapeutic outcomes in non-small cell lung cancer.Frontiers in immunology · 2024Review
- The global trends and distribution in tumor-infiltrating lymphocytes over the past 49 years: bibliometric and visualized analysis.Frontiers in immunology · 2024Article
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- Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside.Frontiers in oncology · 2023Article
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Authors and funding
10 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Enrichment of tumor-infiltrating lymphocytes (TIL) in the tumor microenvironment (TME) is a reliable biomarker of immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC). Phenotyping through computed tomography (CT) radiomics has the overcome the limitations of tissue-based assessment, including for TIL analysis. Here, we assess TIL enrichment objectively using an artificial intelligence-powered TIL analysis in hematoxylin and eosin (H&E) image and analyze its association with quantitative radiomic features (RFs). Clinical significance of the selected RFs is then validated in the independent NSCLC patients who received ICI. Methods: In the training cohort containing both tumor tissue samples and corresponding CT images obtained within 1 month, we extracted 86 RFs from the CT images. The TIL enrichment score (TILes) was defined as the fraction of tissue area with high intra-tumoral or stromal TIL density divided by the whole TME area, as measured on an H&E slide. From the corresponding CT images, the least absolute shrinkage and selection operator model was then developed using features that were significantly associated with TIL enrichment. The CT model was applied to CT images from the validation cohort, which included NSCLC patients who received ICI monotherapy. Results: A total of 220 NSCLC samples were included in the training cohort. After filtering the RFs, two features, gray level variance (coefficient 1.71 x 10 Conclusions: In this CT radiomics model, predicted TILes was significantly associated with ICI outcomes in NSCLC patients. Analyzing TME through radiomics may overcome the limitations of tissue-based analysis and assist clinical decisions regarding ICI.
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