Born in 1991, I specialize in artificial intelligence based image analysis of radiological images. My main interests are in developing solutions for precision phenotyping of diseases from radiological images. This may help understand how different risk and genetic factors affect the development and progression of pathologies.

Total citation count: 2050 | Number of research articles: 118 | First/Last authored: 34 | H‐index: 24

RIA: Radiomics image analysis toolbox for 2D and 3D radiological images.

parseRPDR: Parse and Manipulate Research Patient Data Registry (‘RPDR’) Text Queries.

1. Vattay, B., Szilveszter, B., Boussoussou, M., Vecsey‐Nagy, M., Lin, A., Konkoly, G., Kubovje, A., Schwarz, F., Merkely, B., Maurovich‐Horvat, P., Williams, M. C., Dey, D., & Kolossváry, M. (2023). Impact of virtual monoenergetic levels on coronary plaque volume components using photon‐counting computed tomography. Eur Radiol. https://doi.org/10.1007/s00330-023-09876-7
2. Kwiecinski, J., Kolossváry, M., Tzolos, E., Meah, M. N., Adamson, P. D., Joshi, N. V., Williams, M. C., Van Beek, E. J. R., Berman, D. S., Maurovich‐Horvat, P., Newby, D. E., Dweck, M. R., Dey, D., & Slomka, P. J. (2023). Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes. ATVB, ATVBAHA.123.319332. https://doi.org/10.1161/ATVBAHA.123.319332
3. Kolossváry, M., deFilippi, C., McCallum, S., Fitch, K. V., Diggs, M. R., Fulda, E. S., Ribaudo, H. J., Fichtenbaum, C. J., Aberg, J. A., Malves‐ tutto, C. D., Currier, J. S., Casado, J. L., Gutiérrez, F., Sereti, I., Douglas, P. S., Zanni, M. V., & Grinspoon, S. K. (2023). Identification of pre‐ infection markers and differential plasma protein expression following SARS‐CoV‐2 infection in people living with HIV. eBioMedicine, 90, 104538. https://doi.org/10.1016/j.ebiom.2023.104538
4. Kolossváry, M., Raghu, V. K., Nagurney, J. T., Hoffmann, U., & Lu, M. T. (2023). Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome. Radiology, 306(2), e221926. https://doi.org/10.1148/radiol.221926
5. Boussoussou, M., Vattay, B., Szilveszter, B., Simon, J., Lin, A., Vecsey‐Nagy, M., Konkoly, G., Merkely, B., Maurovich‐Horvat, P., Dey, D., & Kolossváry, M. (2023). The effect of patient and imaging characteristics on coronary CT angiography assessed pericoronary adipose tissue attenuation and gradient. Journal of Cardiovascular Computed Tomography, 17(1), 34–42. https://doi.org/10.1016/j.jcct.2022.09.006
6. Kolossváry, M., deFilippi, C., Lu, M. T., Zanni, M. V., Fulda, E. S., Foldyna, B., Ribaudo, H., Mayrhofer, T., Collier, A. C., Bloomfield, G. S., Fichtenbaum, C., Overton, E. T., Aberg, J. A., Currier, J., Fitch, K. V., Douglas, P. S., & Grinspoon, S. K. (2022). Proteomic Signature of Subclinical Coronary Artery Disease in People With HIV: Analysis of the REPRIEVE Mechanistic Substudy. The Journal of Infectious Diseases, 226(10), 1809–1822. https://doi.org/10.1093/infdis/jiac196
7. Szilveszter, B., Vattay, B., Bossoussou, M., Vecsey‐Nagy, M., Simon, J., Merkely, B., Maurovich‐Horvat, P., & Kolossváry, M. (2022). CAD‐RADS may underestimate coronary plaque progression as detected by serial CT angiography. European Heart Journal ‐ Cardiovascular Imaging, 23(11), 1530–1539. https://doi.org/10.1093/ehjci/jeab215
8. Kolossváry, M., Mayrhofer, T., Ferencik, M., Karády, J., Pagidipati, N. J., Shah, S. H., Nanna, M. G., Foldyna, B., Douglas, P. S., Hoffmann, U., & Lu, M. T. (2022). Are risk factors necessary for pretest probability assessment of coronary artery disease? A patient similarity network analysis of the PROMISE trial. Journal of Cardiovascular Computed Tomography, 16(5), 397–403. https://doi.org/10.1016/j.jcct.2022.03.006
9. Kolossváry, M., Bluemke, D. A., Fishman, E. K., Gerstenblith, G., Celentano, D., Mandler, R. N., Khalsa, J., Bhatia, S., Chen, S., Lai, S., & Lai, H. (2022). Temporal assessment of lesion morphology on radiological images beyond lesion volumes—a proof‐of‐principle study. Eur Radiol, 32(12), 8748–8760. https://doi.org/10.1007/s00330-022-08894-1
10. Jávorszky, N., Homonnay, B., Gerstenblith, G., Bluemke, D., Kiss, P., Török, M., Celentano, D., Lai, H., Lai, S., & Kolossváry, M. (2022). Deep learning–based atherosclerotic coronary plaque segmentation on coronary CT angiography. Eur Radiol, 32(10), 7217–7226. https://doi.org/10.1007/s00330-022-08801-8
11. Lin, A., Kolossváry, M., Cadet, S., McElhinney, P., Goeller, M., Han, D., Yuvaraj, J., Nerlekar, N., Slomka, P. J., Marwan, M., Nicholls, S. J., Achenbach, S., Maurovich‐Horvat, P., Wong, D. T. L., & Dey, D. (2022). Radiomics‐Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography. JACC: Cardiovascular Imaging, 15(5), 859–871. https://doi.org/10.1016/j.jcmg.2021.11.016
12. Kolossváry, M., Celentano, D., Gerstenblith, G., Bluemke, D. A., Mandler, R. N., Fishman, E. K., Bhatia, S., Chen, S., Lai, S., & Lai, H. (2021). HIV indirectly accelerates coronary artery disease by promoting the effects of risk factors: Longitudinal observational study. Sci Rep, 11(1), 23110. https://doi.org/10.1038/s41598-021-02556-w
13. Kolossváry, M., Fishman, E. K., Gerstenblith, G., Bluemke, D. A., Mandler, R. N., Celentano, D., Kickler, T. S., Bazr, S., Chen, S., Lai, S., & Lai, H. (2021). Cardiovascular risk factors and illicit drug use may have a more profound effect on coronary atherosclerosis progression in people living with HIV. Eur Radiol, 31(5), 2756–2767. https://doi.org/10.1007/s00330-021-07755-7
14. Kolossváry, M., Gerstenblith, G., Bluemke, D. A., Fishman, E. K., Mandler, R. N., Kickler, T. S., Chen, S., Bhatia, S., Lai, S., & Lai, H. (2021). Contribution of Risk Factors to the Development of Coronary Atherosclerosis as Confirmed via Coronary CT Angiography: A Longitudi‐ nal Radiomics‐based Study. Radiology, 299(1), 97–106. https://doi.org/10.1148/radiol.2021203179
15. Kolossváry, M., Jávorszky, N., Karády, J., Vecsey‐Nagy, M., Dávid, T. Z., Simon, J., Szilveszter, B., Merkely, B., & Maurovich‐Horvat, P. (2021). Effect of vessel wall segmentation on volumetric and radiomic parameters of coronary plaques with adverse characteristics. Journal of Cardiovascular Computed Tomography, 15(2), 137–145. https://doi.org/10.1016/j.jcct.2020.08.001
16. Lin, A., Kolossváry, M., Yuvaraj, J., Cadet, S., McElhinney, P. A., Jiang, C., Nerlekar, N., Nicholls, S. J., Slomka, P. J., Maurovich‐Horvat, P., Wong, D. T. L., & Dey, D. (2020). Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype. JACC: Cardiovascular Imaging, 13(11), 2371–2383. https://doi.org/10.1016/j.jcmg.2020.06.033
17. Simon, J., Száraz, L., Szilveszter, B., Panajotu, A., Jermendy, Á., Bartykowszki, A., Boussoussou, M., Vattay, B., Drobni, Z. D., Merkely, B., Maurovich‐Horvat, P., & Kolossváry, M. (2020). Calcium scoring: A personalized probability assessment predicts the need for additional or alternative testing to coronary CT angiography. Eur Radiol, 30(10), 5499–5506. https://doi.org/10.1007/s00330-020-06921-7
18. Kolossváry, M., Szilveszter, B., Karády, J., Drobni, Z. D., Merkely, B., & Maurovich‐Horvat, P. (2019). Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques. Journal of Cardiovascular Computed Tomography, 13(6), 325– 330. https://doi.org/10.1016/j.jcct.2018.11.004
19. Kolossváry, M., Park, J., Bang, J.‐I., Zhang, J., Lee, J. M., Paeng, J. C., Merkely, B., Narula, J., Kubo, T., Akasaka, T., Koo, B.‐K., & Maurovich‐ Horvat, P. (2019). Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography. European Heart Journal ‐ Cardiovascular Imaging, 20(11), 1250–1258. https://doi.org/10.1093/ehjci/jez033
20. Kolossváry, M., Karády, J., Kikuchi, Y., Ivanov, A., Schlett, C. L., Lu, M. T., Foldyna, B., Merkely, B., Aerts, H. J., Hoffmann, U., & Maurovich‐ Horvat, P. (2019). Radiomics versus Visual and Histogram‐based Assessment to Identify Atheromatous Lesions at Coronary CT Angiog‐ raphy: An ex Vivo Study. Radiology, 293(1), 89–96. https://doi.org/10.1148/radiol.2019190407
21. Kolossváry, M., Karády, J., Szilveszter, B., Kitslaar, P., Hoffmann, U., Merkely, B., & Maurovich‐Horvat, P. (2017). Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin‐Ring Sign. Circ: Cardiovascular Imaging, 10(12), e006843. https://doi.org/10.1161/CIRCIMAGING.117.006843
22. Kolossváry, M., Székely, A. D., Gerber, G., Merkely, B., & Maurovich‐Horvat, P. (2017). CT Images Are Noninferior to Anatomic Specimens in Teaching Cardiac Anatomy—A Randomized Quantitative Study. Journal of the American College of Radiology, 14(3), 409–415.e2. https://doi.org/10.1016/j.jacr.2016.09.050
23. Kolossváry, M., Szilveszter, B., Édes, I. F., Nardai, S., Voros, V., Hartyánszky, I., Merkely, B., Voros, S., & Maurovich‐Horvat, P. (2016). Comparison of Quantity of Coronary Atherosclerotic Plaques Detected by Computed Tomography Versus Angiography. The American Journal of Cardiology, 117(12), 1863–1867. https://doi.org/10.1016/j.amjcard.2016.03.031

1. Kolossváry, M., De Cecco, C. N., Feuchtner, G., & Maurovich‐Horvat, P. (2019). Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. Journal of Cardiovascular Computed To‐ mography, 13(5), 274–280. https://doi.org/10.1016/j.jcct.2019.04.007
2. Kolossváry, M., Kellermayer, M., Merkely, B., & Maurovich‐Horvat, P. (2018). Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging, 33(1), 26–34. https://doi.org/10.1097/RTI.0000000000000268
3. Kolossváry, M., Szilveszter, B., Merkely, B., & Maurovich‐Horvat, P. (2017). Plaque imaging with CT—a com‐ prehensive review on coronary CT angiography based risk assessment. Cardiovasc. Diagn. Ther., 7(5), 489– 506. https://doi.org/10.21037/cdt.2016.11.06

1. Pontone, G., Mushtaq, S., Al’Aref, S. J., Andreini, D., Baggiano, A., Canan, A., Cavalcante, J. L., Chelliah, A., Chen, M., Choi, A., Damini, D., De Cecco, C. N., Farooqi, K. M., Ferencik, M., Feuchtner, G., Hecht, H., Gransar, H., Kolossváry, M., Leipsic, J., ... Arbab‐Zadeh, A. (2023). The journal of cardiovascular computed tomography: A year in review: 2022. Journal of Cardiovascular Computed Tomography, S1934592523000862. https://doi.org/10.1016/j.jcct.2023.03.001
2. Kolossváry, M., Reid, A. B., Baggiano, A., Nagpal, P., Canan, A., Al’Aref, S. J., Andreini, D., Cavalcante, J. L., Cecco, C. N. de, Chelliah, A., Chen, M. Y., Choi, A. D., Dey, D., Fairbairn, T., Ferencik, M., Gransar, H., Hecht, H., Leipsic, J., Lu, M. T., ... Villines, T. C. (2022). The Journal of cardiovascular computed tomography: A year in review 2021. Journal of Cardiovascular Computed Tomography, 16(3), 266–276. https://doi.org/10.1016/j.jcct.2022.03.004
3. Kolossváry, M. (2022). AI Can Evaluate Cardiac Ultrasounds. JACC: Cardiovascular Imaging, 15(4), 564–565. https://doi.org/10.1016/j.jcmg.2021.09.028
4. Kolossváry, M., & Dey, D. (2022). Editorial: Radiomics in Cardiovascular Imaging. Front. Cardiovasc. Med., 9, 876713. https://doi.org/10.3389/fcvm.2022.876713
5. Kolossváry, M., & Maurovich‐Horvat, P. (2019). Radiomics: The Link Between Radiology and Histology? Circ: Cardiovascular Imaging, 12(11), e009990. https://doi.org/10.1161/CIRCIMAGING.119.009990
6. Maurovich‐Horvat, P., Tárnoki, D. L., Tárnoki, Á. D., Horváth, T., Jermendy, Á. L., Kolossváry, M., Szilveszter, B., Voros, V., Kovács, A., Molnár, A. Á., Littvay, L., Lamb, H. J., Voros, S., Jermendy, G., & Merkely, B. (2015). Rationale, Design, and Methodological Aspects of the BUDAPEST‐GLOBAL Study (Burden of Atherosclerotic Plaques Study in Twins‐Genetic Loci and the Burden of Atherosclerotic Lesions): Aspects of the BUDAPEST‐ GLOBAL study. Clin Cardiol, 38(12), 699–707. https://doi.org/10.1002/clc.22482

1. Kolossváry, M. (2022). Atherosclerotic plaque imaging. In O. Gaemperli, P. Maurovich‐ Horvat, K. Nieman, G. Pontone, & F. Pugliese (Eds.), EACVI Handbook of Cardiovascular CT (1st ed., pp. 137–144). Oxford University PressOxford. https://academic.oup.com/book/44817/chapter/382563141
2. Kolossváry, M. (2022). Artificial intelligence in cardiac CT. In O. Gaemperli, P. Maurovich‐ Horvat, K. Nieman, G. Pontone, & F. Pugliese (Eds.), EACVI Handbook of Cardiovascular CT (1st ed., pp. 349–C3.16.S7). Oxford University PressOxford. https://academic.oup.com/book/44817/chapter/382564414
3. Kolossváry, M., & Maurovich‐Horvat, P. (2022). Radiomics in Cardiac CT. In C. N. De Cecco, M. van Assen, & T. Leiner (Eds.), Artificial Intelligence in Cardiothoracic Imaging (pp. 305–311). Springer International Publish‐ ing. https://link.springer.com/10.1007/978-3-030-92087-6_31
4. Kolossváry, M., & Maurovich‐Horvat, P. (2019). Cardiac CT Radiomics. In U. J. Schoepf (Ed.), CT of the Heart (pp. 715–724). Humana Press. http://link.springer.com/10.1007/978-1-60327-237-7_56

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