James T. Coates

649 total citations
17 papers, 276 citations indexed

About

James T. Coates is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, James T. Coates has authored 17 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Oncology and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in James T. Coates's work include Radiomics and Machine Learning in Medical Imaging (6 papers), Advanced Radiotherapy Techniques (3 papers) and Medical Imaging Techniques and Applications (2 papers). James T. Coates is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), Advanced Radiotherapy Techniques (3 papers) and Medical Imaging Techniques and Applications (2 papers). James T. Coates collaborates with scholars based in United States, United Kingdom and Canada. James T. Coates's co-authors include Issam El Naqa, Geoff S. Higgins, Luís Souhami, Sean Smart, Paul Kinchesh, Nicola R. Sibson, James R. Larkin, Michael A. Chappell, Marc David and Barry S. Rosenstein and has published in prestigious journals such as Cancer Research, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

James T. Coates

16 papers receiving 272 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James T. Coates United States	10	158	72	54	54	40	17	276
Max C. Wu United States	10	153 1.0×	69 1.0×	43 0.8×	73 1.4×	8 0.2×	15	340
Trang Thanh Pham Australia	10	203 1.3×	116 1.6×	131 2.4×	56 1.0×	16 0.4×	21	455
Jake C. Forster Australia	8	106 0.7×	81 1.1×	33 0.6×	114 2.1×	10 0.3×	12	345
A.E. Saarnak Netherlands	11	117 0.7×	200 2.8×	125 2.3×	181 3.4×	42 1.1×	13	393
Sarah C. Brüningk Switzerland	13	81 0.5×	106 1.5×	75 1.4×	121 2.2×	10 0.3×	24	349
Ken Kang‐Hsin Wang United States	8	97 0.6×	219 3.0×	59 1.1×	238 4.4×	29 0.7×	19	312
Scott Friesen United States	9	121 0.8×	192 2.7×	164 3.0×	81 1.5×	20 0.5×	31	421
M Scarpelli United States	10	108 0.7×	219 3.0×	110 2.0×	31 0.6×	43 1.1×	29	350
Sebastian Marschner Germany	9	179 1.1×	121 1.7×	72 1.3×	51 0.9×	8 0.2×	38	340
Akihiro Nomoto Japan	10	120 0.8×	275 3.8×	155 2.9×	111 2.1×	47 1.2×	25	399

Countries citing papers authored by James T. Coates

Since Specialization

Citations

This map shows the geographic impact of James T. Coates'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 James T. Coates with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James T. Coates more than expected).

Fields of papers citing papers by James T. Coates

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James T. Coates. 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 James T. Coates. The network helps show where James T. Coates may publish in the future.

Co-authorship network of co-authors of James T. Coates

This figure shows the co-authorship network connecting the top 25 collaborators of James T. Coates. A scholar is included among the top collaborators of James T. Coates 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 James T. Coates. James T. Coates is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Spring, Laura M., Bogang Wu, Simona Cristea, et al.. (2024). Abstract PR08: Intratumoral heterogeneity drives resistance to Antibody Drug Conjugate therapy: Analysis of the NeoSTAR trial of neoadjuvant Sacituzumab govitecan for localized TNBC. Cancer Research. 84(3_Supplement_1). PR08–PR08. 1 indexed citations

Cristea, Simona, Mengran Zhang, Siang‐Boon Koh, et al.. (2023). Abstract 4353: Utilizing scRNA sequencing to understand biomarkers of response and resistance to Sacitizumab Govetican in localized TNBC. Cancer Research. 83(7_Supplement). 4353–4353. 1 indexed citations

Wu, Bogang, Sheng Sun, Aiko Nagayama, et al.. (2023). Abstract 1696: Systematic identification of pathways associated with antibody drug conjugate sensitivity in breast cancer. Cancer Research. 83(7_Supplement). 1696–1696.

Coates, James T., et al.. (2022). Machine learning-driven critical care decision making. Journal of the Royal Society of Medicine. 115(6). 236–238. 1 indexed citations

Coates, James T., Giacomo Pirovano, & Issam El Naqa. (2021). Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. Journal of Medical Imaging. 8(3). 31902–31902. 11 indexed citations

Coates, James T., Gonzalo Rodríguez‐Berriguete, Rathi Puliyadi, et al.. (2020). The anti-malarial drug atovaquone potentiates platinum-mediated cancer cell death by increasing oxidative stress. Cell Death Discovery. 6(1). 110–110. 18 indexed citations

Larkin, James R., James T. Coates, Paul Kinchesh, et al.. (2019). Tumor pH and Protein Concentration Contribute to the Signal of Amide Proton Transfer Magnetic Resonance Imaging. Cancer Research. 79(7). 1343–1352. 60 indexed citations

Kang, John, James T. Coates, Robert L. Strawderman, Barry S. Rosenstein, & Sarah L. Kerns. (2019). Radiogenomics models in precision radiotherapy: from mechanistic to machine learning. arXiv (Cornell University). 2 indexed citations

Coates, James T., et al.. (2018). Targeting tumour hypoxia: shifting focus from oxygen supply to demand. British Journal of Radiology. 92(1093). 20170843–20170843. 23 indexed citations

10.

Naqa, Issam El, Sarah L. Kerns, James T. Coates, et al.. (2017). Radiogenomics and radiotherapy response modeling. Physics in Medicine and Biology. 62(16). R179–R206. 38 indexed citations

11.

Coates, James T., Luís Souhami, & Issam El Naqa. (2016). Big Data Analytics for Prostate Radiotherapy. Frontiers in Oncology. 6. 149–149. 27 indexed citations

12.

Coates, James T. & Issam El Naqa. (2016). Outcome modeling techniques for prostate cancer radiotherapy: Data, models, and validation. Physica Medica. 32(3). 512–520. 13 indexed citations

13.

Coates, James T., Marc David, Sergio Faria, et al.. (2015). Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer. Radiotherapy and Oncology. 115(1). 107–113. 21 indexed citations

14.

Coates, James T., Marc David, Sergio Faria, et al.. (2014). Sci—Thur AM: YIS ‐ 02: Radiogenomic Modeling of Normal Tissue Toxicities in Prostate Cancer Patients Receiving Hypofractionated Radiotherapy. Medical Physics. 41(8Part1). 1–1. 26 indexed citations

15.

Fernandes, Bruno F., et al.. (2013). Local chemotherapeutic agents for the treatment of ocular malignancies. Survey of Ophthalmology. 59(1). 97–114. 22 indexed citations

16.

Fernandes, Bruno F., et al.. (2013). Hypoxia-Inducible Factor-1α and its Role in the Proliferation of Retinoblastoma Cells. Pathology & Oncology Research. 20(3). 557–563. 9 indexed citations

17.

Coates, James T.. (2008). Seeing the Invisible; Practical Studies in Psychometry, Thought Transference, Telepathy, and Allied Phenomena. Medical Entomology and Zoology. 3 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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