ArticleScience advances2025
A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of artificial intelligence-assisted 18f-fdg pet/ct for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis.Annals of nuclear medicine · 2026Pooled it
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Integrating delta radiomics and changes in multimodal ultrasound features for predicting response to neoadjuvant chemotherapy in breast cancer.Gland surgery · 2026Article
- Simultaneous Preoperative Prediction of Locally Advanced Breast Cancer, DCIS Component, and Multifocality Using Structured Mammographic Features and Gradient-Boosting Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer.Signal transduction and targeted therapy · 2026Article
- Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficacy and Safety of Shen-Ling-Lian-Xia Granule Combined With Neoadjuvant Chemotherapy in Patients With Triple-Negative Breast Cancer: Protocol for a Randomized, Double-Blind, Multicenter Clinical Trial.JMIR research protocols · 2026Article
- Patch-to-slide fusion deep learning model for histological diagnosis of early pregnancy loss including hydatidiform mole.NPJ digital medicine · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy.NPJ precision oncology · 2026Article
- Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.ESMO real world data and digital oncology · 2026Review
- Deep learning-based multimodal fusion of MRI and whole slide image for predicting neoadjuvant therapy response in locally advanced head and neck squamous cell carcinoma.BMC medical imaging · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy Decision-Making in Luminal Breast Cancer.Research (Washington, D.C.) · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence.International journal of surgery (London, England) · 2026Article
- Ensemble transformer-based multiple instance learning for predicting neoadjuvant chemotherapy response from breast cancer biopsy whole-slide images.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately predicting pathological complete response (pCR) before neoadjuvant chemotherapy (NAC) is crucial for patients with breast cancer. In this study, we developed a multimodal integrated fully automated pipeline system (MIFAPS) in forecasting pCR to NAC, using a multicenter and prospective dataset of 1004 patients with locally advanced breast cancer, incorporating pretreatment magnetic resonance imaging, whole slide image, and clinical risk factors. The results demonstrated that MIFAPS offered a favorable predictive performance in both the pooled external test set [area under the curve (AUC) = 0.882] and the prospective test set (AUC = 0.909). In addition, MIFAPS significantly outperformed single-modality models (
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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.