Michael R. Harowicz

1.0k total citations
19 papers, 697 citations indexed

About

Michael R. Harowicz is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Michael R. Harowicz has authored 19 papers receiving a total of 697 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 5 papers in Cancer Research. Recurrent topics in Michael R. Harowicz's work include Radiomics and Machine Learning in Medical Imaging (14 papers), MRI in cancer diagnosis (7 papers) and AI in cancer detection (7 papers). Michael R. Harowicz is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (14 papers), MRI in cancer diagnosis (7 papers) and AI in cancer detection (7 papers). Michael R. Harowicz collaborates with scholars based in United States and China. Michael R. Harowicz's co-authors include Maciej A. Mazurowski, Ashirbani Saha, Lars J. Grimm, Jeffrey R. Marks, Sujata V. Ghate, Ruth Walsh, Zhe Zhu, Connie E. Kim, P. Kelly Marcom and Ehab A. AlBadawy and has published in prestigious journals such as The American Journal of Cardiology, British Journal of Cancer and Medical Physics.

In The Last Decade

Michael R. Harowicz

16 papers receiving 688 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael R. Harowicz United States 10 533 349 144 84 68 19 697
Pasquale Tamborra Italy 16 486 0.9× 392 1.1× 124 0.9× 95 1.1× 53 0.8× 29 682
Vittorio Didonna Italy 17 520 1.0× 419 1.2× 124 0.9× 89 1.1× 52 0.8× 33 726
Zixiao Lu China 10 380 0.7× 265 0.8× 107 0.7× 70 0.8× 142 2.1× 20 602
Amelie Echle Germany 9 350 0.7× 362 1.0× 115 0.8× 198 2.4× 84 1.2× 10 618
Yini Huang China 10 493 0.9× 266 0.8× 97 0.7× 67 0.8× 41 0.6× 23 665
Lakshmanan Sannachi Canada 18 689 1.3× 322 0.9× 137 1.0× 82 1.0× 42 0.6× 57 834
Chao You China 15 459 0.9× 152 0.4× 142 1.0× 107 1.3× 56 0.8× 44 607
John Maddison United Kingdom 8 295 0.6× 215 0.6× 72 0.5× 189 2.3× 66 1.0× 18 542
Isabel Schobert Germany 9 410 0.8× 167 0.5× 89 0.6× 127 1.5× 31 0.5× 18 694
John Arne Nesheim Norway 5 266 0.5× 204 0.6× 82 0.6× 217 2.6× 49 0.7× 9 477

Countries citing papers authored by Michael R. Harowicz

Since Specialization
Citations

This map shows the geographic impact of Michael R. Harowicz'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 Michael R. Harowicz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael R. Harowicz more than expected).

Fields of papers citing papers by Michael R. Harowicz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael R. Harowicz. 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 Michael R. Harowicz. The network helps show where Michael R. Harowicz may publish in the future.

Co-authorship network of co-authors of Michael R. Harowicz

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

All Works

19 of 19 papers shown
1.
Harrawood, Brian, Mojtaba Zarei, Michael R. Harowicz, et al.. (2025). Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection. Medical Image Analysis. 103. 103576–103576. 2 indexed citations
2.
Harowicz, Michael R., et al.. (2025). The Duke Lung Cancer Screening (DLCS) Dataset: A Reference Dataset of Annotated Low-Dose Screening Thoracic CT. Radiology Artificial Intelligence. 7(4). e240248–e240248.
3.
Harowicz, Michael R., et al.. (2023). Massive Mysteries. The American Journal of Cardiology. 204. 178–182.
4.
Harowicz, Michael R., Amar Shah, & Stefan L. Zimmerman. (2020). Preoperative Planning for Structural Heart Disease. Radiologic Clinics of North America. 58(4). 733–751. 5 indexed citations
5.
Mazurowski, Maciej A., et al.. (2019). Association of distant recurrence‐free survival with algorithmically extracted MRI characteristics in breast cancer. Journal of Magnetic Resonance Imaging. 49(7). e231–e240. 21 indexed citations
6.
Zhu, Zhe, Ehab A. AlBadawy, Ashirbani Saha, et al.. (2019). Deep learning for identifying radiogenomic associations in breast cancer. Computers in Biology and Medicine. 109. 85–90. 122 indexed citations
7.
Zhu, Zhe, Michael R. Harowicz, Jun Zhang, et al.. (2019). Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. Computers in Biology and Medicine. 115. 103498–103498. 42 indexed citations
8.
Saha, Ashirbani, Michael R. Harowicz, Lars J. Grimm, et al.. (2018). A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. British Journal of Cancer. 119(4). 508–516. 187 indexed citations
9.
10.
Saha, Ashirbani, et al.. (2018). A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. Journal of Cancer Research and Clinical Oncology. 144(5). 799–807. 32 indexed citations
11.
Saha, Ashirbani, Michael R. Harowicz, Allison Hall, et al.. (2018). Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival. Breast Cancer Research and Treatment. 172(1). 123–132. 7 indexed citations
12.
13.
Saha, Ashirbani, Michael R. Harowicz, & Maciej A. Mazurowski. (2018). Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors. Medical Physics. 45(7). 3076–3085. 49 indexed citations
14.
Saha, Ashirbani, Michael R. Harowicz, Lars J. Grimm, et al.. (2018). Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation. 15. 6–6.
15.
AlBadawy, Ehab A., Ashirbani Saha, Jun Zhang, et al.. (2018). Breast cancer molecular subtype classification using deep features: preliminary results. 104–104. 7 indexed citations
16.
Harowicz, Michael R., Timothy J. Robinson, Michaela A. Dinan, et al.. (2017). Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset. Breast Cancer Research and Treatment. 162(1). 1–10. 32 indexed citations
17.
Harowicz, Michael R., Ashirbani Saha, Lars J. Grimm, et al.. (2017). Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?. Journal of Magnetic Resonance Imaging. 46(5). 1332–1340. 23 indexed citations
18.
Saha, Ashirbani, Lars J. Grimm, Michael R. Harowicz, et al.. (2016). Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics. Medical Physics. 43(8Part1). 4558–4564. 24 indexed citations
19.
Kinon, Merritt D., Rani Nasser, Jonathan Nakhla, et al.. (2016). Predictive parameters for the antecedent development of hip pathology associated with long segment fusions to the pelvis for the treatment of adult spinal deformity. Surgical Neurology International. 7(1). 93–93. 2 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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