ArticleScientific reports2021
Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 4 of them syntheses that pooled it.
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Who cites it
67 citing papers in PubMed, 4 syntheses or guidelines pooled it, 119 citations in OpenAlex.
- ESR Essentials: artificial intelligence in breast imaging-practice recommendations by the European Society of Breast Imaging.European radiology · 2026Guideline
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Pooled it
- Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- A dual-tower multimodal framework with feature quality enhancement for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer: a multicenter study.Translational oncology · 2026Article
- Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images.Surgery today · 2026Article
- Joint synthesis of longitudinal MRI and enhanced prediction of neoadjuvant chemotherapy response in breast cancer: a multicohort study.BMC cancer · 2026Article
- Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.Diagnostics (Basel, Switzerland) · 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
- Integrative multimodal hybrid data fusion for mortality prediction.Scientific reports · 2026Article
- Dual-input spatio-temporal transformer model: Predicting the efficacy of NACT in breast cancer based on DCE-MRI images.iScience · 2026Article
- GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.Frontiers in big data · 2026Article
- Parameters That May Predict NAC Effectiveness in Hormone-Positive Breast Cancer According to CPS Score.Cancer management and research · 2026Article
- Multimodal breast cancer diagnosis using feature fusion and deep learning.Frontiers in medicine · 2026Article
- BCMM: gated bidirectional cross-attention fusion of breast MRI and clinical features for predicting RCB response in the I-SPY1 cohort.Frontiers in artificial intelligence · 2026Article
- Multiomics Machine Learning to Predict Neoadjuvant Chemotherapy Outcome and Relapse of Breast Cancer.BME frontiers · 2026Article
- Automated diagnosis of pulmonary nodules in 3D PET/CT images using dual-path densely connected networks with cross-modal fusion.Quantitative imaging in medicine and surgery · 2026Article
- Prediction of pathological complete response after neoadjuvant therapy in breast cancer using pretreatment core biopsy pathology reports: a pathology-report-driven model with exploratory clinical-laboratory fusion.Frontiers in oncology · 2026Article
- Multimodal deep learning for cancer prognosis prediction with clinical information prompts integration.NPJ digital medicine · 2025Article
- Deep learning in multi-modal breast cancer data fusion: a literature review.Quantitative imaging in medicine and surgery · 2025Review
7 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwent pre-operative NAC. We developed a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. The proposed deep learning model trained on all datasets as clinical information, T1-weighted subtraction images, and T2-weighted images shows better performance with area under the curve (AUC) of 0.888 as compared to the model using only clinical information (AUC = 0.827, P < 0.05). Our results demonstrate that the multimodal fusion approach using deep learning with both clinical information and MR images achieve higher prediction performance compared to the deep learning model without the fusion approach. Deep learning could integrate pretreatment MR images with clinical information to improve pCR prediction performance.
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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.