Thomas E. Yankeelov

13.2k total citations · 1 hit paper
257 papers, 8.0k citations indexed

About

Thomas E. Yankeelov is a scholar working on Radiology, Nuclear Medicine and Imaging, Modeling and Simulation and Cancer Research. According to data from OpenAlex, Thomas E. Yankeelov has authored 257 papers receiving a total of 8.0k indexed citations (citations by other indexed papers that have themselves been cited), including 189 papers in Radiology, Nuclear Medicine and Imaging, 66 papers in Modeling and Simulation and 41 papers in Cancer Research. Recurrent topics in Thomas E. Yankeelov's work include MRI in cancer diagnosis (125 papers), Radiomics and Machine Learning in Medical Imaging (90 papers) and Advanced MRI Techniques and Applications (72 papers). Thomas E. Yankeelov is often cited by papers focused on MRI in cancer diagnosis (125 papers), Radiomics and Machine Learning in Medical Imaging (90 papers) and Advanced MRI Techniques and Applications (72 papers). Thomas E. Yankeelov collaborates with scholars based in United States, Italy and Canada. Thomas E. Yankeelov's co-authors include John C. Gore, David A. Hormuth, Lori R. Arlinghaus, C. Chad Quarles, Jared A. Weis, Richard G. Abramson, Stephanie L. Barnes, Angela M. Jarrett, Jennifer G. Whisenant and Michael I. Miga and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Thomas E. Yankeelov

252 papers receiving 7.9k citations

Hit Papers

align trajectories

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.

Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion.

2021 152 citations Guillermo Lorenzo, Thomas J.R. Hughes et al. PubMed profile →

Peers

Thomas E. Yankeelov

RNMI

ONCOL

Laurence T. Baxter United States

James P. Freyer United States

Michael Milosevic Canada

Hermann B. Frieboes United States

Vittorio Cristini United States

J. F. Fowler United Kingdom

Anwar R. Padhani United Kingdom

Benedikt Wiestler Germany

Alexander R.A. Anderson United States

Michael C. Joiner United States

Laurence T. Baxter United States

Thomas E. Yankeelov

1.6k ×0.3

RNMI

716 ×0.5

1.6k ×1.5

944 ×0.9

ONCOL

1.7k ×1.7

Citations per year, relative to Thomas E. Yankeelov Thomas E. Yankeelov (= 1×) peers Laurence T. Baxter

Countries citing papers authored by Thomas E. Yankeelov

Since Specialization

Citations

This map shows the geographic impact of Thomas E. Yankeelov'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 Thomas E. Yankeelov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas E. Yankeelov more than expected).

Fields of papers citing papers by Thomas E. Yankeelov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas E. Yankeelov. 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 Thomas E. Yankeelov. The network helps show where Thomas E. Yankeelov may publish in the future.

Co-authorship network of co-authors of Thomas E. Yankeelov

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas E. Yankeelov. A scholar is included among the top collaborators of Thomas E. Yankeelov 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 Thomas E. Yankeelov. Thomas E. Yankeelov 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

Lima, Ernesto A. B. F., et al.. (2024). A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin. Cancer Biology & Therapy. 25(1). 2321769–2321769. 11 indexed citations

Jarrett, Angela M., et al.. (2023). Investigating tumor-host response dynamics in preclinical immunotherapy experiments using a stepwise mathematical modeling strategy. Mathematical Biosciences. 366. 109106–109106. 1 indexed citations

Lima, Ernesto A. B. F., Chengyue Wu, Angela M. Jarrett, et al.. (2023). Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting. Journal of Computational Science. 69. 102006–102006. 4 indexed citations

Wu, Chengyue, Guillermo Lorenzo, David A. Hormuth, et al.. (2022). Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. PubMed. 3(2). 21304–21304. 68 indexed citations

Hormuth, David A., Tessa Davis, Gibraan Rahman, et al.. (2022). Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer. Cancers. 14(7). 1837–1837. 31 indexed citations

Wu, Chengyue, Angela M. Jarrett, Zijian Zhou, et al.. (2022). MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Cancer Research. 82(18). 3394–3404. 45 indexed citations

Hormuth, David A., Chengyue Wu, William T. Phillips, et al.. (2021). Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme. Biomedical Physics & Engineering Express. 7(4). 45012–45012. 17 indexed citations

DiCarlo, Julie C., John Virostko, Anna G. Sorace, et al.. (2021). Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer. Tomography. 7(3). 253–267. 2 indexed citations

Jarrett, Angela M., Chengyue Wu, John Virostko, et al.. (2021). Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nature Protocols. 16(11). 5309–5338. 26 indexed citations

10.

Virostko, John, Chengyue Wu, Anna G. Sorace, et al.. (2021). The rate of breast fibroglandular enhancement during dynamic contrast-enhanced MRI reflects response to neoadjuvant therapy. European Journal of Radiology. 136. 109534–109534. 2 indexed citations

11.

Hormuth, David A., Chengyue Wu, Ernesto A. B. F. Lima, et al.. (2021). Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers. 13(12). 3008–3008. 37 indexed citations

12.

Hormuth, David A., Angela M. Jarrett, Kaitlyn E. Johnson, et al.. (2020). Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology. iScience. 23(12). 101807–101807. 27 indexed citations

13.

Wu, Chengyue, David A. Hormuth, Todd Oliver, et al.. (2020). Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE Transactions on Medical Imaging. 39(9). 2760–2771. 24 indexed citations

14.

Syed, Anum, Jennifer G. Whisenant, Stephanie L. Barnes, Anna G. Sorace, & Thomas E. Yankeelov. (2020). Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer. Cancers. 12(6). 1682–1682. 34 indexed citations

15.

Wu, Chengyue, Federico Pineda, David A. Hormuth, Gregory S. Karczmar, & Thomas E. Yankeelov. (2018). Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors. Magnetic Resonance in Medicine. 81(3). 2147–2160. 46 indexed citations

16.

Abramson, Richard G., Laurie Jones-Jackson, Lori R. Arlinghaus, et al.. (2015). Prone Versus Supine Breast FDG-PET/CT for Assessing Locoregional Disease Distribution in Locally Advanced Breast Cancer. Academic Radiology. 22(7). 853–859. 10 indexed citations

17.

Abramson, Richard G., Kirsteen R. Burton, John‐Paul J. Yu, et al.. (2014). Methods and Challenges in Quantitative Imaging Biomarker Development. Academic Radiology. 22(1). 25–32. 64 indexed citations

18.

Li, Xia, E. Brian Welch, Lori R. Arlinghaus, et al.. (2011). A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology. 56(17). 5753–5769. 54 indexed citations

19.

Arlinghaus, Lori R., E. Brian Welch, A. Bapsi Chakravarthy, et al.. (2011). Motion correction in diffusion‐weighted MRI of the breast at 3T. Journal of Magnetic Resonance Imaging. 33(5). 1063–1070. 26 indexed citations

20.

Yankeelov, Thomas E., et al.. (2007). Incorporating contrast agent diffusion into the analysis of DCE‐MRI data. Magnetic Resonance in Medicine. 58(6). 1124–1134. 47 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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