ArticleEBioMedicine2021
A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer.
Article in EBioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review.BMC cancer · 2024Pooled it
- Prognostic value of visually and computationally assessed tumor-infiltrating lymphocytes in early-stage triple-negative breast cancer (TBCRC-030).Journal of the National Cancer Institute · 2026Trial
- Current and future therapies for triple-negative breast cancer.Journal of hematology & oncology · 2026Review
- Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challenge.Nature communications · 2026Article
- QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.Research square · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- Cystic lesions and their role in pancreatic cancer risk stratification.Translational oncology · 2026Review
- Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study.EClinicalMedicine · 2025Article
- Article
- In triple-negative breast cancer, fibrotic focus, the mitotic activity index and tumour-infiltrating lymphocytes have independent prognostic value: an observational population-based cohort study with very long follow-up.Journal of clinical pathology · 2025Observational
- Immuno-Oncology at the Crossroads: Confronting Challenges in the Quest for Effective Cancer Therapies.International journal of molecular sciences · 2025Review
- Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.Breast cancer research : BCR · 2025Article
- Article
- Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration.Diagnostics (Basel, Switzerland) · 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
- Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations.Journal of pathology informatics · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Spatial transcriptomics reveals substantial heterogeneity in triple-negative breast cancer with potential clinical implications.Nature communications · 2024Article
- Emerging measurements for tumor-infiltrating lymphocytes in breast cancer.Japanese journal of clinical oncology · 2024Review
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTumor-infiltrating lymphocytes (TILs) are clinically significant in triple-negative breast cancer (TNBC). Although a standardized methodology for visual TILs assessment (VTA) exists, it has several inherent limitations. We established a deep learning-based computational TIL assessment (CTA) method broadly following VTA guideline and compared it with VTA for TNBC to determine the prognostic value of the CTA and a reasonable CTA workflow for clinical practice.
methodsWe trained three deep neural networks for nuclei segmentation, nuclei classification and necrosis classification to establish a CTA workflow. The automatic TIL (aTIL) score generated was compared with manual TIL (mTIL) scores provided by three pathologists in an Asian (n = 184) and a Caucasian (n = 117) TNBC cohort to evaluate scoring concordance and prognostic value.
findingsThe intraclass correlations (ICCs) between aTILs and mTILs varied from 0.40 to 0.70 in two cohorts. Multivariate Cox proportional hazards analysis revealed that the aTIL score was associated with disease free survival (DFS) in both cohorts, as either a continuous [hazard ratio (HR)=0.96, 95% CI 0.94-0.99] or dichotomous variable (HR=0.29, 95% CI 0.12-0.72). A higher C-index was observed in a composite mTIL/aTIL three-tier stratification model than in the dichotomous model, using either mTILs or aTILs alone.
interpretationThe current study provides a useful tool for stromal TIL assessment and prognosis evaluation for patients with TNBC. A workflow integrating both VTA and CTA may aid pathologists in performing risk management and decision-making tasks.
fundingNational Natural Science Foundation of China, Guangdong Medical Research Foundation, Guangdong Natural Science Foundation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.