ArticleTranslational lung cancer research2021
Artificial intelligence-based analysis for immunohistochemistry staining of immune checkpoints to predict resected non-small cell lung cancer survival and relapse.
Article in Translational lung cancer research, 2021. 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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15 citing papers in PubMed, 22 citations in OpenAlex.
- Trial
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Automatic segmentation of clear cell renal cell carcinoma based on deep learning and a preliminary exploration of the tumor microenvironment.Translational andrology and urology · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Value analysis of preoperative peripheral blood LMR in predicting prognosis of serous papillary ovarian adenocarcinoma.Discover oncology · 2024Article
- The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis.Frontiers in oncology · 2024Article
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
- Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies.Cancers · 2023Review
- Review
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Biological informed graph neural network for tumor mutation burden prediction and immunotherapy-related pathway analysis in gastric cancer.Computational and structural biotechnology journal · 2023Article
- Artificial intelligence in lung cancer: Application and future thinking.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer.Frontiers in radiology · 2022Review
- Low peripheral blood derived neutrophil-to-lymphocyte ratio (dNLR) is associated with increased tumor T-cell infiltration and favorable outcomes to first-line pembrolizumab in non-small cell lung cancer.Journal for immunotherapy of cancer · 2021Article
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConventional analysis of single-plex chromogenic immunohistochemistry (IHC) focused on quantitative but spatial analysis. How immune checkpoints localization related to non-small cell lung cancer (NSCLC) prognosis remained unclear.
methodsHere, we analyzed ten immune checkpoints on 1,859 tumor microarrays (TMAs) from 121 NSCLC patients and recruited an external cohort of 30 NSCLC patients with 214 whole-slide IHC. EfficientUnet was applied to segment tumor cells (TCs) and tumor-infiltrating lymphocytes (TILs), while ResNet was performed to extract prognostic features from IHC images.
resultsThe features of galectin-9, OX40, OX40L, KIR2D, and KIR3D played an un-negatable contribution to overall survival (OS) and relapse-free survival (RFS) in the internal cohort, validated in public databases (GEPIA, HPA, and STRING). The IC-Score and Res-Score were two predictive models established by EfficientUnet and ResNet. Based on the IC-Score, Res-Score, and clinical features, the integrated score presented the highest AUC for OS and RFS, which could achieve 0.9 and 0.85 in the internal testing cohort. The robustness of Res-Score was validated in the external cohort (AUC: 0.80-0.87 for OS, and 0.83-0.94 for RFS). Additionally, the neutrophil-to-lymphocyte ratio (NLR) combined with the PD-1/PD-L1 signature established by EfficientUnet can be a predictor for RFS in the external cohort.
conclusionsOverall, we established a reliable model to risk-stratify relapse and death in NSCLC with a generalization ability, which provided a convenient approach to spatial analysis of single-plex chromogenic IHC.
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