Rachel earned her bachelor’s degrees in physics and applied mathematics from the University of Utah in 2015. She then spent three years working with the genitourinary oncology group at Huntsman Cancer Institute, where her research focused primarily on treatment outcomes for patients diagnosed with late stage prostate cancer. She also spearheaded the effort to integrate her institution's genitourinary cancer data collection into the ORIEN Avatar cancer center alliance. Rachel joined the Physics & Biology in Medicine PhD Program in 2018, and the RO-SAML group shortly thereafter. Her current research focuses on the development of automated tools to enhance patient safety in the image guided radiotherapy clinical workflow and to search retrospectively for previously unreported clinical scenarios that represent learning opportunities. She is also heavily involved in qualitative research projects to study the most efficient and effective methods of integrating automation into the clinic, with a particular focus on the barriers and facilitators to use. Rachel is particularly interested in studying the ways automation and clinicians can successfully interface in underserved clinics and improve patient care, especially for patients in remote areas of the country without easy access to renowned cancer centers. When she's not in the lab, Rachel enjoys long distance running and escaping to the mountains for long hikes and backpacking trips with her husband and dog.

1. Petragallo, R., Bertram, P., Halvorsen, P., Iftimia, I., Low, D.A., Morin, O., Narayanasamy, G., Saenz, D.L., Sukumar, K.N., Valdes, G., Weinstein, L., Wells, M.C., Ziemer, B.P., Lamb, J.M. (2023). Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images. Medical Physics, 50, 2662-2671.
2. Luximon, D. C., Ritter, T., Fields, E., Neylon, J., Petragallo, R., Abdulkadir, Y., Charters J., Low D.A., & Lamb, J. M. (2022). Development and interinstitutional validation of an automatic vertebral-body misalignment error detector for cone-beam CT-guided radiotherapy. Medical Physics, 49(10), 6410-6423.
3. Petragallo, R., Bardach, N., Ramirez, E., & Lamb, J. M. (2022). Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: a survey study of medical dosimetrists. Journal of Applied Clinical Medical Physics, 23:e, 13568.
4. Tward, J.D., O'Neil, B., Boucher, K., Kokeny, K., Lowrance, W.T., Lloyd, S., Cannon, D., Stephenson, R.A., Agarwal, N., Farr, T., Petragallo, R., Sherar, N.Z., Kunz, I., Hofer, A., Courdy, S., Shrieve, D.C., & Dechet, C. (2020). Metastasis, mortality, and quality of life for men with NCCN high and very high risk localized prostate cancer after surgical and/or combined modality radiotherapy. Clinical Genitourinary Cancer, 18(4), 274-283.

Recent Honors & Awards: