ArticleThe journal of pathology. Clinical research2022
Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.
Article in The journal of pathology. Clinical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Pooled it
- Review
- Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer.Cancer immunology, immunotherapy : CII · 2026Article
- Engagement-quality mismatch in robot-assisted total knee arthroplasty short videos on TikTok and Bilibili.Journal of robotic surgery · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- TILseg: Automated Whole Slide-Level Spatial Scoring of Tumor-Infiltrating Lymphocytes Reveals Prognostic Patterns in Triple Negative Breast Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Self-identified African ancestry and pathomics-derived tumor-infiltrating lymphocyte scores in colon and rectal adenocarcinoma whole-slide images: an exploratory multicohort whole-slide image study.Frontiers in oncology · 2026Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Prognostic Value of Deep Learning-Extracted Tumor-Infiltrating Lymphocytes in Esophageal Cancer: A Multicenter Retrospective Cohort Study.Cancer medicine · 2025Article
- Statistical analysis of spatial patterns in tumor microenvironment images.Nature communications · 2025Article
- Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning.NPJ precision oncology · 2025Article
- Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer.Journal of translational medicine · 2025Article
- Advanced deep learning for multi-class colorectal cancer histopathology: integrating transfer learning and ensemble methods.Quantitative imaging in medicine and surgery · 2025Article
- Computational pathology applied to clinical colorectal cancer cohorts identifies immune and endothelial cell spatial patterns predictive of outcome.The Journal of pathology · 2025Article
- Single-cell spatial immune profiling for precision immunotherapy in Lynch syndrome.Journal of the National Cancer Center · 2025Article
- Article
- Peri-tumoural lymphocyte neighbourhoods predict longer survival in pancreatic ductal adenocarcinoma.Frontiers in immunology · 2025Article
- Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer.Communications medicine · 2024Article
- T cell receptor clonotype in tumor microenvironment contributes to intratumoral signaling network in patients with colorectal cancer.Immunologic research · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0-3 by the Klintrup-Mäkinen (KM) grading method and this was compared to DL approaches. Inter-rater agreement for TILs was measured using Cohen's kappa coefficient. On multivariate analysis of spatial TIL features derived by DL approaches and clinicopathological variables including tumor stage, microsatellite instability, and KRAS mutation, TIL densities within 200 μm of the IM (f_im200) remained the most significant prognostic factor for progression-free survival (PFS) (hazard ratio [HR] 0.004 [95% confidence interval, CI, 0.0001-0.15], p = 0.0028) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031 [95% CI 0.001-0.645], p = 0.024). Inter-rater agreement of manual KM grading was insignificant in the Yonsei (κ = 0.109) and the TCGA (κ = 0.121) cohorts. The survival analysis based on KM grading showed statistically significant different PFS in the TCGA cohort, but not the Yonsei cohort. Automatic quantification of TILs at the IM based on DL approaches shows prognostic utility to predict PFS, and could provide robust and reproducible TIL density measurement in patients with CRC.
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