cardiotomskСибирский журнал клинической и экспериментальной медициныSiberian Journal of Clinical and Experimental Medicine2713-29272713-265XTSU publishing10.29001/2073-8552-2023-39-3-13-22cardiotomsk-1932Research ArticleОБЗОРЫ И ЛЕКЦИИREVIEWS AND LECTURESРадиомический анализ магнитно-резонансных изображений сердца: обзор литературыCardiac MRI Radiomics: reviewhttps://orcid.org/0000-0002-4871-3283МаксимоваА. С.MaksimovaA. S.

Максимова Александра Сергеевна, канд. мед. наук, научный сотрудник, отделение рентгеновских и томографических методов диагностики

634012, Российская Федерация, Томск, ул. Киевская, 111а

Aleksandra S. Maksimova, M.D., Cand. Sci. (Med.), Research Scientist, Department of Radiology and Tomography

111a, Kievskaya str., Tomsk, 634012, Russian Federation

asmaximova@yandex.ruhttps://orcid.org/0000-0002-7352-6068УсовВ. Ю.UssovW. Yu.

Усов Владимир Юрьевич, д-р мед. наук, профессор, ведущий научный сотрудник, отделение рентгеновских и томографических методов диагностики

634012, Российская Федерация, Томск, ул. Киевская, 111а

Wladimir Yu. Ussov, Dr. Sci. (Med.), Leading Research Scientist, Department of Radiology and Tomography

111a, Kievskaya str., Tomsk, 634012, Russian Federation

ussov1962@yandex.ruhttps://orcid.org/0000-0003-1367-5309ШелковниковаТ. А.ShelkovnikovaT. A.

Шелковникова Татьяна Александровна, канд. мед. наук, старший научный сотрудник, отделение рентгеновских и томографических методов диагностики

634012, Российская Федерация, Томск, ул. Киевская, 111а

Tatyana A. Shelkovnikova, M.D., Cand. Sci. (Med.), Senior Research Scientist, Department of Radiology and Tomography

111a, Kievskaya str., Tomsk, 634012, Russian Federation

fflly@mail.ruhttps://orcid.org/0000-0002-7502-7502МочулаО. В.MochulaO. V.

Мочула Ольга Витальевна, канд. мед. наук, научный сотрудник, отделение рентгеновских и томографических методов диагностики

634012, Российская Федерация, Томск, ул. Киевская, 111а

Olga V. Mochula, M.D., Cand. Sci. (Med.), Research Scientist, Department of Radiology and Tomography

111a, Kievskaya str., Tomsk, 634012, Russian Federation

mochula.olga@gmail.comhttps://orcid.org/0000-0002-6158-026XРюмшинаН. И.RyumshinaN. I.

Рюмшина Надежда Игоревна, канд. мед. наук, научный сотрудник, отделение рентгеновских и томографических методов диагностики

634012, Российская Федерация, Томск, ул. Киевская, 111а

Nadezhda I. Ryumshina, M.D., Cand. Sci. (Med.), Research Scientist, Department of Radiology and Tomography

111a, Kievskaya str., Tomsk, 634012, Russian Federation

n.rumshina@list.ruhttps://orcid.org/0000-0003-4807-3762СухареваА. Е.SykharevaA. E.

Сухарева Анна Евгеньевна, канд. мед. наук, научный сотрудник, отделение рентгеновских и томографических методов диагностики

634012, Российская Федерация, Томск, ул. Киевская, 111а

Anna E. Sykhareva, M.D., Cand. Sci. (Med.), Junior Research Scientist, Department of Radiology and Tomography

111a, Kievskaya str., Tomsk, 634012, Russian Federation

doctor-anyuta@mail.ruhttps://orcid.org/0000-0002-1513-8614ЗавадовскийК. В.ZavadovskyK. V.

Завадовский Константин Валерьевич, д-р мед. наук, заведующий отделом лучевой диагностики

634012, Российская Федерация, Томск, ул. Киевская, 111а

Konstantin V. Zavadovsky, Dr. Sci. (Med.), Head of the Department of Radiation Diagnostics

111a, Kievskaya str., Tomsk, 634012, Russian Federation

konstz@cardio-tomsk.ruНаучно-исследовательский институт кардиологии, Томский национальный исследовательский медицинский центр Российской академии наукРоссияCardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of SciencesRussian Federation2023101020233831322Copyright © Максимова А.С., Усов В.Ю., Шелковникова Т.А., Мочула О.В., Рюмшина Н.И., Сухарева А.Е., Завадовский К.В., 20232023Максимова А.С., Усов В.Ю., Шелковникова Т.А., Мочула О.В., Рюмшина Н.И., Сухарева А.Е., Завадовский К.В.Maksimova A.S., Ussov W.Y., Shelkovnikova T.A., Mochula O.V., Ryumshina N.I., Sykhareva A.E., Zavadovsky K.V.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.sibjcem.ru/jour/article/view/1932

Радиомика и текстурный анализ (ТА) – это новое, бурно развивающееся направление углубленного анализа цифровых медицинских изображений. Число публикаций по данной тематике растет с каждым годом, и данная тема не теряет своей актуальности. Радиомика представляет собой многообещающий метод анализа изображений, который направлен на то, чтобы улучшить диагностику и прогноз заболеваний за счет извлечения большого числа количественных признаков, которые могут быть пропущены человеческим глазом при визуальном анализе изображений. Биомаркеры радиомики, полученные путем извлечения данных из магнитно-резонансных изображений сердца, могут стать ценным инструментом для оценки жизнеспособности миокарда, поражения миокарда при миокардитах и кардиомиопатиях. Проанализированы возможности применения ТА изображений магнитно-резонансной томографии (МРТ) сердца в клинической практике, описаны известные на сегодняшний день особенности, преимущества и ограничения применения ТА и радиомики в диагностике заболеваний сердца, а именно инфаркта миокарда (ИМ), миокардита и кардиомиопатии.

A study of foreign and domestic literature devoted to the application of texture analysis of magnetic resonance images of the heart was performed. The analysis included publications selected by key words and their combinations: cardiac magnetic resonance imaging (MRI), myocarditis, myocardial infarction, cardiomyopathy, radiomics, and texture analysis. Radiomics and texture analysis, as a new and rapidly developing direction of in-depth analysis of digital medical images, is developing, the number of publications on this topic is growing every year and the topic is not losing its relevance. Radiomics is a promising method of image analysis that aims to improve the diagnosis and prognosis of diseases by extracting a large number of quantitative features that can be missed by the human eye in the visual analysis of images. Radiomics biomarkers derived by extracting data from magnetic resonance images of the heart could be a valuable tool for assessing myocardial viability, myocardial lesions in myocarditis and cardiomyopathies.

магнитно-резонансная томография сердцамиокардитинфаркт миокардакардиомиопатиярадиомикатекстурный анализcardiac MRImyocarditismyocardial infarctioncardiomyopathyradiomicstextural analysisReferencesMayerhoefer M.E., Materka A., Langs G., Häggström I., Szczypiński P., Gibbs P. et al. Introduction to Radiomics. J. Nucl. Med. 2020;61(4):488–495. DOI: 10.2967/jnumed.118.222893.Mayerhoefer M.E., Materka A., Langs G., Häggström I., Szczypiński P., Gibbs P. et al. Introduction to Radiomics. J. Nucl. Med. 2020;61(4):488–495. DOI: 10.2967/jnumed.118.222893.van Timmeren J.E., Cester D., Tanadini-Lang S., Alkadhi H., Baessler B. Radiomics in medical imaging-“how-to” guide and critical refl ection. Insights Imaging. 2020;11(1):91. DOI: 10.1186/s13244-020-00887-2.van Timmeren J.E., Cester D., Tanadini-Lang S., Alkadhi H., Baessler B. Radiomics in medical imaging-“how-to” guide and critical refl ection. Insights Imaging. 2020;11(1):91. DOI: 10.1186/s13244-020-00887-2.Murray J.M., Kaissis G., Braren R., Kleesiek J. Wie funktioniert Radiomics? [A primer on radiomics]. Radiologe. 2020;60(1):32–41. (In German). DOI: 10.1007/s00117-019-00617-w.Murray J.M., Kaissis G., Braren R., Kleesiek J. Wie funktioniert Radiomics? [A primer on radiomics]. Radiologe. 2020;60(1):32–41. (In German). DOI: 10.1007/s00117-019-00617-w.Avanzo M., Stancanello J., Pirrone G., Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020;196(10):879–887. DOI: 10.1007/s00066-020-01625-9.Avanzo M., Stancanello J., Pirrone G., Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020;196(10):879–887. DOI: 10.1007/s00066-020-01625-9.Огнерубов Н.А., Шатов А.В., Шатов И.А. Радиогеномика и радиомика в диагностике злокачественных опухолей: обзор литературы. Вестник Тамбовского университета. Серия: Естественные и технические науки. 2017;22(6–2):1453–1460. DOI: 10.20310/1810-0198-2017-22-6-1453-1460.Ognerubov N.A., Shatov A.V., Shatov I.A. Radiogenomics and radiomics in the diagnostics of malignant tumours: a literary review. Tambov University Reports. Series: Natural and Technical Sciences. 2017;22(6–2):1453–1460. (In Russ.) DOI: 10.20310/1810-0198-2017-22-6-1453-1460.Литвин А.А., Буркин Д.А., Кропинов А.А., Парамзин Ф.Н. Радиомика и анализ текстур цифровых изображений в онкологии (обзор). Современные технологии в медицине. 2021;13(2):97–106. DOI: 10.17691/stm2021.13.2.11.Litvin A.A., Burkin D.A., Kropinov A.A., Paramzin F.N. Radiomics and digital image texture analysis in oncology (review). Modern Technologies in Medicine. 2021;13(2):97–106. (In Russ.) DOI: 10.17691/stm2021.13.2.11.Замятина К.А., Годзенко М.В., Кармазановский Г.Г., Ревишвили А.Ш. Радиомика при заболеваниях печени и поджелудочной железы. Обзор литературы. Анналы хирургической гепатологии. 2022;27(1):40–47. DOI: 10.16931/1995-5464.2022-1-40-47.Zamyatina K.A., Godzenko M.V., Kаrmаzаnovsky G.G., Revishvili A.S. Radiomics in liver and pancreatic disorders: a review. Annaly khirurgicheskoy gepatologii = Annals of HPB Surgery. 2022;27(1):40–47. (In Russ.) DOI: 10.16931/1995-5464.2022-1-40-47.Shur J.D., Doran S.J., Kumar S., Ap Dafydd D., Downey K., O’Connor J.P.B. et al. Radiomics in oncology: A practical guide. Radiographics. 2021;41(6):1717–1732. DOI: 10.1148/rg.2021210037.Shur J.D., Doran S.J., Kumar S., Ap Dafydd D., Downey K., O’Connor J.P.B. et al. Radiomics in oncology: A practical guide. Radiographics. 2021;41(6):1717–1732. DOI: 10.1148/rg.2021210037.Salvatore C., Castiglioni I., Cerasa A. Radiomics approach in the neurodegenerative brain. Aging Clin. Exp. Res. 2021;33(6):1709–1711. DOI: 10.1007/s40520-019-01299-z.Salvatore C., Castiglioni I., Cerasa A. Radiomics approach in the neurodegenerative brain. Aging Clin. Exp. Res. 2021;33(6):1709–1711. DOI: 10.1007/s40520-019-01299-z.Feng Q., Ding Z. MRI Radiomics classification and prediction in Alzheimer’s disease and mild cognitive impairment: A review. Curr. Alzheimer Res. 2020;17(3):297–309. DOI: 10.2174/1567205017666200303105016.Feng Q., Ding Z. MRI Radiomics classification and prediction in Alzheimer’s disease and mild cognitive impairment: A review. Curr. Alzheimer Res. 2020;17(3):297–309. DOI: 10.2174/1567205017666200303105016.Pinamonti B., Picano E., Ferdeghini E.M., Lattanzi F., Slavich G., Landini L. et al. Quantitative texture analysis in two-dimensional echocardiography: application to the diagnosis of myocardial amyloidosis. J. Am. Coll. Cardiol. 1989;14(3):666–671. DOI: 10.1016/0735-1097(89)90108-3.Pinamonti B., Picano E., Ferdeghini E.M., Lattanzi F., Slavich G., Landini L. et al. Quantitative texture analysis in two-dimensional echocardiography: application to the diagnosis of myocardial amyloidosis. J. Am. Coll. Cardiol. 1989;14(3):666–671. DOI: 10.1016/0735-1097(89)90108-3.Ferdeghini E.M., Pinamonti B., Picano E., Lattanzi F., Bussani R., Slavich G. et al. Quantitative texture analysis in echocardiography: application to the diagnosis of myocarditis. J. Clin. Ultrasound. 1991;19(5):263–270. DOI: 10.1002/jcu.1870190503.Ferdeghini E.M., Pinamonti B., Picano E., Lattanzi F., Bussani R., Slavich G. et al. Quantitative texture analysis in echocardiography: application to the diagnosis of myocarditis. J. Clin. Ultrasound. 1991;19(5):263–270. DOI: 10.1002/jcu.1870190503.Lattanzi F., Bellotti P., Picano E., Chiarella F., Paterni M., Forni G. et al. Quantitative texture analysis in two-dimensional echocardiography: Application to the diagnosis of myocardial hemochromatosis. Echocardiography. 1996;13(1):9–20. DOI: 10.1111/j.1540-8175.1996.tb00863.x.Lattanzi F., Bellotti P., Picano E., Chiarella F., Paterni M., Forni G. et al. Quantitative texture analysis in two-dimensional echocardiography: Application to the diagnosis of myocardial hemochromatosis. Echocardiography. 1996;13(1):9–20. DOI: 10.1111/j.1540-8175.1996.tb00863.x.Kagiyama N., Shrestha S., Cho J.S., Khalil M., Singh Y., Challa A. et al. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine. 2020;54:102726. DOI: 10.1016/j.ebiom.2020.102726.Kagiyama N., Shrestha S., Cho J.S., Khalil M., Singh Y., Challa A. et al. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine. 2020;54:102726. DOI: 10.1016/j.ebiom.2020.102726.Amichi A., Laugier P. Quantitative texture analysis and transesophageal echocardiography to characterize the acute myocardial contusion. Open Med. Inform. J. 2009;3:13–18. DOI: 10.2174/1874431100903010013.Amichi A., Laugier P. Quantitative texture analysis and transesophageal echocardiography to characterize the acute myocardial contusion. Open Med. Inform. J. 2009;3:13–18. DOI: 10.2174/1874431100903010013.Li L., Hu X., Tao X., Shi X., Zhou W., Hu H. Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard. Eur. J. Radiol. 2021;140:109769. DOI: 10.1016/j.ejrad.2021.109769.Li L., Hu X., Tao X., Shi X., Zhou W., Hu H. Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard. Eur. J. Radiol. 2021;140:109769. DOI: 10.1016/j.ejrad.2021.109769.Shang J., Guo Y., Ma Y., Hou Y. Cardiac computed tomography radiomics: a narrative review of current status and future directions. Quant. Imaging Med. Surg. 2022;12(6):3436–3453. DOI: 10.21037/qims-21-1022.Shang J., Guo Y., Ma Y., Hou Y. Cardiac computed tomography radiomics: a narrative review of current status and future directions. Quant. Imaging Med. Surg. 2022;12(6):3436–3453. DOI: 10.21037/qims-21-1022.Yunus M.M., Mohamed Yusof A.K., Ab Rahman M.Z., Koh X.J., Sabarudin A., Nohuddin P.N.E. et al. Automated classification of atherosclerotic radiomics features in coronary computed tomography angiography (CCTA). Diagnostics (Basel). 2022;12(7):1660. DOI: 10.3390/diagnostics12071660.Yunus M.M., Mohamed Yusof A.K., Ab Rahman M.Z., Koh X.J., Sabarudin A., Nohuddin P.N.E. et al. Automated classification of atherosclerotic radiomics features in coronary computed tomography angiography (CCTA). Diagnostics (Basel). 2022;12(7):1660. DOI: 10.3390/diagnostics12071660.Oikonomou E.K., Williams M.C., Kotanidis C.P., Desai M.Y., Marwan M., Antonopoulos A.S. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur. Heart J. 2019;40(43):3529–3543. DOI: 10.1093/eurheartj/ehz592.Oikonomou E.K., Williams M.C., Kotanidis C.P., Desai M.Y., Marwan M., Antonopoulos A.S. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur. Heart J. 2019;40(43):3529–3543. DOI: 10.1093/eurheartj/ehz592.Hu G.Q., Ge Y.Q., Hu X.K., Wei W. Predicting coronary artery calcified plaques using perivascular fat CT radiomics features and clinical risk factors. BMC Med. Imaging. 2022;22(1):134. DOI: 10.1186/s12880-022-00858-7.Hu G.Q., Ge Y.Q., Hu X.K., Wei W. Predicting coronary artery calcified plaques using perivascular fat CT radiomics features and clinical risk factors. BMC Med. Imaging. 2022;22(1):134. DOI: 10.1186/s12880-022-00858-7.Попов Е.В., Анашбаев Ж.Ж., Мальцева А.Н., Сазонова С.И. Радиомические характеристики текстурных изменений эпикардиальной жировой ткани при атеросклеротическом поражении коронарных артерий. Комплексные проблемы сердечно-сосудистых заболеваний. 2021;10(4):6–16. DOI: 10.17802/2306-1278-2021-10-4-6-16.Popov E.V., Anashbaev Z.Z., Maltseva A.N., Sazonova S.I. Radiomic features of epicardial adipose tissue in coronary atherosclerosis. Complex Issues of Cardiovascular Diseases. 2021;10(4):6–16. (In Russ.) DOI: 10.17802/2306-1278-2021-10-4-6-16.Leiner T. Radiomics in cardiac MRI: Sisyphean struggle or close to the summit of Olympus? Radiol. Cardiothorac. Imaging. 2020;25;2(3):e200244. DOI: 10.1148/ryct.2020200244.Leiner T. Radiomics in cardiac MRI: Sisyphean struggle or close to the summit of Olympus? Radiol. Cardiothorac. Imaging. 2020;25;2(3):e200244. DOI: 10.1148/ryct.2020200244.Chang S., Han K., Suh Y.J., Choi B.W. Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review. Eur. Radiol. 2022;32(7):4361–4373. DOI: 10.1007/s00330-022-08587-9.Chang S., Han K., Suh Y.J., Choi B.W. Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review. Eur. Radiol. 2022;32(7):4361–4373. DOI: 10.1007/s00330-022-08587-9.Jang J., Ngo L.H., Mancio J., Kucukseymen S., Rodriguez J., Pierce P. et al. Reproducibility of segmentation-based myocardial radiomic features with cardiac MRI. Radiol. Cardiothorac. Imaging. 2020;2(3):e190216. DOI: 10.1148/ryct.2020190216.Jang J., Ngo L.H., Mancio J., Kucukseymen S., Rodriguez J., Pierce P. et al. Reproducibility of segmentation-based myocardial radiomic features with cardiac MRI. Radiol. Cardiothorac. Imaging. 2020;2(3):e190216. DOI: 10.1148/ryct.2020190216.Baessler B., Mannil M., Maintz D., Alkadhi H., Manka R. Texture analysis and machine learning of noncontrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results. Eur. J. Radiol. 2018;102:61–67. DOI: 10.1016/j.ejrad.2018.03.013.Baessler B., Mannil M., Maintz D., Alkadhi H., Manka R. Texture analysis and machine learning of noncontrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results. Eur. J. Radiol. 2018;102:61–67. DOI: 10.1016/j.ejrad.2018.03.013.Alis D., Guler A., Yergin M., Asmakutlu O. Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI. Diagn. Interv. Imaging. 2020;101:137–146. DOI: 10.1016/j.diii.2019.10.005.Alis D., Guler A., Yergin M., Asmakutlu O. Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI. Diagn. Interv. Imaging. 2020;101:137–146. DOI: 10.1016/j.diii.2019.10.005.Baessler B., Luecke C., Lurz J., Klingel K., von Roeder M., de Waha S. et al. Cardiac MRI texture analysis of T1 and T2 maps in patients with infarctlike acute myocarditis. Radiology. 2018;289(2):357–365. DOI: 10.1148/radiol.2018180411.Baessler B., Luecke C., Lurz J., Klingel K., von Roeder M., de Waha S. et al. Cardiac MRI texture analysis of T1 and T2 maps in patients with infarctlike acute myocarditis. Radiology. 2018;289(2):357–365. DOI: 10.1148/radiol.2018180411.Hassani C., Saremi F., Varghese B.A, Duddalwar V. Myocardial radiomics in cardiac MRI. AJR Am. J. Roentgenol. 2020;214(3):536–545. DOI: 10.2214/AJR.19.21986.Hassani C., Saremi F., Varghese B.A, Duddalwar V. Myocardial radiomics in cardiac MRI. AJR Am. J. Roentgenol. 2020;214(3):536–545. DOI: 10.2214/AJR.19.21986.Koçak B., Durmaz E.Ş., Ateş E., Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn. Interv. Radiol. 2019;25(6):485–495. DOI: 10.5152/dir.2019.19321.Koçak B., Durmaz E.Ş., Ateş E., Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn. Interv. Radiol. 2019;25(6):485–495. DOI: 10.5152/dir.2019.19321.Buch K., Kuno H., Qureshi M.M., Li B., Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J. Appl. Clin. Med. Phys. 2018;19(6):253–264. DOI: 10.1002/acm2.12482.Buch K., Kuno H., Qureshi M.M., Li B., Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J. Appl. Clin. Med. Phys. 2018;19(6):253–264. DOI: 10.1002/acm2.12482.Florez E., Fatemi A., Claudio P.P., Howard C.M. Emergence of radiomics: Novel methodology identifying imaging biomarkers of disease in diagnosis, response, and progression. SM J. Clin. Med. Imaging. 2018;4(1):1019.Florez E., Fatemi A., Claudio P.P., Howard C.M. Emergence of radiomics: Novel methodology identifying imaging biomarkers of disease in diagnosis, response, and progression. SM J. Clin. Med. Imaging. 2018;4(1):1019.Scapicchio C., Gabelloni M., Barucci A., Cioni D., Saba L., Neri E. A deep look into radiomics. Radiol. Med. 2021;126(10):1296–1311. DOI: 10.1007/s11547-021-01389-x.Scapicchio C., Gabelloni M., Barucci A., Cioni D., Saba L., Neri E. A deep look into radiomics. Radiol. Med. 2021;126(10):1296–1311. DOI: 10.1007/s11547-021-01389-x.Rizzo S., Botta F., Raimondi S., Origgi D., Fanciullo C., Morganti A.G. et al. Radiomics: the facts and the challenges of image analysis. Eur. Radiol. Exp. 2018;2:36. DOI: 10.1186/s41747-018-0068-z.Rizzo S., Botta F., Raimondi S., Origgi D., Fanciullo C., Morganti A.G. et al. Radiomics: the facts and the challenges of image analysis. Eur. Radiol. Exp. 2018;2:36. DOI: 10.1186/s41747-018-0068-z.Aerts H., Velazquez E., Leijenaar R.T.H., Parmar C., Grossmann P., Carvalho S. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014;5:4006. DOI: 10.1038/ncomms5006.Aerts H., Velazquez E., Leijenaar R.T.H., Parmar C., Grossmann P., Carvalho S. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014;5:4006. DOI: 10.1038/ncomms5006.Parmar C., Grossmann P., Bussink J., Lambin P., Aerts H.J.W.L. Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 2015;5:13087. DOI: 10.1038/srep13087.Parmar C., Grossmann P., Bussink J., Lambin P., Aerts H.J.W.L. Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 2015;5:13087. DOI: 10.1038/srep13087.Jolliffe I. Principal component analysis. In: Encyclopedia of Statistics in Behavioral Science. Wiley StatsRef: Stastistics Reference Online. 2005:501. DOI: 10.1002/0470013192.bsa501.Jolliffe I. Principal component analysis. In: Encyclopedia of Statistics in Behavioral Science. Wiley StatsRef: Stastistics Reference Online. 2005:501. DOI: 10.1002/0470013192.bsa501.Rau A., Soschynski M., Taron J., Ruile P., Schlett C.L., Bamberg F. et al. Künstliche Intelligenz und Radiomics: Stellenwert in der kardialen MRT [Artificial intelligence and radiomics: Value in cardiac MRI]. Radiologie (Heidelb.). 2022;62(11):947–953. (In German). DOI: 10.1007/s00117-022-01060-0.Rau A., Soschynski M., Taron J., Ruile P., Schlett C.L., Bamberg F. et al. Künstliche Intelligenz und Radiomics: Stellenwert in der kardialen MRT [Artificial intelligence and radiomics: Value in cardiac MRI]. Radiologie (Heidelb.). 2022;62(11):947–953. (In German). DOI: 10.1007/s00117-022-01060-0.Арутюнов Г.П., Палеев Ф.Н., Моисеева О.М., Драгунов Д.О., Соколова А.В., Арутюнов А.Г. и др. Миокардиты у взрослых. Клинические рекомендации 2020. Российский кардиологический журнал. 2021;26(11):4790. DOI: 10.15829/1560-4071-2021-4790.Arutyunov G.P., Paleev F.N., Moiseeva O.M., Dragunov D.O., Sokolova A.V., Arutyunov A.G. et al. 2020 Clinical practice guidelines for Myocarditis in adults. Russian Journal of Cardiology. 2021;26(11):4790. (In Russ.) DOI: 10.15829/1560-4071-2021-4790.Baessler B., Luecke C., Lurz J., Klingel K., Das A., von Roeder M. et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology. 2019:292(3):608–617. DOI: 10.1148/radiol.2019190101.Baessler B., Luecke C., Lurz J., Klingel K., Das A., von Roeder M. et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology. 2019:292(3):608–617. DOI: 10.1148/radiol.2019190101.Di Noto T., von Spiczak J., Mannil M., Gantert E., Soda P., Manka R. et al. Radiomics for distinguishing myocardial infarction from myocarditis at late gadolinium enhancement at MRI: Comparison with subjective visual analysis. Radiol. Cardiothorac. Imaging. 2019;1(5):e180026. DOI: 10.1148/ryct.2019180026.Di Noto T., von Spiczak J., Mannil M., Gantert E., Soda P., Manka R. et al. Radiomics for distinguishing myocardial infarction from myocarditis at late gadolinium enhancement at MRI: Comparison with subjective visual analysis. Radiol. Cardiothorac. Imaging. 2019;1(5):e180026. DOI: 10.1148/ryct.2019180026.McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Böhm M. et al. ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 2021;42(36):3599–3726. DOI: 10.1093/eurheartj/ehab368.McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Böhm M. et al. ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 2021;42(36):3599–3726. DOI: 10.1093/eurheartj/ehab368.Belloni E., De Cobelli F., Esposito A., Mellone R., Perseghin G., Canu T. et al. MRI of cardiomyopathy. AJR Am. J. Roentgenol. 2008;191(6):1702–1710. DOI: 10.2214/AJR.07.3997.Belloni E., De Cobelli F., Esposito A., Mellone R., Perseghin G., Canu T. et al. MRI of cardiomyopathy. AJR Am. J. Roentgenol. 2008;191(6):1702–1710. DOI: 10.2214/AJR.07.3997.Amano Y., Yanagisawa F., Omori Y., Suzuki Y., Ando C., Yamamoto H. et al. Detection of myocardial tissue alterations in hypertrophic cardiomyopathy using texture analysis of T2-weighted short inversion time inversion recovery magnetic resonance imaging. J. Comput. Assist. Tomogr. 2020;44(3):341–345. DOI: 10.1097/RCT.0000000000001007.Amano Y., Yanagisawa F., Omori Y., Suzuki Y., Ando C., Yamamoto H. et al. Detection of myocardial tissue alterations in hypertrophic cardiomyopathy using texture analysis of T2-weighted short inversion time inversion recovery magnetic resonance imaging. J. Comput. Assist. Tomogr. 2020;44(3):341–345. DOI: 10.1097/RCT.0000000000001007.Schofield R., Ganeshan B., Fontana M., Nasis A., Castelletti S., Rosmini S. et al. Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy. Clin. Radiol. 2019;74(2):140–149. DOI: 10.1016/j.crad.2018.09.016.Schofield R., Ganeshan B., Fontana M., Nasis A., Castelletti S., Rosmini S. et al. Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy. Clin. Radiol. 2019;74(2):140–149. DOI: 10.1016/j.crad.2018.09.016.Izquierdo C., Casas G., Martin-Isla C., Campello V.M., Guala A., Gkontra P. et al. Radiomics-based classification of left ventricular non-compaction, hypertrophic cardiomyopathy, and dilated cardiomyopathy in cardiovascular magnetic resonance. Front. Cardiovasc. Med. 2021;8:764312. DOI: 10.3389/fcvm.2021.764312.Izquierdo C., Casas G., Martin-Isla C., Campello V.M., Guala A., Gkontra P. et al. Radiomics-based classification of left ventricular non-compaction, hypertrophic cardiomyopathy, and dilated cardiomyopathy in cardiovascular magnetic resonance. Front. Cardiovasc. Med. 2021;8:764312. DOI: 10.3389/fcvm.2021.764312.Neisius U., El-Rewaidy H., Kucukseymen S., Tsao C.W., Mancio J. et al. Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar. J. Magn. Reson. Imaging. 2020;52(3):906–919. DOI: 10.1002/jmri.27048.Neisius U., El-Rewaidy H., Kucukseymen S., Tsao C.W., Mancio J. et al. Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar. J. Magn. Reson. Imaging. 2020;52(3):906–919. DOI: 10.1002/jmri.27048.Wang J., Yang F., Liu W., Sun J., Han Y., Li D. et al. Radiomic analysis of native T1 mapping images discriminates between MYH7 and MYB-PC3-related hypertrophic cardiomyopathy. J. Magn. Reson. Imaging. 2020;52(6):1714–1721. DOI: 10.1002/jmri.27209.Wang J., Yang F., Liu W., Sun J., Han Y., Li D. et al. Radiomic analysis of native T1 mapping images discriminates between MYH7 and MYB-PC3-related hypertrophic cardiomyopathy. J. Magn. Reson. Imaging. 2020;52(6):1714–1721. DOI: 10.1002/jmri.27209.Spadarella G., Perillo T., Ugga L., Cuocolo R. Radiomics in cardiovascular disease imaging: from pixels to the heart of the problem. Curr. Cardiovasc. Imaging Rep. 2022;15:11–21. DOI: 10.1007/s12410-022-09563-z.Spadarella G., Perillo T., Ugga L., Cuocolo R. Radiomics in cardiovascular disease imaging: from pixels to the heart of the problem. Curr. Cardiovasc. Imaging Rep. 2022;15:11–21. DOI: 10.1007/s12410-022-09563-z.Larroza A., López-Lereu M.P., Monmeneu J.V., Gavara J., Chorro F.J., Bodí V. et al. Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction. Med. Phys. 2018;45(4):1471–1480. DOI: 10.1002/mp.12783.Larroza A., López-Lereu M.P., Monmeneu J.V., Gavara J., Chorro F.J., Bodí V. et al. Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction. Med. Phys. 2018;45(4):1471–1480. DOI: 10.1002/mp.12783.Chen B., An D., He J., Wu C.-W., Yue T., Wu R. et al. Myocardial extracellular volume fraction radiomics analysis for differentiation of reversible versus irreversible myocardial damage and prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction. Eur. Radiol. 2021;31:504–514. DOI: 10.1007/s00330-020-07117-9.Chen B., An D., He J., Wu C.-W., Yue T., Wu R. et al. Myocardial extracellular volume fraction radiomics analysis for differentiation of reversible versus irreversible myocardial damage and prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction. Eur. Radiol. 2021;31:504–514. DOI: 10.1007/s00330-020-07117-9.Eftestøl T., Woie L., Engan K., Kvaløy J.T., Nilsen D.W., Ørn S. Texture analysis to assess risk of serious arrhythmias after myocardial infarction. In: Computing in Cardiology IEEE. Krakow, Poland; 2012:365–368. URL: https://ieeexplore.ieee.org/abstract/document/6420406 (06.07.2023).Eftestøl T., Woie L., Engan K., Kvaløy J.T., Nilsen D.W., Ørn S. Texture analysis to assess risk of serious arrhythmias after myocardial infarction. In: Computing in Cardiology IEEE. Krakow, Poland; 2012:365–368. URL: https://ieeexplore.ieee.org/abstract/document/6420406 (06.07.2023).Engan K., Eftestol T., Orn S., Kvaloy J.T., Woie L. Exploratory data analysis of image texture and statistical features on myocardium and infarction areas in cardiac magnetic resonance images. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010;2010:5728–5731. DOI: 10.1109/IEMBS.2010.5627866.Engan K., Eftestol T., Orn S., Kvaloy J.T., Woie L. Exploratory data analysis of image texture and statistical features on myocardium and infarction areas in cardiac magnetic resonance images. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010;2010:5728–5731. DOI: 10.1109/IEMBS.2010.5627866.Ma Q., Ma Y., Yu T., Sun Z., Hou Y. Radiomics of non-contrast-enhanced T1 mapping: diagnostic and predictive performance for myocardial injury in acute ST-segment-elevation myocardial infarction. Korean J. Radiol. 2021;22(4):535–546. DOI:10.3348/kjr.2019.0969.Ma Q., Ma Y., Yu T., Sun Z., Hou Y. Radiomics of non-contrast-enhanced T1 mapping: diagnostic and predictive performance for myocardial injury in acute ST-segment-elevation myocardial infarction. Korean J. Radiol. 2021;22(4):535–546. DOI:10.3348/kjr.2019.0969.Raisi-Estabragh Z., Gkontra P., Jaggi A., Cooper J., Augusto J., Bhuva A.N. et al. Repeatability of cardiac magnetic resonance radiomics: A multi-centre multi-vendor test-retest study. Front. Cardiovasc. Med. 2020;7:586236. DOI: 10.3389/fcvm.2020.586236.Raisi-Estabragh Z., Gkontra P., Jaggi A., Cooper J., Augusto J., Bhuva A.N. et al. Repeatability of cardiac magnetic resonance radiomics: A multi-centre multi-vendor test-retest study. Front. Cardiovasc. Med. 2020;7:586236. DOI: 10.3389/fcvm.2020.586236.Park J.E., Park S.Y., Kim H.J., Kim H.S. Reproducibility and generalizability in radiomics modeling: Possible strategies in radiologic and statistical perspectives. Korean J. Radiol. 2019;20(7):1124–1137. DOI: 10.3348/kjr.2018.0070.Park J.E., Park S.Y., Kim H.J., Kim H.S. Reproducibility and generalizability in radiomics modeling: Possible strategies in radiologic and statistical perspectives. Korean J. Radiol. 2019;20(7):1124–1137. DOI: 10.3348/kjr.2018.0070.Park S.H., Han K. Methodologic Guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286(3):800–809. DOI: 10.1148/radiol.2017171920.Park S.H., Han K. Methodologic Guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286(3):800–809. DOI: 10.1148/radiol.2017171920.Varghese B.A., Cen S.Y., Hwang D.H., Duddalwar V.A. Texture analysis of imaging: What radiologists need to know. AJR Am. J. Roentgenol. 2019;212(3):520–528. DOI: 10.2214/AJR.18.20624.Varghese B.A., Cen S.Y., Hwang D.H., Duddalwar V.A. Texture analysis of imaging: What radiologists need to know. AJR Am. J. Roentgenol. 2019;212(3):520–528. DOI: 10.2214/AJR.18.20624.Kumar V., Gu Y., Basu S., Berglund A., Eschrich S.A., Schabath M.B. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging. 2012;30(9):1234–1248. DOI: 10.1016/j.mri.2012.06.010.Kumar V., Gu Y., Basu S., Berglund A., Eschrich S.A., Schabath M.B. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging. 2012;30(9):1234–1248. DOI: 10.1016/j.mri.2012.06.010.Amano Y., Suzuki Y., Yanagisawa F., Omori Y., Matsumoto N. Relationship between extension or texture features of late gadolinium enhancement and ventricular tachyarrhythmias in hypertrophic cardiomyopathy. Biomed. Res. Int. 2018;2018:4092469. DOI: 10.1155/2018/4092469.Amano Y., Suzuki Y., Yanagisawa F., Omori Y., Matsumoto N. Relationship between extension or texture features of late gadolinium enhancement and ventricular tachyarrhythmias in hypertrophic cardiomyopathy. Biomed. Res. Int. 2018;2018:4092469. DOI: 10.1155/2018/4092469.

The authors declare that there are no conflicts of interest present.