ArticleFrontiers in oncology2022
Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Hybrid deep learning time-to-event modeling of major adverse cardiovascular events using coronary artery calcium score scans.European journal of radiology artificial intelligence · 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
- HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Bidirectional Perceptual Multimodal Interaction Network Based on Contrastive Learning for Breast Cancer pCR Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- Multiregional MRI-based deep learning radiomics to predict axillary response after neoadjuvant chemotherapy in breast cancer patients.Insights into imaging · 2026Article
- Dual-input spatio-temporal transformer model: Predicting the efficacy of NACT in breast cancer based on DCE-MRI images.iScience · 2026Article
- Prediction of pathological complete response to neoadjuvant therapy in breast cancer using deep learning with multi-modal radiological image and biopsy whole slide images: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Predicting breast cancer response to neoadjuvant therapy by integrating radiomic and deep-learning features from early-and-peak phases of DCE-MRI.BMC cancer · 2025Article
- A Priori Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Deep Features from Pre-Treatment MRI and CT.Cancers · 2025Article
- BCECNN: an explainable deep ensemble architecture for accurate diagnosis of breast cancer.BMC medical informatics and decision making · 2025Article
- Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization.Bioengineering (Basel, Switzerland) · 2025Article
- Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 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
- Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.Breast cancer research : BCR · 2025Article
- Performance of low- and high-temporal-resolution DCE-MRI texture analysis in distinguishing breast lesions from background enhancement.American journal of translational research · 2025Article
- Deep learning model for the early prediction of pathologic response following neoadjuvant chemotherapy in breast cancer patients using dynamic contrast-enhanced MRI.Frontiers in oncology · 2025Article
- Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.Cancer medicine · 2024Article
- Leveraging MRI radiomics signature for predicting the diagnosis of CXCL9 in breast cancer.Heliyon · 2024Article
- Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability.Scientific reports · 2024Article
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Materials and methods: This retrospective study included 95 women (mean age, 48.1 years; range, 29-77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results: The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. Conclusion: The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.
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