ArticleJournal of translational medicine2022
Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Pathology-interpretable radiomic model for predicting clinical outcome in patients with osteosarcoma: a retrospective, multicenter study.BMC medicine · 2025Article
- Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms.Journal of clinical pathology · 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
- Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma.Heliyon · 2024Article
- Review
- A narrative review on perioperative systemic therapy in non-small cell lung cancer.Exploration of targeted anti-tumor therapy · 2024Review
- The artificial intelligence and machine learning in lung cancer immunotherapy.Journal of hematology & oncology · 2023Review
- Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning.Journal of cancer research and clinical oncology · 2023Article
- Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma.iScience · 2022Article
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Authors and funding
20 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed.
methodsWe performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3
resultsA discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33-0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36-0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685).
conclusionThis automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC.
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