Reid Thompson, M.D., Ph.D. is an assistant professor of radiation medicine and a member of the Knight Cancer Institute’s Computational Biology Program. He received his medical and doctorate degrees from the Albert Einstein College of Medicine and trained at the University of Pennsylvania. He also holds degrees in genetics and biophysical chemistry.
Immunotherapy has opened a new frontier in cancer treatment and has improved survival for many patients with metastatic melanoma and other aggressive cancers. The field of immunotherapy is exciting and fast-evolving, however there remain a number of critical questions regarding predictors of immunotherapy response and treatment-related toxicity. The Thompson Lab is actively engaged in this field broadly, and focused specifically on cancer neoepitope prediction and genomic correlates of immunotherapy response and toxicity. The lab has multiple active projects ongoing, from development of novel software tools and algorithms to analysis of large immunotherapy datasets to clinical informatics based investigations of cancer outcomes.
Lung cancer screening of high-risk individuals has been shown to improve survival in the population, and can lead to earlier diagnosis, earlier stage migration, and more effective treatment for the disease. However, there are many outstanding issues and opportunities for improving lung cancer outcomes. In conjunction with The Knight Cancer Institute's, Center for Cancer Early Detection Advanced Research (CEDAR), the Thompson Lab is investigating biomarkers for early detection of lung cancer and lung cancer outcomes prediction.
Dr. Thompson serves as a co-leader of the Portland VA's Clinical Data Science Research Group (CDSRG). Within the scope of the CDSRG, the Thompson Lab is actively investigating patterns of cancer care and long-term patient outcomes within the VA, particularly as related to radiotherapy.
About Thompson Lab
Julianne David, Ph.D. student
Mary Wood, Research Associate
April 13, 2018
Reid Thompson, M.D., Ph.D., assistant professor, presented webinar on Artificial Intelligence in Radiation Oncology
April 12, 2018
Artificial Intelligence in Radiation Oncology, ACR Data Science Institute podcast featuring Dr. Reid Thompson
March 7, 2018
Reid Thompson, M.D., Ph.D. participated in 60th Annual ASTRO panel, "Current Applications & Future Directions"
May 6, 2017
Human leukocyte antigen susceptibility map for SARS-CoV-2. Nguyen A, David JK, Maden SK, Wood MA, Weeder BR, Nellore A, Thompson RF. J Virol. 2020 Apr 17. pii: JVI.00510-20. doi: 10.1128/JVI.00510-20.
Burden of tumor mutations, neoepitopes, and other variants are weak predictors of cancer immunotherapy response and overall survival. Wood MA, Weeder BR, David JK, Nellore A, Thompson RF. Genome Med. 2020 Mar 30;12(1):33. doi: 10.1186/s13073-020-00729-2.
Real-world applications of deep convolutional neural networks in diagnostic cancer imaging. Elhalawani H, Yang P, Abazeed M, Shah C, Mohamed ASR, Thomas CR Jr, Fuller CD, Thompson RF. Chin Clin Oncol. 2020 Feb 1. pii: cco.2020.01.02. doi: 10.21037/cco.2020.01.02
neoepiscope improves neoepitope prediction with multivariant phasing. Wood MA, Nguyen A, Struck AJ, Ellrott K, Nellore A, Thompson RF. Bioinformatics. 2020 Feb 1;36(3):713-720. doi: 10.1093/bioinformatics/btz653.
In Regard to Wallner et al. Kang J, Thompson RF, Fuller CD, Camphausen KA, Gabriel PE, Thomas CR. Int J Radiat Oncol Biol Phys. 2020 Jan 1;106(1):217-218. doi: 10.1016/j.ijrobp.2019.10.044
Validity of Veterans Health Administration structured data to determine accurate smoking status. Golden SE, Hooker ER, Shull S, Howard M, Crothers K, Thompson RF, Slatore CG. Health Informatics J. 2019 Nov 7:1460458219882259. doi: 10.1177/1460458219882259.
Artificial Intelligence in Radiation Oncology. Deig CR, Kanwar A, Thompson RF. Hematol Oncol Clin North Am. 2019 Dec;33(6):1095-1104. doi: 10.1016/j.hoc.2019.08.003. Epub 2019 Sep 11
Tumor Mutation Burden-From Doubts to Concerns. Wood MA, Nellore A, Thompson RF. JAMA Oncol. 2019 Oct 24. doi: 10.1001/jamaoncol.2019.4138.
Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology. Luh JY, Thompson RF, Lin S. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1343-1346. doi: 10.1016/j.jacr.2019.05.044
Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy. Pillai M, Adapa K, Das SK, Mazur L, Dooley J, Marks LB, Thompson RF, Chera BS. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1267-1272. doi: 10.1016/j.jacr.2019.06.001.
Enhancing Career Paths for Tomorrow's Radiation Oncologists. Vapiwala N, Thomas CR Jr, Grover S, Yap ML, Mitin T, Shulman LN, Gospodarowicz MK, Longo J, Petereit DG, Ennis RD, Hayman JA, Rodin D, Buchsbaum JC, Vikram B, Abdel-Wahab M, Epstein AH, Okunieff P, Goldwein J, Kupelian P, Weidhaas JB, Tucker MA, Boice JD Jr, Fuller CD, Thompson RF, Trister AD, Formenti SC,
The Potential and Pitfalls of Crowdsourced Algorithm Development in Radiation Oncology. Elhalawani H, Fuller CD, Thompson RF. JAMA Oncol. 2019 May 1;5(5):662-663. doi: 10.1001/jamaoncol.2019.0157.
Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C Jr, Rosenthal SA, Yu JB, Thomas CR Jr. Radiother Oncol. 2018 Dec;129(3):421-426. doi: 10.1016/j.radonc.2018.05.030.
Career Enrichment Opportunities at the Scientific Frontier in Radiation Oncology. Thompson RF, Fuller CD, Berman AT, Aneja S, Thomas CR Jr. JCO Clin Cancer Inform. 2019 Feb;3:1-4. doi: 10.1200/CCI.18.00126.