ArticleClinical cancer research : an official journal of the American Association for Cancer Research2021
An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- Neoadjuvant palbociclib and endocrine therapy versus chemotherapy in ER + /HER2- breast cancer: a randomized phase II trial.Nature communications · 2026Trial
- Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.The Lancet. Oncology · 2026Article
- An Open Source Automated Tumor Infiltrating Lymphocytes Algorithm for Prognosis in Primary Small Bowel Adenocarcinoma Using Routine Hematoxylin and Eosin Stained Sections.Cancer medicine · 2026Article
- AI-assisted image analysis of tumor-infiltrating lymphocytes as a prognostic marker in chemotherapy-naïve luminal breast cancer.NPJ breast cancer · 2026Article
- Comprehensive comparison of homologous recombination deficiency predictors in early-stage triple-negative breast cancer.Breast cancer research : BCR · 2026Article
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Multidimensional MRI radiomics-based model predicts recurrence risk in triple-negative breast cancer.NPJ precision oncology · 2026Article
- 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
- Quantitative Assays for TROP2 Measurement in Breast Cancer and Comparison to H-Score.Applied immunohistochemistry & molecular morphology : AIMM · 2026Article
- The "Cold Tumor" to "Hot Tumor" transformation strategy for triple-negative breast cancer: from mechanism to clinical translation.Molecular and cellular biochemistry · 2026Review
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Focal hotspot and diffuse immune subtypes of tumor-infiltrating lymphocytes: AI-powered spatial clustering classification and its clinical relevance to HER2 expression in triple-negative breast cancer.Journal of translational medicine · 2025Article
- AI assessment of tumor-infiltrating lymphocytes on routine H&E-slides as a predictor of response to neoadjuvant therapy in breast cancer-a real-world study.Virchows Archiv : an international journal of pathology · 2025Article
- Prognostic significance of CD8+ T cell Spatial Biomarkers in ER+ and ER- breast cancer: A retrospective cohort study.PLoS medicine · 2025Article
- 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
- Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma.JAMA network open · 2025Article
- Quantitative Multiplex Immunofluorescence Assay for Trophoblast Cell-Surface Antigen 2 and Human Epidermal Growth Factor Receptor 2 Expression in Breast Cancer: Toward Guiding Patient Selection for Antibody-Drug Conjugate Therapies.JCO precision oncology · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
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Abstract
purposeAlthough tumor-infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple-negative breast cancer (TNBC), it is subject to inter and intraobserver variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. EXPERIMENTAL
designUsing the QuPath open-source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts, and "other" cells on hematoxylin-eosin (H&E)-stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation, we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets.
resultsWe found that all five machine TIL variables had significant prognostic association with outcomes (
conclusionsNeural net-driven cell classifier-defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open-source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility.
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