SynthesisTomography (Ann Arbor, Mich.)2022
Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.
Synthesis in Tomography (Ann Arbor, Mich.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 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.
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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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Pooled it
- Pooled it
- Artificial intelligence in breast cancer imaging: a systematic review of diagnostic performance, predictive modeling, and clinical translation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Reply to the Letter to the Editor: A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.European radiology · 2026Article
- Longitudinal Breast MRI for Early Treatment-Response Modeling: A Comparative Study of Handcrafted Radiomics and Frozen Deep Image Embeddings for pCR Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.BMC medical imaging · 2026Article
- Article
- Review
- Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.NPJ digital medicine · 2026Article
- Time-Dependent DCE-MRI Radiomics to Predict Response to Neoadjuvant Therapy in Breast Cancer: A Multicenter Study with External Validation.Diagnostics (Basel, Switzerland) · 2026Article
- Review of Prognostic Significance of Quantitative BPE Measurements.Diagnostics (Basel, Switzerland) · 2026Review
- Modified systemic immune-inflammatory index, modified systemic inflammatory response index and hemoglobin-albumin-lymphocyte-platelet score may serve as markers for evaluating the efficacy of neoadjuvant therapy in breast cancer patients.Frontiers in oncology · 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
- A multicenter study of a predictive model for pathological complete response after neoadjuvant therapy in breast cancer using multimodal digital biomarkers.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Exploring the predictive value of mammography combined with clinical factors in recurrent metastasis of breast cancer.European journal of medical research · 2025Article
- Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.Radiology. Artificial intelligence · 2025Article
- Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer.Journal of translational medicine · 2025Article
- Radiomics Analysis of Breast MRI to Predict Oncotype Dx Recurrence Score: Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer.
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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.