Marc D. Ryser, Assistant Professor in Population Health Sciences

Marc D. Ryser conducts research in cancer early detection, with a particular focus on breast cancer overdiagnosis and overtreatment. Using a multi-scale approach, his group generates and analyzes biologic, clinical and population data using a variety of mathematical, statistical and computational tools. Examples of ongoing projects include the evolutionary dynamics of early-stage breast cancer; decision support tools for early-stage breast cancer patients; and estimation of breast cancer overdiagnosis. Dr. Ryser has a keen interest in training the next generation of interdisciplinary researchers and teaches an immersive research seminar for undergraduate students called “Math & Medicine.”

Website: https://sites.duke.edu/marcdryser/

Contact Info:

Office Location:	215 Morris St, Suite 210, Durham, NC 27701
Email Address:
Web Pages:	http://www.math.duke.edu/~ryser/ https://sites.duke.edu/marcdryser/

Education:

Ph.D.	McGill University (Canada)	2011
MS (Physics)	EPFL	2006
BS (Physics)	EPFL	2004

Research Interests:

Please visit my personal site for information about me and my research

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Keywords:

Bone and Bones • Bone Neoplasms • Bone Resorption • Chemotaxis • Computational Biology • Computer Simulation • Fractures, Bone • Gene Expression Profiling • Homeostasis • Meta-Analysis as Topic • Models, Biological • Osteoblasts • Osteoprotegerin • Parathyroid Hormone-Related Protein • RANK Ligand • Solubility • Time Factors

Recent Publications   (More Publications)

1. Lange, J; Zhao, Y; Gogebakan, KC; Olivas-Martinez, A; Ryser, MD; Gard, CC; Etzioni, R, Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure., Stat Methods Med Res, vol. 32 no. 6 (June, 2023), pp. 1053-1063 [doi]  [abs]
2. Byng, D; Thomas, SM; Rushing, CN; Lynch, T; McCarthy, A; Francescatti, AB; Frank, ES; Partridge, AH; Thompson, AM; Retèl, VP; van Harten, WH; Grimm, LJ; Hyslop, T; Hwang, ES; Ryser, MD, Surveillance Imaging after Primary Diagnosis of Ductal Carcinoma in Situ., Radiology, vol. 307 no. 1 (April, 2023), pp. e221210 [doi]  [abs]
3. Etzioni, R; Gulati, R; Owens, L; Lange, J; Ryser, MD, Opportunity for interception as a driver of benefit in cancer early detection: implications for multi-cancer early detection testing., Cancer Prevention Research, vol. 16 no. 1 (2023), pp. 6-6
4. Fridman, I; Chan, L; Thomas, J; Fish, LJ; Falkovic, M; Brioux, J; Hunter, N; Ryser, DH; Hwang, ES; Pollak, KI; Weinfurt, KP; Ryser, MD, A web-based personalized decision support tool for patients diagnosed with ductal carcinoma in situ: development, content evaluation, and usability testing., Breast Cancer Res Treat, vol. 192 no. 3 (April, 2022), pp. 517-527 [doi]  [abs]
5. Ryser, MD; Lange, J; Inoue, LYT; O'Meara, ES; Gard, C; Miglioretti, DL; Bulliard, J-L; Brouwer, AF; Hwang, ES; Etzioni, RB, Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort., Ann Intern Med, vol. 175 no. 4 (April, 2022), pp. 471-478 [doi]  [abs]

Recent Grant Support

• Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications, National Institutes of Health, 2022/09-2027/08.      
• Modeling to Minimize Detection Bias in Cancer Risk Prediction Studies, Fred Hutchinson Cancer Center, 2022/04-2024/08.      
• Comparative Modeling for the Prevention and Control of Uterine Cancer, Columbia University, 2021/09-2024/08.      
• Modeling and analytics for cancer diagnostics: traversing the data-evidence divide, Fred Hutchinson Cancer Center, 2022/09-2023/08.      
• The Mathematics of Breast Cancer Overtreatment: Improving Treatment Choice through Effective Communication of Personalized Cancer Risk, National Institutes of Health, 2018/09-2022/08.      
• Modeling to Minimize Detection Bias in Cancer Risk Prediction Studies, Fred Hutchinson Cancer Research Center, 2019/09-2022/03.      
• Molecular and Radiologic Predictors of Invasion in a DCIS Active Surveillance Cohort, Breast Cancer Research Foundation, 2020/10-2021/09.