Scholars@Duke

• Background
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  Education, Training, & Certifications

  • M.D., Duke University School of Medicine 2017
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • Alkhunaizi, Fatimah A., Michael R. Harowicz, Catherine G. Ireland, Brian A. Houston, Rachel L. Damico, Todd M. Kolb, Stephen C. Mathai, et al. “Kussmaul's Sign in Pulmonary Hypertension Corresponds With Severe Pulmonary Vascular Pathology Rather Than Right Ventricular Diastolic Dysfunction.” Circ Heart Fail 14, no. 1 (January 2021): e007461. https://doi.org/10.1161/CIRCHEARTFAILURE.120.007461.
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    • Harowicz, Michael R., Amar Shah, and Stefan L. Zimmerman. “Preoperative Planning for Structural Heart Disease.” Radiol Clin North Am 58, no. 4 (July 2020): 733–51. https://doi.org/10.1016/j.rcl.2020.02.005.
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    • Zhu, Zhe, Michael Harowicz, Jun Zhang, Ashirbani Saha, Lars J. Grimm, E Shelley Hwang, and Maciej A. Mazurowski. “Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.” Comput Biol Med 115 (December 2019): 103498. https://doi.org/10.1016/j.compbiomed.2019.103498.
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    • Mazurowski, Maciej A., Ashirbani Saha, Michael R. Harowicz, Elizabeth Hope Cain, Jeffrey R. Marks, and P Kelly Marcom. “Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.” J Magn Reson Imaging 49, no. 7 (June 2019): e231–40. https://doi.org/10.1002/jmri.26648.
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    • Zhu, Zhe, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, and Maciej A. Mazurowski. “Deep learning for identifying radiogenomic associations in breast cancer.” Comput Biol Med 109 (June 2019): 85–90. https://doi.org/10.1016/j.compbiomed.2019.04.018.
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    • Cain, Elizabeth Hope, Ashirbani Saha, Michael R. Harowicz, Jeffrey R. Marks, P Kelly Marcom, and Maciej A. Mazurowski. “Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.” Breast Cancer Res Treat 173, no. 2 (January 2019): 455–63. https://doi.org/10.1007/s10549-018-4990-9.
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    • Saha, Ashirbani, Michael R. Harowicz, Elizabeth Hope Cain, Allison H. Hall, Eun-Sil Shelley Hwang, Jeffrey R. Marks, Paul Kelly Marcom, and Maciej A. Mazurowski. “Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival.” Breast Cancer Res Treat 172, no. 1 (November 2018): 123–32. https://doi.org/10.1007/s10549-018-4879-7.
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    • Saha, Ashirbani, Michael R. Harowicz, Lars J. Grimm, Connie E. Kim, Sujata V. Ghate, Ruth Walsh, and Maciej A. Mazurowski. “A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.” Br J Cancer 119, no. 4 (August 2018): 508–16. https://doi.org/10.1038/s41416-018-0185-8.
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    • Saha, Ashirbani, Michael R. Harowicz, and Maciej A. Mazurowski. “Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.” Med Phys 45, no. 7 (July 2018): 3076–85. https://doi.org/10.1002/mp.12925.
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    • Saha, Ashirbani, Michael R. Harowicz, Weiyao Wang, and Maciej A. Mazurowski. “A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.” J Cancer Res Clin Oncol 144, no. 5 (May 2018): 799–807. https://doi.org/10.1007/s00432-018-2595-7.
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    • Harowicz, Michael R., Ashirbani Saha, Lars J. Grimm, P Kelly Marcom, Jeffrey R. Marks, E Shelley Hwang, and Maciej A. Mazurowski. “Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?” J Magn Reson Imaging 46, no. 5 (November 2017): 1332–40. https://doi.org/10.1002/jmri.25655.
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    • Harowicz, Michael R., Timothy J. Robinson, Michaela A. Dinan, Ashirbani Saha, Jeffrey R. Marks, P Kelly Marcom, and Maciej A. Mazurowski. “Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset.” Breast Cancer Res Treat 162, no. 1 (February 2017): 1–10. https://doi.org/10.1007/s10549-016-4093-4.
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    • Saha, Ashirbani, Lars J. Grimm, Michael Harowicz, Sujata V. Ghate, Connie Kim, Ruth Walsh, and Maciej A. Mazurowski. “Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.” Med Phys 43, no. 8 (August 2016): 4558. https://doi.org/10.1118/1.4955435.
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    • Kinon, Merritt D., Rani Nasser, Jonathan P. Nakhla, Owoicho Adogwa, Jessica R. Moreno, Michael Harowicz, Terence Verla, Reza Yassari, and Carlos A. Bagley. “Predictive parameters for the antecedent development of hip pathology associated with long segment fusions to the pelvis for the treatment of adult spinal deformity.” Surg Neurol Int 7 (2016): 93. https://doi.org/10.4103/2152-7806.192724.
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  • Conference Papers

    • Zhu, Z., E. Albadawy, A. Saha, J. Zhang, M. R. Harowicz, and M. A. Mazurowski. “Breast cancer molecular subtype classification using deep features: Preliminary results.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2295471.
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    • Saha, A., M. R. Harowicz, L. J. Grimm, C. E. Kim, S. V. Ghate, R. Walsh, and M. A. Mazurowski. “Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: A large scale evaluation.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2293207.
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    • Zhu, Z., M. Harowicz, J. Zhang, A. Saha, L. J. Grimm, S. Hwang, and M. A. Mazurowski. “Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: Preliminary data.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2295470.
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    • Harowicz, M. R., J. R. Marks, P. Kelly Marcom, and M. A. Mazurowski. “Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?” In Proceedings of Spie  the International Society for Optical Engineering, Vol. 10327, 2017.
    • Harowicz, M. R., J. R. Marks, P. Kelly Marcom, and M. A. Mazurowski. “Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10136, 2017. https://doi.org/10.1117/12.2255866.
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