ArticlePloS one2023
Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled 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
- Separating Ranking Signal, Incremental Feature Value, and Evaluation-Induced Optimism in Breast MRI Treatment-Effect Analysis: Foundation Model and Radiomic Representations.Journal of imaging informatics in medicine · 2026Article
- Review
- Bidirectional Perceptual Multimodal Interaction Network Based on Contrastive Learning for Breast Cancer pCR Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.European radiology · 2026Article
- Review of Prognostic Significance of Quantitative BPE Measurements.Diagnostics (Basel, Switzerland) · 2026Review
- Pretreatment MRI radiomics for predicting pathological Miller-Payne grading in breast cancer following neoadjuvant chemotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- MR Imaging Features Predictive of Pathologic Complete Response and Survival Outcomes for Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- A Priori Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Deep Features from Pre-Treatment MRI and CT.Cancers · 2025Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images.Physics in medicine and biology · 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
- Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma.Esophagus : official journal of the Japan Esophageal Society · 2025Article
- Article
- Review
- Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.Radiology. Imaging cancer · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and progression-free survival (PFS) in breast cancer patients treated with neoadjuvant chemotherapy using longitudinal multiparametric MRI, demographics, and molecular subtypes as inputs. In the I-SPY-1 TRIAL, 155 patients with stage 2 or 3 breast cancer with breast tumors underwent neoadjuvant chemotherapy met the inclusion/exclusion criteria. The inputs were dynamic-contrast-enhanced (DCE) MRI, and T2- weighted MRI as three-dimensional whole-images without the tumor segmentation, as well as molecular subtypes and demographics. The outcomes were PCR, RCB, and PFS. Three ("Integrated", "Stack" and "Concatenation") CNN were evaluated using receiver-operating characteristics and mean absolute errors. The Integrated approach outperformed the "Stack" or "Concatenation" CNN. Inclusion of both MRI and non-MRI data outperformed either alone. The combined pre- and post-neoadjuvant chemotherapy data outperformed either alone. Using the best model and data combination, PCR prediction yielded an accuracy of 0.81±0.03 and AUC of 0.83±0.03; RCB prediction yielded an accuracy of 0.80±0.02 and Cohen's κ of 0.73±0.03; PFS prediction yielded a mean absolute error of 24.6±0.7 months (survival ranged from 6.6 to 127.5 months). Deep learning using longitudinal multiparametric MRI, demographics, and molecular subtypes accurately predicts PCR, RCB, and PFS in breast cancer patients. This approach may prove useful for treatment selection, planning, execution, and mid-treatment adjustment.
Indexed as
Identifiers
What OpenQuestion holds
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.