Research graph
References from Development and external evaluation of MRI and clinical models for pathological complete response in breast cancer. Local targets link to admitted publications; unresolved targets remain external evidence.
Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline
10.1200/jco.20.03399 · 2021 · External reference
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 predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL–CALGB 150007/150012, ACRIN 6657
10.1200/jco.2011.39.2779 · 2012 · 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
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
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
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
Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study
10.1158/1078-0432.ccr-18-3190 · 2019 · External reference
Multimodal AI Methods for Predicting Pathologic Complete Response in Breast Cancer
10.1007/s10278-026-02188-4 · 2026 · External reference
Multi-omic machine learning predictor of breast cancer therapy response
10.1038/s41586-021-04278-5 · 2022 · 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
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer
10.1038/s41467-024-53450-8 · 2024 · External reference
Calibration: the Achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
Assessing the performance of prediction models: a framework for traditional and novel measures
10.1097/ede.0b013e3181c30fb2 · 2010 · External reference
A new framework to enhance the interpretation of external validation studies of clinical prediction models
10.1016/j.jclinepi.2014.06.018 · 2015 · External reference
Evaluation of clinical prediction models (part 1): from development to external validation
10.1136/bmj-2023-074819 · 2024 · External reference
Evaluation of clinical prediction models (part 2): how to undertake an external validation study
10.1136/bmj-2023-074820 · 2024 · External reference
Prediction models need appropriate internal, internal-external, and external validation
10.1016/j.jclinepi.2015.04.005 · 2016 · External reference
Stability of clinical prediction models developed using statistical or machine learning methods
10.1002/bimj.202200302 · 2023 · External reference
The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository
10.1007/s10278-013-9622-7 · 2013 · External reference
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
10.1038/s41597-025-04707-4 · 2025 · External reference
A standardized deep learning-ready breast DCE-MRI dataset of 2,070 patients
10.1038/s41597-026-06589-6 · 2026 · External reference
TRIPOD + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
PROBAST + AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
10.1136/bmj-2024-082505 · 2025 · External reference
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10.5281/zenodo.18114231
10.5281/zenodo.18114231 · 2025 · External reference
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Effect of Pembrolizumab Plus Neoadjuvant Chemotherapy on Pathologic Complete Response in Women With Early-Stage Breast Cancer: An Analysis of the Ongoing Phase 2 Adaptively Randomized I-SPY2 Trial
10.1001/jamaoncol.2019.6650 · 2020 · External reference
Minimum sample size for external validation of a clinical prediction model with a binary outcome
10.1002/sim.9025 · 2021 · External reference
Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study
10.1136/bmj-2023-074821 · 2024 · External reference
CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII
10.1186/s13244-023-01415-8 · 2023 · External reference
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
10.1148/radiol.2020191145 · 2020 · External reference
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
10.1093/jamia/ocac093 · 2022 · External reference
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
10.1016/j.jclinepi.2019.02.004 · 2019 · External reference
Bias in error estimation when using cross-validation for model selection
10.1186/1471-2105-7-91 · 2006 · External reference
Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics
10.1186/s13244-021-01115-1 · 2021 · External reference
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
10.1371/journal.pone.0118432 · 2015 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
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Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL
10.1148/radiol.2015150013 · 2016 · External reference
Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial
10.1148/radiol.2018180273 · 2018 · External reference
External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients
10.1093/aje/kwq223 · 2010 · External reference
A calibration hierarchy for risk models was defined: from utopia to empirical data
10.1016/j.jclinepi.2015.12.005 · 2016 · External reference
Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers
10.1002/sim.5941 · 2014 · External reference
Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small
10.1016/j.jclinepi.2020.12.005 · 2021 · External reference
Prediction meets causal inference: the role of treatment in clinical prediction models
10.1007/s10654-020-00636-1 · 2020 · External reference
Overall Survival with Pembrolizumab in Early-Stage Triple-Negative Breast Cancer
10.1056/nejmoa2409932 · 2024 · External reference
Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients
10.1016/s1470-2045(21)00589-1 · 2022 · External reference
Trastuzumab Emtansine for Residual Invasive HER2-Positive Breast Cancer
10.1056/nejmoa1814017 · 2019 · External reference
MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability
10.1038/s41598-020-70940-z · 2020 · External reference
Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial
10.1002/jmri.26539 · 2019 · External reference
Assessing robustness of radiomic features by image perturbation
10.1038/s41598-018-36938-4 · 2019 · External reference
Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes
10.1002/sim.7992 · 2019 · External reference
