Savannah C. Partridge

8.4k total citations · 1 hit paper
120 papers, 5.8k citations indexed

About

Savannah C. Partridge is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Cancer Research. According to data from OpenAlex, Savannah C. Partridge has authored 120 papers receiving a total of 5.8k indexed citations (citations by other indexed papers that have themselves been cited), including 112 papers in Radiology, Nuclear Medicine and Imaging, 24 papers in Pathology and Forensic Medicine and 18 papers in Cancer Research. Recurrent topics in Savannah C. Partridge's work include MRI in cancer diagnosis (100 papers), Radiomics and Machine Learning in Medical Imaging (63 papers) and Advanced Neuroimaging Techniques and Applications (45 papers). Savannah C. Partridge is often cited by papers focused on MRI in cancer diagnosis (100 papers), Radiomics and Machine Learning in Medical Imaging (63 papers) and Advanced Neuroimaging Techniques and Applications (45 papers). Savannah C. Partridge collaborates with scholars based in United States, Germany and Japan. Savannah C. Partridge's co-authors include Constance D. Lehman, Habib Rahbar, Wendy B. DeMartini, Nola M. Hylton, Peter R. Eby, Sue Peacock, Laura J. Esserman, Brenda F. Kurland, Jessica Gibbs and Steven White and has published in prestigious journals such as Cell, Journal of Clinical Oncology and JNCI Journal of the National Cancer Institute.

In The Last Decade

Savannah C. Partridge

114 papers receiving 5.7k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Diffusion-weighted imaging of the breast—a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

2019 270 citations Mami Iima, Savannah C. Partridge et al. European Radiology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Savannah C. Partridge United States	44	4.9k	1.2k	1.1k	579	541	120	5.8k
Lindsay W. Turnbull United Kingdom	37	3.5k 0.7×	778 0.6×	867 0.8×	1.2k 2.0×	291 0.5×	92	4.7k
Nola M. Hylton United States	45	5.2k 1.1×	1.8k 1.4×	2.2k 2.1×	876 1.5×	1.0k 1.9×	172	7.0k
Maxine S. Jochelson United States	41	2.7k 0.5×	888 0.7×	1.1k 1.1×	1.7k 2.9×	964 1.8×	142	4.5k
Gillian M. Newstead United States	33	3.1k 0.6×	1.3k 1.0×	1.1k 1.1×	450 0.8×	458 0.8×	92	4.1k
Başak E. Doğan United States	24	1.3k 0.3×	1.1k 0.9×	1.2k 1.1×	336 0.6×	609 1.1×	115	2.8k
Katja Pinker Austria	56	6.5k 1.3×	1.2k 1.0×	1.1k 1.0×	1.3k 2.2×	775 1.4×	282	8.7k
Marc B. I. Lobbes Netherlands	40	2.2k 0.5×	1.3k 1.0×	1.9k 1.8×	1.4k 2.3×	929 1.7×	126	4.3k
Rita S. Mehta United States	34	1.9k 0.4×	374 0.3×	1.3k 1.2×	997 1.7×	1.4k 2.5×	81	4.0k
Rachel F. Brem United States	35	1.5k 0.3×	1.6k 1.3×	1.4k 1.3×	912 1.6×	713 1.3×	116	3.8k
Eric L. Rosen United States	26	1.2k 0.2×	1.1k 0.9×	844 0.8×	353 0.6×	409 0.8×	46	2.3k

Countries citing papers authored by Savannah C. Partridge

Since Specialization

Citations

This map shows the geographic impact of Savannah C. Partridge's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Savannah C. Partridge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Savannah C. Partridge more than expected).

Fields of papers citing papers by Savannah C. Partridge

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Savannah C. Partridge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Savannah C. Partridge. The network helps show where Savannah C. Partridge may publish in the future.

Co-authorship network of co-authors of Savannah C. Partridge

This figure shows the co-authorship network connecting the top 25 collaborators of Savannah C. Partridge. A scholar is included among the top collaborators of Savannah C. Partridge based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Savannah C. Partridge. Savannah C. Partridge is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Oviedo, Felipe, Yixi Xu, Robert A. Vandermeulen, et al.. (2025). Cancer Detection in Breast MRI Screening via Explainable AI Anomaly Detection. Radiology. 316(1). e241629–e241629. 2 indexed citations

Hippe, Daniel S., et al.. (2025). Time to Enhancement Measured From Ultrafast Dynamic Contrast-Enhanced MRI for Improved Breast Lesion Diagnosis. Journal of Breast Imaging. 7(4). 453–462.

Youn, Inyoung, Daniel S. Hippe, Sara H. Javid, et al.. (2024). Diagnostic Performance of Point-of-Care Apparent Diffusion Coefficient Measures to Reduce Biopsy in Breast Lesions at MRI: Clinical Validation. Radiology. 310(2). e232313–e232313. 6 indexed citations

Bihan, Denis Le, Mami Iima, & Savannah C. Partridge. (2024). Fat-signal suppression in breast diffusion-weighted imaging: the Good, the Bad, and the Ugly. European Radiology. 35(2). 733–741. 5 indexed citations

Boss, Michael A., Dariya Malyarenko, Savannah C. Partridge, et al.. (2024). The QIBA Profile for Diffusion-Weighted MRI: Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker. Radiology. 313(1). e233055–e233055. 15 indexed citations

Kennedy, Laura C., et al.. (2023). 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. 15(17). 4337–4337. 2 indexed citations

Li, Wen, Natsuko Onishi, David C. Newitt, et al.. (2022). Effect of Inter-Reader Variability on Diffusion-Weighted MRI Apparent Diffusion Coefficient Measurements and Prediction of Pathologic Complete Response for Breast Cancer. Tomography. 8(3). 1208–1220. 5 indexed citations

Partridge, Savannah C., Jon A. Steingrimsson, David C. Newitt, et al.. (2022). Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial. Tomography. 8(2). 701–717. 7 indexed citations

Li, Wen, Natsuko Onishi, David C. Newitt, et al.. (2022). Diffusion-Weighted MRI for Predicting Pathologic Complete Response in Neoadjuvant Immunotherapy. Cancers. 14(18). 4436–4436. 14 indexed citations

10.

Sullivan, Erin, et al.. (2022). Validating within-limb calibrated algorithm using a smartphone attached infrared thermal camera for detection of arthritis in children. Journal of Thermal Biology. 111. 103437–103437. 2 indexed citations

11.

Iima, Mami, Savannah C. Partridge, & Denis Le Bihan. (2020). Six DWI questions you always wanted to know but were afraid to ask: clinical relevance for breast diffusion MRI. European Radiology. 30(5). 2561–2570. 45 indexed citations

12.

Newitt, David C., Savannah C. Partridge, Helga S. Marques, et al.. (2020). Repeatability and Reproducibility of ADC Histogram Metrics from the ACRIN 6698 Breast Cancer Therapy Response Trial. Tomography. 6(2). 177–185. 11 indexed citations

13.

Sorace, Anna G., Asser Abou Elkassem, Samuel J. Galgano, et al.. (2020). Imaging for Response Assessment in Cancer Clinical Trials. Seminars in Nuclear Medicine. 50(6). 488–504. 26 indexed citations

14.

Tudorica, Alina, Brendan Moloney, Karen Y. Oh, et al.. (2020). Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study. Tomography. 6(2). 148–159. 15 indexed citations

15.

Li, Wen, Lisa J. Wilmes, Ella F. Jones, et al.. (2019). Additive value of diffusion‐weighted MRI in the I‐SPY 2 TRIAL. Journal of Magnetic Resonance Imaging. 50(6). 1742–1753. 27 indexed citations

16.

Amornsiripanitch, Nita, Habib Rahbar, Daniel S. Hippe, et al.. (2017). Diffusion‐weighted MRI characteristics associated with prognostic pathological factors and recurrence risk in invasive ER+/HER2– breast cancers. Journal of Magnetic Resonance Imaging. 48(1). 226–236. 34 indexed citations

17.

Bowen, Stephen R., William T. C. Yuh, Daniel S. Hippe, et al.. (2017). Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy. Journal of Magnetic Resonance Imaging. 47(5). 1388–1396. 87 indexed citations

18.

Lehman, Constance D., Janie M. Lee, Wendy B. DeMartini, et al.. (2016). Screening MRI in Women With a Personal History of Breast Cancer. JNCI Journal of the National Cancer Institute. 108(3). djv349–djv349. 104 indexed citations

19.

Loiselle, Christopher, Peter R. Eby, Kristine E. Calhoun, et al.. (2013). Preoperative MRI Improves Prediction of Extensive Occult Axillary Lymph Node Metastases in Breast Cancer Patients with a Positive Sentinel Lymph Node Biopsy. Academic Radiology. 21(1). 92–98. 15 indexed citations

20.

Partridge, Savannah C., Graeme C. McKinnon, Roland G. Henry, & Nola M. Hylton. (2001). Menstrual cycle variation of apparent diffusion coefficients measured in the normal breast using MRI. Journal of Magnetic Resonance Imaging. 14(4). 433–438. 97 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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