Research graph
References from Vision Transformer for Pretreatment Prediction of Pathologic Complete Response in Multi-center Breast DCE-MRI. Local targets link to admitted publications; unresolved targets remain external evidence.
Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis
10.1016/s0140-6736(13)62422-8 · 2014 · External reference
Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis
10.1158/1078-0432.ccr-19-3492 · 2020 · External reference
Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy—results from ACRIN 6657/I-SPY TRIAL
10.1148/radiol.12110748 · 2012 · External reference
Mapping pathophysiological features of breast tumors by MRI at high spatial resolution
10.1038/nm0797-780 · 1997 · External reference
Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced MR mammography
10.1016/j.clinimag.2005.11.005 · 2006 · External reference
3TP-CNN: radiomics and deep learning for lesions classification in DCE-MRI
2019 · External reference
BreastDCEDL: a standardized deep learning-ready breast DCE-MRI dataset of 2,070 patients
10.1038/s41597-026-06589-6 · 2026 · External reference
Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials
2016 · External reference
I-SPY 2 breast dynamic contrast enhanced MRI trial (ISPY2)
2022 · External reference
Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)
2021 · External reference
Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multi-center study
10.1158/1078-0432.ccr-18-3190 · 2019 · External reference
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set
10.1007/s10549-018-4990-9 · 2019 · External reference
Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL
10.1038/s41523-020-00203-7 · 2020 · External reference
MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer
2023 · External reference
Nomogram for predicting neoadjuvant chemotherapy response in breast cancer using MRI-based intratumoral heterogeneity quantification
10.1148/radiol.241805 · 2025 · External reference
Pretreatment DCE-MRI-based deep learning outperforms radiomics analysis in predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer
2022 · External reference
A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database
10.1016/j.mri.2020.08.021 · 2020 · External reference
Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs
10.1038/s41598-021-93592-z · 2021 · External reference
PD-DWI: predicting response to neoadjuvant chemotherapy in invasive breast cancer with physiologically-decomposed diffusion-weighted MRI machine-learning model
2022 · External reference
Deep learning models based on pretreatment MRI and clinicopathological data to predict responses to neoadjuvant systemic therapy in triple-negative breast cancer
10.3390/cancers17060966 · 2025 · External reference
Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
10.1038/s41598-021-98408-8 · 2021 · External reference
A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer
10.1126/sciadv.adr1576 · 2025 · External reference
Attention is all you need
2017 · External reference
Transformers in medical image analysis
10.1016/j.imed.2022.07.002 · 2023 · External reference
Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer
10.1186/s12967-025-06617-w · 2025 · External reference
Multi-modal longitudinal representation learning for predicting neoadjuvant therapy response in breast cancer treatment
10.1109/jbhi.2025.3540574 · 2025 · External reference
Dual-input spatio-temporal transformer model: Predicting the efficacy of NACT in breast cancer based on DCE-MRI images
10.1016/j.isci.2025.114433 · 2026 · External reference
The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository
10.1007/s10278-013-9622-7 · 2013 · External reference
Curated, segmented, and deep learning-optimized I-SPY 2 MRI dataset for prediction of pCR, HR, and HER2 Status (BreastDCEDL-ISPY2)
2025 · External reference
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI
10.1186/s13058-017-0846-1 · 2017 · External reference
Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer
2017 · External reference
Scikit-learn: machine learning in Python
2011 · External reference
BreastDCEDL: a deep learning-ready breast DCE-MRI dataset
2025 · External reference
Unresolved reference
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