ArticleFrontiers in oncology2022
Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic 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 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- 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
- PET/CT-based quantification of intratumoral heterogeneity for predicting pathological complete response in breast cancer patients treated with neoadjuvant chemotherapy: A multicentre study.European journal of nuclear medicine and molecular imaging · 2026Article
- Dual-input spatio-temporal transformer model: Predicting the efficacy of NACT in breast cancer based on DCE-MRI images.iScience · 2026Article
- Article
- 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 radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.Frontiers in oncology · 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
- 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
- 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
- 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
- Article
- Comparative analysis of deep learning and radiomics models in predicting hepatocellular carcinoma differentiation via ultrasound.Frontiers in medicine · 2025Article
- Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.Radiology. Artificial intelligence · 2025Article
- Combined morphology and radiomics of intravoxel incoherent movement as a predictive model for the pathologic complete response before neoadjuvant chemotherapy in patients with breast cancer.Frontiers in oncology · 2025Article
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
- Deep-learning based discrimination of pathologic complete response using MRI in HER2-positive and triple-negative breast cancer.Scientific reports · 2024Article
- Baseline [European journal of nuclear medicine and molecular imaging · 2024Article
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Authors and funding
10 authors at 5 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: To compare the performances of deep learning (DL) to radiomics analysis (RA) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) based on pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in breast cancer. Materials and Methods: This retrospective study included 356 breast cancer patients who underwent DCE-MRI before NAC and underwent surgery after NAC. Image features and kinetic parameters of tumors were derived from DCE-MRI. Molecular information was assessed based on immunohistochemistry results. The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method. Results: The overall pCR rate was 23.3% (83/356). The area under the ROC (AUROC) of the image-kinetic-molecular RA model was 0.781 [95% confidence interval (CI): 0.735, 0.828], which was higher than that of the image-kinetic RA model (0.629, 95% CI: 0.595, 0.663; Conclusions: The pretreatment DCE-MRI-based DL model is superior to the RA model in predicting pCR to NAC in breast cancer patients. The image-kinetic-molecular DL model has the best prediction performance.
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