ArticleNPJ precision oncology2024
Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Deep learning in breast cancer histopathology: predicting and detecting axillary lymph node metastasis.Virchows Archiv : an international journal of pathology · 2026Review
- Review
- Review
- Bidirectional Perceptual Multimodal Interaction Network Based on Contrastive Learning for Breast Cancer pCR Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- Multimodal deep learning framework for recurrence risk stratification in soft tissue sarcoma: a multicenter study.NPJ precision oncology · 2026Article
- Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence.International journal of surgery (London, England) · 2026Article
- Artificial intelligence-driven multimodal fusion for precision diagnosis and personalized management of breast cancer.Oncology reviews · 2026Review
- Fractal analysis of longitudinal MRI for predicting response to neoadjuvant chemotherapy in breast cancer.NPJ precision oncology · 2025Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
- Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- The Evolving Landscape of Deep Learning in Breast Cancer Imaging: A Bibliometric Study of Segmentation, Detection, and Diagnostic Research From 2006 to 2025.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.
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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.