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Savannah C. Partridge

University of Washington · US
Area of research
Radiology, Nuclear Medicine and Imaging · Cancer Research
Research interest
Research interests include MRI in cancer diagnosis, Radiomics and Machine Learning in Medical Imaging, Advanced Neuroimaging Techniques and Applications, and Advanced MRI Techniques and Applications.
h-index
51
citations
8,441
works
207
NIH funding
primary concept
email

Recent publications

Radiomics-based Machine Learning Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Physiologically Decomposed Diffusion-weighted MRI
Radiology Imaging Cancer 2025cited by 6position: middledoi
The QIBA Profile for Diffusion-Weighted MRI: Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker
Radiology 2024cited by 47position: middledoi
The <scp>ISMRM</scp> Open Science Initiative for Perfusion Imaging (<scp>OSIPI</scp>): Results from the <scp>OSIPI–Dynamic Contrast‐Enhanced</scp> challenge
Magnetic Resonance in Medicine 2023cited by 12position: middledoi
Associations of Multiparametric Breast MRI Features, Tumor-Infiltrating Lymphocytes, and Immune Gene Signature Scores Following a Single Dose of Trastuzumab in HER2-Positive Early-Stage Breast Cancer
Cancers 2023cited by 6position: middledoi
Diffusion-Weighted MRI for Predicting Pathologic Complete Response in Neoadjuvant Immunotherapy
Cancers 2022cited by 22position: middledoi
Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response
Radiology 2021cited by 36position: middledoi
Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL
npj Breast Cancer 2020cited by 81position: middledoi
Mean Apparent Diffusion Coefficient Is a Sufficient Conventional Diffusion-weighted MRI Metric to Improve Breast MRI Diagnostic Performance: Results from the ECOG-ACRIN Cancer Research Group A6702 Diffusion Imaging Trial
Radiology 2020cited by 43position: lastdoi
Imaging for Response Assessment in Cancer Clinical Trials
Seminars in Nuclear Medicine 2020cited by 43position: middledoi
Factors Affecting Image Quality and Lesion Evaluability in Breast Diffusion-weighted MRI: Observations from the ECOG-ACRIN Cancer Research Group Multisite Trial (A6702)
Journal of Breast Imaging 2020cited by 17position: lastdoi
Diffusion-weighted MRI for Unenhanced Breast Cancer Screening
Radiology 2019cited by 154position: lastdoi
Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702)
Clinical Cancer Research 2019cited by 140position: lastdoi
Background parenchymal enhancement on breast MRI: A comprehensive review
Journal of Magnetic Resonance Imaging 2019cited by 128position: middledoi
Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial
Radiology 2018cited by 281position: firstdoi
MRI, Clinical Examination, and Mammography for Preoperative Assessment of Residual Disease and Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer: ACRIN 6657 Trial
American Journal of Roentgenology 2018cited by 128position: middledoi
Test–retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial
Journal of Magnetic Resonance Imaging 2018cited by 108position: middledoi
Diffusion-weighted breast MRI: Clinical applications and emerging techniques
Journal of Magnetic Resonance Imaging 2016cited by 335position: firstdoi
MR spectroscopy of breast cancer for assessing early treatment response: Results from the ACRIN 6657 MRS trial
Journal of Magnetic Resonance Imaging 2016cited by 53position: middledoi
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
Radiology 2015cited by 275position: middledoi
Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis
BMC Cancer 2015cited by 134position: middledoi

Grants

No grants ingested yet.

Frequent collaborators

Nola M. Hylton · UCSF Helen Diller Family Comprehensive Cancer Center7 papers (2015–2020)Bonnie N. Joe · University of California, San Francisco6 papers (2018–2022)Thomas L. Chenevert · University of Michigan–Ann Arbor6 papers (2018–2024)Habib Rahbar · University of Washington6 papers (2016–2020)Laura J. Esserman · UCSF Helen Diller Family Comprehensive Cancer Center6 papers (2015–2022)David C. Newitt · University of California, San Francisco5 papers (2015–2022)Başak E. Doğan · Bartin University5 papers (2018–2020)Mitchell D. Schnall · ECOG-ACRIN Cancer Research Group5 papers (2015–2020) · 5 papers (2018–2020)Mark Rosen · Amgen (United States)5 papers (2015–2018)Justin Romanoff · Brown University4 papers (2018–2020)Lisa J. Wilmes · University of California, San Francisco4 papers (2020–2024)Linda Moy · New York University4 papers (2019–2020) · 4 papers (2016–2020)Helga S. Marques · Brown University4 papers (2015–2018)Constance D. Lehman · Breast Center4 papers (2015–2019)Paul T. Weatherall · Southwestern Medical Center3 papers (2015–2018)Jessica Gibbs · University of Georgia3 papers (2018–2022)Elizabeth A. Morris · University of Surrey3 papers (2015–2018)Thomas E. Yankeelov · Livestrong Foundation3 papers (2020–2020)