Trial reportBreast cancer research : BCR2016
Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.
Trial report in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00070278 (A Phase III Randomized Neoadjuvant Study of Sequential Epirubicin/Cyclophosphamide and Paclitaxel + - Gemcitabine in Poor Risk Early Breast Cancer), which is not on this map. Cited by 50 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Phase III Randomized Neoadjuvant Study of Sequential Epirubicin/Cyclophosphamide and Paclitaxel + - Gemcitabine in Poor Risk Early Breast Cancer
Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Effect of neoadjuvant chemotherapy on tumor immune infiltration in breast cancer patients: Systematic review and meta-analysis.PloS one · 2023Pooled it
- Predictive and prognostic role of tumour-infiltrating lymphocytes in breast cancer patients with different molecular subtypes: a meta-analysis.BMC cancer · 2020Pooled it
- Lymphocyte density determined by computational pathology validated as a predictor of response to neoadjuvant chemotherapy in breast cancer: secondary analysis of the ARTemis trial.Annals of oncology : official journal of the European Society for Medical Oncology · 2017Trial
- Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support.PLOS digital health · 2026Article
- Computational Pathology Reveals Extracellular-Matrix Imaging Biomarkers of Therapy Response in Preclinical Breast Cancer.Cancers · 2026Article
- Article
- AI-assisted image analysis of tumor-infiltrating lymphocytes as a prognostic marker in chemotherapy-naïve luminal breast cancer.NPJ breast cancer · 2026Article
- Review
- The tumor microenvironment of 14,837 breast cancers is associated with clinical outcome independently of genomic subtypes.Cell reports. 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
- Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.bioRxiv : the preprint server for biology · 2025Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation.Breast cancer research : BCR · 2025Article
- Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.Journal of imaging · 2024Review
- Spatial Biology of Breast Cancer.Cold Spring Harbor perspectives in medicine · 2024Review
- Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.BMC oral health · 2024Article
- Artificial Intelligence in pathology: current applications, limitations, and future directions.Irish journal of medical science · 2024Review
- Predictive modelling of response to neoadjuvant therapy in HER2+ breast cancer.NPJ breast cancer · 2023Article
- Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images.NPJ precision oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 8 institutions in 3 countries.
Funding
Abstract
backgroundThere is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.
methodsWe digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.
resultsMedian lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).
conclusionsA data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.
trial registrationClinicalTrials.gov NCT00070278 ; 03/10/2003.
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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.