ArticleNPJ breast cancer2023
Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.
Article in NPJ breast cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review.BMC cancer · 2024Pooled it
- Thematic trends and knowledge-map of tumor-infiltrating lymphocytes in breast cancer: a scientometric analysis.Frontiers in oncology · 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
- From cell counts to cellular interactions: Cu-Cyto and the co-localization index as a spatial framework for the tumor immune microenvironment of rectal cancer.International journal of clinical oncology · 2026Review
- Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.The Lancet. Oncology · 2026Article
- Comprehensive molecular profiling of combined hepatocellular carcinoma and cholangiocarcinoma reveals distinct Notch signaling subgroups with prognostic significance.Virchows Archiv : an international journal of pathology · 2026Article
- Review
- MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.Journal of the National Cancer Center · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- Tumour-infiltrating lymphocytes do not predict survival in human papillomavirus-associated oropharyngeal squamous cell carcinoma: a single institution retrospective review.The Journal of laryngology and otology · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- AI-driven spatial analysis of tumor-infiltrating lymphocytes predicts chemo-immunotherapy response in triple-negative breast cancer.Frontiers in immunology · 2026Article
- Prediction of Pathological Complete Response after Chemoradiation for Locally Advanced Rectal Cancer Using Machine Learning with Clinical Features, the INTERCEPTOR Study Preliminary Model.Journal of the anus, rectum and colon · 2026Article
- Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps.Analytical cellular pathology (Amsterdam) · 2026Review
- A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology images.Cancer letters · 2026Article
- Translational deep learning models for risk stratification to predict prognosis and immunotherapy response in gastric cancer using digital pathology.Journal of translational medicine · 2025Article
- Assessment of Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer: Interobserver Variability and Contributing Factors.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.Scientific reports · 2025Article
Corrections and comments
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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
Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.
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Registered trials
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.