ArticleiScience2022
Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.NPJ precision oncology · 2025Article
- Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning.NPJ precision oncology · 2025Article
- Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma.Journal for immunotherapy of cancer · 2025Article
- The HOX Gene Family's Role as Prognostic and Diagnostic Biomarkers in Hematological and Solid Tumors.Cancers · 2025Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Article
- Disulfidptosis‑related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma.Oncology letters · 2024Article
- Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma.Heliyon · 2024Article
- Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma.iScience · 2023Article
- Review
- Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review.Frontiers in oncology · 2023Review
- P-MES: Explainable pathology-based distant metastasis risk stratification in locally advanced nasopharyngeal carcinoma.Digital healthArticle
Corrections and comments
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Authors and funding
19 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.
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