ArticleBritish journal of cancer2024
A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial.
Article in British journal of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 9 papers.
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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.
Neoadjuvant Docetaxel + Carboplatin Versus Epirubicin+Cyclophosphamide Followed by Docetaxel in Triple-Negative, Early-Stage Breast Cancer (NeoCART): Study Protocol for a Multicenter, Randomized Controlled, Open-Label, Phase 2 Trial
Neoadjuvant TCHP Versus THP in Patients With Human Epidermal Growth Factor Receptor 2-positive Breast Cancer (neoCARHP) : a Randomized, Open-label, Multicenter, Phase III Trial
Who cites it
9 citing papers in PubMed.
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Peritumoral habitat radiomics predicts axillary lymph node metastasis in breast cancer.iScience · 2026Article
- Deep Learning Radiomics of Multiparametric MRI for Individualized Prediction of Axillary Lymph Node Response After Neoadjuvant Chemotherapy in Breast Cancer.Breast cancer (Dove Medical Press) · 2026Article
- MRI-based habitat radiomics for preoperative prediction of axillary pathological complete response in breast cancer after neoadjuvant therapy: a multicenter study.Frontiers in oncology · 2026Article
- A Five-Year Retrospective Analysis of Neoadjuvant Therapy in Early-Stage Breast Cancer: Subtype Outcomes and Imaging Concordance.The breast journal · 2026Article
- Development and validation of a delta ultrasomics model for predicting treatment response to neoadjuvant chemotherapy in breast cancer.Translational cancer research · 2025Article
- Nomograms Integrating MRI-derived Apparent Diffusion Coefficient and Clinicopathologic Features for Prediction of Axillary Lymph Node Metastasis in Breast Cancer.Radiology. Imaging cancer · 2025Article
- Constructing a screening model to identify patients at high risk of hospital-acquired influenza on admission to hospital.Frontiers in public health · 2025Article
- Habitat imaging with intratumoral radiomics for prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients.Frontiers in molecular biosciences · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundThis study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer.
methodsWe included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC.
resultsThis study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05).
conclusionsThe stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists. TRIAL REGISTRATION NUMBER: The clinical trial numbers were NCT03154749 and NCT04858529.
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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 Socratic graph.