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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="review-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Digital Diagnostics</journal-id><journal-title-group><journal-title xml:lang="en">Digital Diagnostics</journal-title><trans-title-group xml:lang="ru"><trans-title>Digital Diagnostics</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>Digital Diagnostics</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2712-8490</issn><issn publication-format="electronic">2712-8962</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">625382</article-id><article-id pub-id-type="doi">10.17816/DD625382</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Reviews</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Научные обзоры</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="zh"><subject>科学评论</subject></subj-group><subj-group subj-group-type="article-type"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Prospects for the application of radiomics to brain tumors</article-title><trans-title-group xml:lang="ru"><trans-title>Перспективы применения радиомики при опухолях головного мозга</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>影像组学在脑肿瘤中的应用前景</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0219-7260</contrib-id><contrib-id contrib-id-type="spin">9657-0598</contrib-id><name-alternatives><name xml:lang="en"><surname>Regentova</surname><given-names>Olga S.</given-names></name><name xml:lang="ru"><surname>Регентова</surname><given-names>Ольга Сергеевна</given-names></name><name xml:lang="zh"><surname>Regentova</surname><given-names>Olga S.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>канд. мед. наук</p></bio><bio xml:lang="zh"><p>MD, Cand. Sci. (Medicine)</p></bio><email>olgagraudensh@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-9249-9272</contrib-id><contrib-id contrib-id-type="spin">9902-4244</contrib-id><name-alternatives><name xml:lang="en"><surname>Parkhomenko</surname><given-names>Roman A.</given-names></name><name xml:lang="ru"><surname>Пархоменко</surname><given-names>Роман Алексеевич</given-names></name><name xml:lang="zh"><surname>Parkhomenko</surname><given-names>Roman A.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><bio xml:lang="ru"><p>д-р мед. наук, профессор</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><email>raparkhomenko@rncrr.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4147-1928</contrib-id><contrib-id contrib-id-type="spin">2408-6502</contrib-id><name-alternatives><name xml:lang="en"><surname>Sergeyev</surname><given-names>Nikolay I.</given-names></name><name xml:lang="ru"><surname>Сергеев</surname><given-names>Николай Иванович</given-names></name><name xml:lang="zh"><surname>Sergeyev</surname><given-names>Nikolay I.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>д-р мед. наук</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine)</p></bio><email>sergeev_n@rncrr.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8351-8152</contrib-id><contrib-id contrib-id-type="spin">8380-6617</contrib-id><name-alternatives><name xml:lang="en"><surname>Bozhenko</surname><given-names>Vladimir K.</given-names></name><name xml:lang="ru"><surname>Боженко</surname><given-names>Владимир Константинович</given-names></name><name xml:lang="zh"><surname>Bozhenko</surname><given-names>Vladimir K.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><bio xml:lang="ru"><p>д-р мед. наук, профессор</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><email>vkbojenko@rncrr.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-6661-0280</contrib-id><contrib-id contrib-id-type="spin">7600-7304</contrib-id><name-alternatives><name xml:lang="en"><surname>Polushkin</surname><given-names>Pavel V.</given-names></name><name xml:lang="ru"><surname>Полушкин</surname><given-names>Павел Владимирович</given-names></name><name xml:lang="zh"><surname>Polushkin</surname><given-names>Pavel V.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>канд. мед. наук</p></bio><bio xml:lang="zh"><p>MD, Cand. Sci. (Medicine)</p></bio><email>roentradpc@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1641-6452</contrib-id><contrib-id contrib-id-type="spin">9556-6556</contrib-id><name-alternatives><name xml:lang="en"><surname>Solodkiy</surname><given-names>Vladimir А.</given-names></name><name xml:lang="ru"><surname>Солодкий</surname><given-names>Владимир Алексеевич</given-names></name><name xml:lang="zh"><surname>Solodkiy</surname><given-names>Vladimir А.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine), Professor, Academician of RAS</p></bio><bio xml:lang="ru"><p>д-р мед. наук, профессор, академик РАН</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine), Professor, Academician of RAS</p></bio><email>mailbox@rncrr.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Russian Scientific Center of Roentgenoradiology</institution></aff><aff><institution xml:lang="ru">Российский научный центр рентгенорадиологии</institution></aff><aff><institution xml:lang="zh">Russian Scientific Center of Roentgenoradiology</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Peoples’ Friendship University of Russia</institution></aff><aff><institution xml:lang="ru">Российский университет дружбы народов имени Патриса Лумумбы</institution></aff><aff><institution xml:lang="zh">Peoples’ Friendship University of Russia</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2024-10-16" publication-format="electronic"><day>16</day><month>10</month><year>2024</year></pub-date><pub-date date-type="pub" iso-8601-date="2024-12-04" publication-format="electronic"><day>04</day><month>12</month><year>2024</year></pub-date><volume>5</volume><issue>3</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><issue-title xml:lang="zh"/><fpage>567</fpage><lpage>577</lpage><history><date date-type="received" iso-8601-date="2023-12-28"><day>28</day><month>12</month><year>2023</year></date><date date-type="accepted" iso-8601-date="2024-03-13"><day>13</day><month>03</month><year>2024</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Eco-Vector</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Эко-вектор</copyright-statement><copyright-statement xml:lang="zh">Copyright ©; 2024, Eco-Vector</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Eco-Vector</copyright-holder><copyright-holder xml:lang="ru">Эко-вектор</copyright-holder><copyright-holder xml:lang="zh">Eco-Vector</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc-nd/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://jdigitaldiagnostics.com/DD/article/view/625382">https://jdigitaldiagnostics.com/DD/article/view/625382</self-uri><abstract xml:lang="en"><p>Radiomics is a new branch in diagnostics based on a quantitative approach to medical imaging able to ensure more efficient use of medical equipment, optimize imaging time per patient, and increase the accuracy of differential diagnostics in various areas of medicine. Radiogenomics is a branch of radionics intended to establish a connection between the patient’s genotype and phenotypic presentation obtained from medical imaging. The review dwells on general issues for radiomics and radiogenomics in oncology with the recent study findings, focusing on the role of these methods in neurooncology and solving problems in diagnosing brain tumors. One of the current topics in neurooncology is disease prognosis in patients with unverified midline gliomas because morphological confirmation of the diagnosis and molecular genetic testing of tissues is impossible. Besides, the high spatial and temporal heterogeneity of malignant neoplasms prevents a complete assessment of the biological properties of the tumor and even using stereotactic biopsy methods. Radiomics methods can help doctors differentiate the tumor grade, acting as a “virtual biopsy” while avoiding invasive procedures. The study findings on radiomics and radiogenomics in neurooncology indicate the undeniable promise of these methods; however, as in other areas of medicine and biology, errors cannot be completely excluded, so the expert team must aim to minimize them.</p></abstract><trans-abstract xml:lang="ru"><p>Радиомика — новое направление в диагностике, основанное на количественном подходе к медицинской визуализации, способное обеспечить более эффективное использование медицинской аппаратуры, оптимизировать временные затраты на каждого пациента, а также повысить точность дифференциальной диагностики в различных областях медицины. Ответвлением радиомики является радиогеномика, призванная для установления связи между генотипом пациента и фенотипической картиной, представленной методом медицинской визуализации. В обзоре приведены общие вопросы о радиомике и радиогеномике в онкологии с результатами исследований в этой области, полученными в последние годы, с особым вниманием к роли этих методов в нейроонкологии, а также к решению проблем диагностики опухолей головного мозга. Одной из актуальных проблем в нейроонкологии является возможность определения прогноза заболевания у больных с неверифицированными срединными глиомами ввиду невозможности морфологического подтверждения диагноза и молекулярно-генетического исследования тканей. Кроме того, высокая гетерогенность злокачественного новообразования, свойственная как в пространственном, так и временном смысле, препятствует полноценной оценке биологических свойств опухоли, даже с использованием методов стереотаксической биопсии. Методы радиомики могут помочь врачам дифференцировать степень злокачественности опухоли, выполняя роль «виртуальной биопсии», при этом избежать целого ряда инвазивных процедур. Результаты исследований по радиомике и радиогеномике в нейроонкологии свидетельствуют о несомненной перспективности этих методик, однако, как и в других областях медицины и биологии, нельзя полностью исключить ошибки, и задача команды вовлечённых в неё специалистов — свести эту вероятность к минимуму.</p></trans-abstract><trans-abstract xml:lang="zh"><p>影像组学是一个基于医学成像定量方法的诊断学新领域，它能更有效地利用医疗设备，优化每位患者的治疗时间，并提高医学各个领域鉴别诊断的准确性。放射影像基因组学是影像组学的一个分支，旨在将病人的基因型与医学影像所呈现的表型联系起来。在综述中结合近年来该领域取得的研究成果，提出肿瘤学中影像组学和放射影像基因组学的一般问题，特别关注这些方法在神经肿瘤学中的作用，以及脑肿瘤诊断问题的解决方案。神经肿瘤学亟待解决的问题之一是判断疾病预后的能力，这是因为无法对中线胶质瘤患者进行形态学确诊和组织分子遗传学研究。此外，恶性肿瘤在空间和时间上固有的高度异质性阻碍对肿瘤生物特性的全面评估，甚至使用立体定向活检法术也是如此。放射组学方法可以帮助医生区分肿瘤的恶性程度，发挥“虚拟活检”的作用，同时避免一些侵入性操作。神经肿瘤学中的影像组学和放射影像基因组学研究结果表明，这些技术的前景毋庸置疑，但是，与其他医学和生物学领域一样，错误也不能完全排除，相关专家团队的任务就是将这种错误概率降到最低。</p></trans-abstract><kwd-group xml:lang="en"><kwd>malignant tumors</kwd><kwd>radiomics</kwd><kwd>radiogenomics</kwd><kwd>artificial intelligence</kwd><kwd>brain</kwd><kwd>neurooncology</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>злокачественные опухоли</kwd><kwd>радиомика</kwd><kwd>радиогеномика</kwd><kwd>искусственный интеллект</kwd><kwd>головной мозг</kwd><kwd>нейроонкология</kwd></kwd-group><kwd-group xml:lang="zh"><kwd>恶性肿瘤</kwd><kwd>影像组学</kwd><kwd>放射影像基因组学</kwd><kwd>人工智能</kwd><kwd>大脑</kwd><kwd>神经肿瘤学</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><citation-alternatives><mixed-citation xml:lang="en">Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012;48(4):441–446. doi: 10.1016/j.ejca.2011.11.036</mixed-citation><mixed-citation xml:lang="ru">Lambin P., Rios-Velazquez E., Leijenaar R., et al. Radiomics: Extracting more information from medical images using advanced feature analysis // European Journal of Cancer. 2012. Vol. 48, N 4. P. 441–446. doi: 10.1016/j.ejca.2011.11.036</mixed-citation><mixed-citation xml:lang="zh">Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012;48(4):441–446. doi: 10.1016/j.ejca.2011.11.036</mixed-citation></citation-alternatives></ref><ref id="B2"><label>2.</label><citation-alternatives><mixed-citation xml:lang="en">Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magnetic Resonance Imaging. 2012;30(9):1234–1248. doi: 10.1016/j.mri.2012.06.010</mixed-citation><mixed-citation xml:lang="ru">Kumar V., Gu Y., Basu S., et al. Radiomics: the process and the challenges // Magnetic Resonance Imaging. 2012. Vol. 30, N 9. P. 1234–1248. doi: 10.1016/j.mri.2012.06.010</mixed-citation><mixed-citation xml:lang="zh">Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magnetic Resonance Imaging. 2012;30(9):1234–1248. doi: 10.1016/j.mri.2012.06.010</mixed-citation></citation-alternatives></ref><ref id="B3"><label>3.</label><citation-alternatives><mixed-citation xml:lang="en">Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovremennye tekhnologii v meditsine. 2021;13(2):97–104. (In Russ.) doi: 10.17691/stm2021.13.2.11</mixed-citation><mixed-citation xml:lang="ru">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. T. 13, № 2. C. 97–104. doi: 10.17691/stm2021.13.2.11</mixed-citation><mixed-citation xml:lang="zh">Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovremennye tekhnologii v meditsine. 2021;13(2):97–104. (In Russ.) doi: 10.17691/stm2021.13.2.11</mixed-citation></citation-alternatives></ref><ref id="B4"><label>4.</label><citation-alternatives><mixed-citation xml:lang="en">Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics. Journal of Nuclear Medicine. 2020;61(4):488–495. doi: 10.2967/jnumed.118.222893</mixed-citation><mixed-citation xml:lang="ru">Mayerhoefer M.E., Materka A., Langs G., et al. Introduction to Radiomics // Journal of Nuclear Medicine. 2020. Vol. 61, N 4. P. 488–495. doi: 10.2967/jnumed.118.222893</mixed-citation><mixed-citation xml:lang="zh">Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics. Journal of Nuclear Medicine. 2020;61(4):488–495. doi: 10.2967/jnumed.118.222893</mixed-citation></citation-alternatives></ref><ref id="B5"><label>5.</label><citation-alternatives><mixed-citation xml:lang="en">Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neuro-Oncology Advances. 2021;2 Suppl. 4:iv3-iv14. doi: 10.1093/noajnl/vdaa148</mixed-citation><mixed-citation xml:lang="ru">Beig N., Bera K., Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges // Neuro-Oncology Advances. 2021. Vol. 2, Suppl. 4. P. iv3–iv14. doi: 10.1093/noajnl/vdaa148</mixed-citation><mixed-citation xml:lang="zh">Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neuro-Oncology Advances. 2021;2 Suppl. 4:iv3-iv14. doi: 10.1093/noajnl/vdaa148</mixed-citation></citation-alternatives></ref><ref id="B6"><label>6.</label><citation-alternatives><mixed-citation xml:lang="en">Chernobrivtseva VV, Misyurin AS. New technologies in radiology diagnostics. Practical Oncology. 2022;23(4):203–210. EDN: DUCVOW doi: 10.31917/2304203</mixed-citation><mixed-citation xml:lang="ru">Чернобривцева В.В., Мисюрин А.С. Новые технологии лучевой диагностики // Практическая онкология. 2022. Т. 23, № 4. С. 203–210. EDN: DUCVOW doi: 10.31917/2304203</mixed-citation><mixed-citation xml:lang="zh">Chernobrivtseva VV, Misyurin AS. New technologies in radiology diagnostics. Practical Oncology. 2022;23(4):203–210. EDN: DUCVOW doi: 10.31917/2304203</mixed-citation></citation-alternatives></ref><ref id="B7"><label>7.</label><citation-alternatives><mixed-citation xml:lang="en">Danilov GV, Ishankulov TA, Kotik KV, et al. Artificial intelligence technologies in clinical neurooncology. Voprosy neyrokhirurgii imeni N.N. Burdenko. 2022;86(6):127–133. (In Russ.) EDN: XPLMSB doi: 10.17116/neiro202286061127</mixed-citation><mixed-citation xml:lang="ru">Данилов Г.В., Ишанкулов Т.А., Котик К.В., и др. Технологии искусственного интеллекта в клинической нейроонкологии // Вопросы нейрохирургии им. Н.Н. Бурденко. 2022. Т. 86, № 6. С. 127–133. EDN: XPLMSB doi: 10.17116/neiro202286061127</mixed-citation><mixed-citation xml:lang="zh">Danilov GV, Ishankulov TA, Kotik KV, et al. Artificial intelligence technologies in clinical neurooncology. Voprosy neyrokhirurgii imeni N.N. Burdenko. 2022;86(6):127–133. (In Russ.) EDN: XPLMSB doi: 10.17116/neiro202286061127</mixed-citation></citation-alternatives></ref><ref id="B8"><label>8.</label><citation-alternatives><mixed-citation xml:lang="en">Solodkiy VA, Kaprin AD, Nudnov NV, et al. Artificial intelligence capabilities in breast cancer risk assessment on mammographic images (clinical examples). Vestnik Rossijskogo naučnogo centra rentgenoradiologii. 2023;23(1):24–31. (In Russ.) EDN: BWYQPJ</mixed-citation><mixed-citation xml:lang="ru">Солодкий В.А., Каприн А.Д., Нуднов Н.В., и др. Возможности искусственного интеллекта в оценке риска рака молочной железы на маммографических изображениях (клинические примеры) // Вестник Российского научного центра рентгенорадиологии. 2023. Т. 23, № 1. С. 24–31. EDN: BWYQPJ</mixed-citation><mixed-citation xml:lang="zh">Solodkiy VA, Kaprin AD, Nudnov NV, et al. Artificial intelligence capabilities in breast cancer risk assessment on mammographic images (clinical examples). Vestnik Rossijskogo naučnogo centra rentgenoradiologii. 2023;23(1):24–31. (In Russ.) EDN: BWYQPJ</mixed-citation></citation-alternatives></ref><ref id="B9"><label>9.</label><citation-alternatives><mixed-citation xml:lang="en">Prokop M. Multislice CT: technical principles and future trends. European Radiology. 2003;13 Suppl. 5:M3–13. doi: 10.1007/s00330-003-2178-z</mixed-citation><mixed-citation xml:lang="ru">Prokop M. Multislice CT: technical principles and future trends // European Radiology. 2003. Vol. 13, Suppl. 5. P. M3–13. doi: 10.1007/s00330-003-2178-z</mixed-citation><mixed-citation xml:lang="zh">Prokop M. Multislice CT: technical principles and future trends. European Radiology. 2003;13 Suppl. 5:M3–13. doi: 10.1007/s00330-003-2178-z</mixed-citation></citation-alternatives></ref><ref id="B10"><label>10.</label><citation-alternatives><mixed-citation xml:lang="en">Groheux D, Quere G, Blanc E, et al. FDG PET-CT for solitary pulmonary nodule and lung cancer: Literature review. Diagnostic and Interventional Imaging. 2016;97(10):1003–1017. doi: 10.1016/j.diii.2016.06.020</mixed-citation><mixed-citation xml:lang="ru">Groheux D., Quere G., Blanc E., et al. FDG PET-CT for solitary pulmonary nodule and lung cancer: Literature review // Diagnostic and Interventional Imaging. 2016. Vol. 97, N 10. P. 1003–1017. doi: 10.1016/j.diii.2016.06.020</mixed-citation><mixed-citation xml:lang="zh">Groheux D, Quere G, Blanc E, et al. FDG PET-CT for solitary pulmonary nodule and lung cancer: Literature review. Diagnostic and Interventional Imaging. 2016;97(10):1003–1017. doi: 10.1016/j.diii.2016.06.020</mixed-citation></citation-alternatives></ref><ref id="B11"><label>11.</label><citation-alternatives><mixed-citation xml:lang="en">Goo HW, Goo JM. Dual-Energy CT: New Horizon in Medical Imaging. Korean Journal of Radiology. 2017;18(4):555–569. doi: 10.3348/kjr.2017.18.4.555</mixed-citation><mixed-citation xml:lang="ru">Goo H.W., Goo J.M. Dual-Energy CT: New Horizon in Medical Imaging // Korean Journal of Radiology. 2017. Vol. 18, N 4. P. 555–569. doi: 10.3348/kjr.2017.18.4.555</mixed-citation><mixed-citation xml:lang="zh">Goo HW, Goo JM. Dual-Energy CT: New Horizon in Medical Imaging. Korean Journal of Radiology. 2017;18(4):555–569. doi: 10.3348/kjr.2017.18.4.555</mixed-citation></citation-alternatives></ref><ref id="B12"><label>12.</label><citation-alternatives><mixed-citation xml:lang="en">Zaharchuk G. Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning. European Journal of Nuclear Medicine and Molecular Imaging. 2019;46(13):2700–2707. doi: 10.1007/s00259-019-04374-9</mixed-citation><mixed-citation xml:lang="ru">Zaharchuk G. Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning // European Journal of Nuclear Medicine and Molecular Imaging. 2019. Vol. 46, N 13. P. 2700–2707. doi: 10.1007/s00259-019-04374-9</mixed-citation><mixed-citation xml:lang="zh">Zaharchuk G. Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning. European Journal of Nuclear Medicine and Molecular Imaging. 2019;46(13):2700–2707. doi: 10.1007/s00259-019-04374-9</mixed-citation></citation-alternatives></ref><ref id="B13"><label>13.</label><citation-alternatives><mixed-citation xml:lang="en">Hsieh J, Flohr T. Computed tomography recent history and future perspectives. Journal of Medical Imaging. 2021;8(5):052109. doi: 10.1117/1.JMI.8.5.052109</mixed-citation><mixed-citation xml:lang="ru">Hsieh J., Flohr T. Computed tomography recent history and future perspectives // Journal of Medical Imaging. 2021. Vol. 8, N 5. P. 052109. doi: 10.1117/1.JMI.8.5.052109</mixed-citation><mixed-citation xml:lang="zh">Hsieh J, Flohr T. Computed tomography recent history and future perspectives. Journal of Medical Imaging. 2021;8(5):052109. doi: 10.1117/1.JMI.8.5.052109</mixed-citation></citation-alternatives></ref><ref id="B14"><label>14.</label><citation-alternatives><mixed-citation xml:lang="en">Gouel P, Decazes P, Vera P, et al. Advances in PET and MRI imaging of tumor hypoxia. Frontiers in Medicine. 2023;10:1055062. doi: 10.3389/fmed.2023.1055062</mixed-citation><mixed-citation xml:lang="ru">Gouel P., Decazes P., Vera P., et al. Advances in PET and MRI imaging of tumor hypoxia // Frontiers in Medicine. 2023. Vol. 10. P. 1055062. doi: 10.3389/fmed.2023.1055062</mixed-citation><mixed-citation xml:lang="zh">Gouel P, Decazes P, Vera P, et al. Advances in PET and MRI imaging of tumor hypoxia. Frontiers in Medicine. 2023;10:1055062. doi: 10.3389/fmed.2023.1055062</mixed-citation></citation-alternatives></ref><ref id="B15"><label>15.</label><citation-alternatives><mixed-citation xml:lang="en">Szczykutowicz TP, Bour RK, Rubert N, et al. CT protocol management: simplifying the process by using a master protocol concept. Journal of Applied Clinical Medical Physics. 2015;16(4):228–243. doi: 10.1120/jacmp.v16i4.5412</mixed-citation><mixed-citation xml:lang="ru">Szczykutowicz T.P., Bour R.K., Rubert N., et al. CT protocol management: simplifying the process by using a master protocol concept // Journal of Applied Clinical Medical Physics. 2015. Vol. 16, N 4. P. 228–243. doi: 10.1120/jacmp.v16i4.5412</mixed-citation><mixed-citation xml:lang="zh">Szczykutowicz TP, Bour RK, Rubert N, et al. CT protocol management: simplifying the process by using a master protocol concept. Journal of Applied Clinical Medical Physics. 2015;16(4):228–243. doi: 10.1120/jacmp.v16i4.5412</mixed-citation></citation-alternatives></ref><ref id="B16"><label>16.</label><citation-alternatives><mixed-citation xml:lang="en">Zalog uspekha bolshie dannye v umelykh rukakh. In: Biomolecula [Internet]. 2007–2024 [cited 2023 Oct 26]. Available from: https://biomolecula.ru/articles/zalog-uspekha-bolshie-dannye-v-umelykh-rukakh</mixed-citation><mixed-citation xml:lang="ru">Залог успеха — большие данные в умелых руках. В: Биомолекула [интернет]. 2007–2024. Режим доступа: https://biomolecula.ru/articles/zalog-uspekha-bolshie-dannye-v-umelykh-rukakh Дата обращения: 26.10.2023.</mixed-citation><mixed-citation xml:lang="zh">Zalog uspekha bolshie dannye v umelykh rukakh. In: Biomolecula [Internet]. 2007–2024 [cited 2023 Oct 26]. Available from: https://biomolecula.ru/articles/zalog-uspekha-bolshie-dannye-v-umelykh-rukakh</mixed-citation></citation-alternatives></ref><ref id="B17"><label>17.</label><citation-alternatives><mixed-citation xml:lang="en">Ognerubov NA, Shatov IA, Shatov AV. Radiogenomics and radiomics in the diagnostics of malignant tumours: a literary review. Vestnik Tambovskogo universiteta. Seriya: yestestvennye i tekhnicheskiye nauki. 2017;22(6-2):1453–1460. (In Russ.) EDN: YRNTMV doi: 10.20310/1810-0198-2017-22-6-1453-1460</mixed-citation><mixed-citation xml:lang="ru">Огнерубов Н.А., Шатов И.А., Шатов А.В. Радиогеномика и радиомика в диагностике злокачественных опухолей: обзор литературы // Вестник Тамбовского университета. Серия: естественные и технические науки. 2017. Т. 22, № 6-2. С. 1453–1460. EDN: YRNTMV doi: 10.20310/1810-0198-2017-22-6-1453-1460</mixed-citation><mixed-citation xml:lang="zh">Ognerubov NA, Shatov IA, Shatov AV. Radiogenomics and radiomics in the diagnostics of malignant tumours: a literary review. Vestnik Tambovskogo universiteta. Seriya: yestestvennye i tekhnicheskiye nauki. 2017;22(6-2):1453–1460. (In Russ.) EDN: YRNTMV doi: 10.20310/1810-0198-2017-22-6-1453-1460</mixed-citation></citation-alternatives></ref><ref id="B18"><label>18.</label><citation-alternatives><mixed-citation xml:lang="en">Nikulshina YaO, Redkin AN. Radiomics and radiogenomics in the diagnosis, clinical prognosis and treatment response assessment in oncological diseases (literature review). Diagnosticheskaya i interventsionnaya radiologiya. 2022;16(3):70–78. (In Russ.) EDN: KHLFNT doi: 10.25512/DIR.2022.16.3.07</mixed-citation><mixed-citation xml:lang="ru">Никульшина Я.О., Редькин А.Н. Радиомика и радиогеномика в диагностике, клиническом прогнозе и оценке ответа на лечение при онкологических заболеваниях (Обзор литературы) // Диагностическая и интервенционная радиология. 2022. T. 16, № 3. C. 70–78. EDN: KHLFNT doi: 10.25512/DIR.2022.16.3.07</mixed-citation><mixed-citation xml:lang="zh">Nikulshina YaO, Redkin AN. Radiomics and radiogenomics in the diagnosis, clinical prognosis and treatment response assessment in oncological diseases (literature review). Diagnosticheskaya i interventsionnaya radiologiya. 2022;16(3):70–78. (In Russ.) EDN: KHLFNT doi: 10.25512/DIR.2022.16.3.07</mixed-citation></citation-alternatives></ref><ref id="B19"><label>19.</label><citation-alternatives><mixed-citation xml:lang="en">Peng Z, Wang Y, Wang Y, et al. Application of radiomics and machine learning in head and neck cancers. International Journal of Biological Sciences. 2021;17(2):475–486. doi: 10.7150/ijbs.55716</mixed-citation><mixed-citation xml:lang="ru">Peng Z., Wang Y., Wang Y., et al. Application of radiomics and machine learning in head and neck cancers // International Journal of Biological Sciences. 2021. Vol. 17, N 2. P. 475–486. doi: 10.7150/ijbs.55716</mixed-citation><mixed-citation xml:lang="zh">Peng Z, Wang Y, Wang Y, et al. Application of radiomics and machine learning in head and neck cancers. International Journal of Biological Sciences. 2021;17(2):475–486. doi: 10.7150/ijbs.55716</mixed-citation></citation-alternatives></ref><ref id="B20"><label>20.</label><citation-alternatives><mixed-citation xml:lang="en">Bernatz S, Böth I, Ackermann J, et al. Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I). BMC Medical Imaging. 2023;23(1):71. doi: 10.1186/s12880-023-01034-1</mixed-citation><mixed-citation xml:lang="ru">Bernatz S., Böth I., Ackermann J., et al. Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I) // BMC Medical Imaging. 2023. Vol. 23, N 1. P. 71. doi: 10.1186/s12880-023-01034-1</mixed-citation><mixed-citation xml:lang="zh">Bernatz S, Böth I, Ackermann J, et al. Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I). BMC Medical Imaging. 2023;23(1):71. doi: 10.1186/s12880-023-01034-1</mixed-citation></citation-alternatives></ref><ref id="B21"><label>21.</label><citation-alternatives><mixed-citation xml:lang="en">Wang Y, Jin ZY. Radiomics approaches in gastric cancer. Chinese Medical Journal. 2019;132(16):1983–1989. doi: 10.1097/CM9.0000000000000360</mixed-citation><mixed-citation xml:lang="ru">Wang Y., Jin Z.-Y. Radiomics approaches in gastric cancer // Chinese Medical Journal. 2019. Vol. 132, N 16. P. 1983–1989. doi: 10.1097/CM9.0000000000000360</mixed-citation><mixed-citation xml:lang="zh">Wang Y, Jin ZY. Radiomics approaches in gastric cancer. Chinese Medical Journal. 2019;132(16):1983–1989. doi: 10.1097/CM9.0000000000000360</mixed-citation></citation-alternatives></ref><ref id="B22"><label>22.</label><citation-alternatives><mixed-citation xml:lang="en">Liu D, Zhang W, Hu F, et al. A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study. Frontiers in Oncology. 2021;11:777760. doi: 10.3389/fonc.2021.777760</mixed-citation><mixed-citation xml:lang="ru">Liu D., Zhang W., Hu F., et al. A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study // Frontiers in Oncology. 2021. Vol. 11. P. 777760. doi: 10.3389/fonc.2021.777760</mixed-citation><mixed-citation xml:lang="zh">Liu D, Zhang W, Hu F, et al. A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study. Frontiers in Oncology. 2021;11:777760. doi: 10.3389/fonc.2021.777760</mixed-citation></citation-alternatives></ref><ref id="B23"><label>23.</label><citation-alternatives><mixed-citation xml:lang="en">Gong XQ, Tao YY, Wu Y, et al. Progress of MRI Radiomics in Hepatocellular Carcinoma. Frontiers in Oncology. 2021;11:698373. doi: 10.3389/fonc.2021.698373</mixed-citation><mixed-citation xml:lang="ru">Gong X-Q., Tao Y-Y., Wu Y., et al. Progress of MRI Radiomics in Hepatocellular Carcinoma // Frontiers in Oncology. 2021. Vol. 11. P. 698373 doi: 10.3389/fonc.2021.698373</mixed-citation><mixed-citation xml:lang="zh">Gong XQ, Tao YY, Wu Y, et al. Progress of MRI Radiomics in Hepatocellular Carcinoma. Frontiers in Oncology. 2021;11:698373. doi: 10.3389/fonc.2021.698373</mixed-citation></citation-alternatives></ref><ref id="B24"><label>24.</label><citation-alternatives><mixed-citation xml:lang="en">Miranda J, Horvat N, Fonseca GM, et al. Current status and future perspectives of radiomics in hepatocellular carcinoma. World Journal of Gastroenterology. 2023;29(1):43–60. doi: 10.3748/wjg.v29.i1.43</mixed-citation><mixed-citation xml:lang="ru">Miranda J., Horvat N., Fonseca G.M., et al. Current status and future perspectives of radiomics in hepatocellular carcinoma // World Journal of Gastroenterology. 2023. Vol. 29, N 1. P. 43–60. doi: 10.3748/wjg.v29.i1.43</mixed-citation><mixed-citation xml:lang="zh">Miranda J, Horvat N, Fonseca GM, et al. Current status and future perspectives of radiomics in hepatocellular carcinoma. World Journal of Gastroenterology. 2023;29(1):43–60. doi: 10.3748/wjg.v29.i1.43</mixed-citation></citation-alternatives></ref><ref id="B25"><label>25.</label><citation-alternatives><mixed-citation xml:lang="en">Ferro M, de Cobelli O, Musi G, et al. Radiomics in prostate cancer: an up-to-date review. Therapeutic Advances in Urology. 2022;14:175628722211090. doi: 10.1177/17562872221109020</mixed-citation><mixed-citation xml:lang="ru">Ferro M., de Cobelli O., Musi G., et al. Radiomics in prostate cancer: an up-to-date review // Therapeutic Advances in Urology. 2022. Vol. 14. P. 175628722211090. doi: 10.1177/17562872221109020</mixed-citation><mixed-citation xml:lang="zh">Ferro M, de Cobelli O, Musi G, et al. Radiomics in prostate cancer: an up-to-date review. Therapeutic Advances in Urology. 2022;14:175628722211090. doi: 10.1177/17562872221109020</mixed-citation></citation-alternatives></ref><ref id="B26"><label>26.</label><citation-alternatives><mixed-citation xml:lang="en">Chaddad A, Tan G, Liang X, et al. Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects. Cancers (Basel). 2023;15(15):3839. doi: 10.3390/cancers15153839</mixed-citation><mixed-citation xml:lang="ru">Chaddad A., Tan G., Liang X., et al. Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects // Cancers (Basel). 2023. Vol. 15, N 15. P. 3839. doi: 10.3390/cancers15153839</mixed-citation><mixed-citation xml:lang="zh">Chaddad A, Tan G, Liang X, et al. Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects. Cancers (Basel). 2023;15(15):3839. doi: 10.3390/cancers15153839</mixed-citation></citation-alternatives></ref><ref id="B27"><label>27.</label><citation-alternatives><mixed-citation xml:lang="en">Loginova MV, Pavlov VN, Gilyazova IR. Radiomics and radiogenomics of prostate cancer. Yakut Medical Journal. 2021;(1):101–104. EDN: QPZIWO doi: 10.25789/YMJ.2021.73.27</mixed-citation><mixed-citation xml:lang="ru">Логинова М.В., Павлов В.Н., Гилязова И.Р. Радиомика и радиогеномика рака предстательной железы // Якутский медицинский журнал. 2021. № 1. С. 101–104. EDN: QPZIWO doi: 10.25789/YMJ.2021.73.27</mixed-citation><mixed-citation xml:lang="zh">Loginova MV, Pavlov VN, Gilyazova IR. Radiomics and radiogenomics of prostate cancer. Yakut Medical Journal. 2021;(1):101–104. EDN: QPZIWO doi: 10.25789/YMJ.2021.73.27</mixed-citation></citation-alternatives></ref><ref id="B28"><label>28.</label><citation-alternatives><mixed-citation xml:lang="en">Govorukhina VG, Semenov SS, Gelezhe PB, et al. The role of mammography in breast cancer radiomics. Digital Diagnostics. 2021;2(2):185–199. EDN: RFSJYH doi: 10.17816/DD70479</mixed-citation><mixed-citation xml:lang="ru">Говорухина В.Г., Семенов С.С., Гележе П.Б., и др. Роль маммографии в радиомике рака молочной железы // Digital Diagnostics. 2021. Т. 2, № 2. С. 185–199. EDN: RFSJYH doi: 10.17816/DD70479</mixed-citation><mixed-citation xml:lang="zh">Govorukhina VG, Semenov SS, Gelezhe PB, et al. The role of mammography in breast cancer radiomics. Digital Diagnostics. 2021;2(2):185–199. EDN: RFSJYH doi: 10.17816/DD70479</mixed-citation></citation-alternatives></ref><ref id="B29"><label>29.</label><citation-alternatives><mixed-citation xml:lang="en">Sergeev NI, Kotlyarov PM, Solodkiy VA. Differential diagnosis of focal changes in the spine using standard and radiomic analysis. N.N. Priorov Journal of Traumatology and Orthopedics. 2023;30(1):77–86. EDN: ZVWOVA doi: 10.17816/vto322858</mixed-citation><mixed-citation xml:lang="ru">Сергеев Н.И., Котляров П.М., Солодкий В.А. Дифференциальная диагностика очаговых изменений позвоночника с использованием стандартного и радиомического анализа: ретроспективное исследование // Вестник травматологии и ортопедии им. Н.Н. Приорова. 2023. Т. 30, № 1. С. 77–86. EDN: ZVWOVA doi: 10.17816/vto322858</mixed-citation><mixed-citation xml:lang="zh">Sergeev NI, Kotlyarov PM, Solodkiy VA. Differential diagnosis of focal changes in the spine using standard and radiomic analysis. N.N. Priorov Journal of Traumatology and Orthopedics. 2023;30(1):77–86. EDN: ZVWOVA doi: 10.17816/vto322858</mixed-citation></citation-alternatives></ref><ref id="B30"><label>30.</label><citation-alternatives><mixed-citation xml:lang="en">Steinhauer V, Sergeev NI. Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion. Sovremennye tekhnologii v meditsine. 2022;14(2):16–25. (In Russ.) EDN: XFVITL doi: 10.17691/stm2022.14.2.02</mixed-citation><mixed-citation xml:lang="ru">Штайнгауэр В., Сергеев Н.И. Радиомика при раке молочной железы: использование глубокого машинного анализа МРТ-изображений метастатического поражения позвоночника // Современные технологии в медицине. 2022. Т. 14, № 2. С. 16–25. EDN: XFVITL doi: 10.17691/stm2022.14.2.02</mixed-citation><mixed-citation xml:lang="zh">Steinhauer V, Sergeev NI. Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion. Sovremennye tekhnologii v meditsine. 2022;14(2):16–25. (In Russ.) EDN: XFVITL doi: 10.17691/stm2022.14.2.02</mixed-citation></citation-alternatives></ref><ref id="B31"><label>31.</label><citation-alternatives><mixed-citation xml:lang="en">Danilov GV, Kalaeva DB, Vikhrova NB, et al. Radiomics in determining tumor-to-normal brain suv ratio based on 11c-methionine pet/ct in glioblastoma. Sovremennye tehnologii v medicine. 2023;15(1):5–13. (In Russ.) EDN: XDCHTK doi: 10.17691/stm2023.15.1.01</mixed-citation><mixed-citation xml:lang="ru">Данилов Г.В., Калаева Д.Б., Вихрова Н.Б., и др. Технологии радиомики в определении индекса накопления радиофармпрепарата в глиобластоме по данным ПЭТ/КТ с 11с-метионином // Современные технологии в медицине. 2023. Т. 15, № 1. С. 5–13. EDN: XDCHTK doi: 10.17691/stm2023.15.1.01</mixed-citation><mixed-citation xml:lang="zh">Danilov GV, Kalaeva DB, Vikhrova NB, et al. Radiomics in determining tumor-to-normal brain suv ratio based on 11c-methionine pet/ct in glioblastoma. Sovremennye tehnologii v medicine. 2023;15(1):5–13. (In Russ.) EDN: XDCHTK doi: 10.17691/stm2023.15.1.01</mixed-citation></citation-alternatives></ref><ref id="B32"><label>32.</label><citation-alternatives><mixed-citation xml:lang="en">Kapishnikov АV, Surovcev EN, Udalov YuD. Magnetic Resonance Imaging of primary extra-axial intracranial tumors: diagnostic problems and prospects of radiomics. Мedical Radiology and Radiation Safety. 2022;67(4):49–56. EDN: HRDUJG doi: 10.33266/1024-6177-2022-67-4-49-56</mixed-citation><mixed-citation xml:lang="ru">Капишников А.В., Суровцев Е.Н., Удалов Ю.Д. Магнитно-резонансная томография первичных внемозговых опухолей: проблемы диагностики и перспективы радиомики // Медицинская радиология и радиационная безопасность. 2022. Т. 67, № 4. С. 49–56. EDN: HRDUJG doi: 10.33266/1024-6177-2022-67-4-49-56</mixed-citation><mixed-citation xml:lang="zh">Kapishnikov АV, Surovcev EN, Udalov YuD. Magnetic Resonance Imaging of primary extra-axial intracranial tumors: diagnostic problems and prospects of radiomics. Мedical Radiology and Radiation Safety. 2022;67(4):49–56. EDN: HRDUJG doi: 10.33266/1024-6177-2022-67-4-49-56</mixed-citation></citation-alternatives></ref><ref id="B33"><label>33.</label><citation-alternatives><mixed-citation xml:lang="en">Maslov NE, Trufanov GE, Efimtsev AYu. Certain aspects of radiomics and radiogenomics in glioblastoma: what the images hide? Translational medicine. 2022;9(2):70–80. EDN: NMDJBU doi: 10.18705/2311-4495-2022-9-2-70-80</mixed-citation><mixed-citation xml:lang="ru">Маслов Н.Е., Труфанов Г.Е., Ефимцев А.Ю. Некоторые аспекты радиомики и радиогеномики глиобластом: что лежит за пределами изображения? // Трансляционная медицина. 2022. Т. 9, № 2. С. 70–80. EDN: NMDJBU doi: 10.18705/2311-4495-2022-9-2-70-80</mixed-citation><mixed-citation xml:lang="zh">Maslov NE, Trufanov GE, Efimtsev AYu. Certain aspects of radiomics and radiogenomics in glioblastoma: what the images hide? Translational medicine. 2022;9(2):70–80. EDN: NMDJBU doi: 10.18705/2311-4495-2022-9-2-70-80</mixed-citation></citation-alternatives></ref><ref id="B34"><label>34.</label><citation-alternatives><mixed-citation xml:lang="en">Zhang Y, Liang K, He J, et al. Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion. Frontiers in Oncology. 2021;11:665891. doi: 10.3389/fonc.2021.665891</mixed-citation><mixed-citation xml:lang="ru">Zhang Y., Liang K., He J., et al. Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion // Frontiers in Oncology. 2021. Vol. 11. P. 665891. doi: 10.3389/fonc.2021.665891</mixed-citation><mixed-citation xml:lang="zh">Zhang Y, Liang K, He J, et al. Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion. Frontiers in Oncology. 2021;11:665891. doi: 10.3389/fonc.2021.665891</mixed-citation></citation-alternatives></ref><ref id="B35"><label>35.</label><citation-alternatives><mixed-citation xml:lang="en">Solov’yeva SN, Shershever AS, Dayneko EA, et al. Differential diagnostic of a recurrent glial tumor from radiation necrosis by signs of radiomics. Rossiiskii neirokhirurgicheskii zhurnal imeni professora A.L. Polenova. 2023;15(3):128–133. (In Russ.) EDN: LHQOOQ doi: 10.56618/2071-2693_2023_15_3_128</mixed-citation><mixed-citation xml:lang="ru">Соловьева С.Н., Шершевер А.С., Дайнеко Е.А., и др. Дифференциация рецидивирующей глиальной опухоли и лучевого некроза с помощью признаков радиомики // Российский нейрохирургический журнал имени профессора А.Л. Поленова. 2023. Т. 15, № 3. С. 128–133. EDN: LHQOOQ doi: 10.56618/2071-2693_2023_15_3_128</mixed-citation><mixed-citation xml:lang="zh">Solov’yeva SN, Shershever AS, Dayneko EA, et al. Differential diagnostic of a recurrent glial tumor from radiation necrosis by signs of radiomics. Rossiiskii neirokhirurgicheskii zhurnal imeni professora A.L. Polenova. 2023;15(3):128–133. (In Russ.) EDN: LHQOOQ doi: 10.56618/2071-2693_2023_15_3_128</mixed-citation></citation-alternatives></ref><ref id="B36"><label>36.</label><citation-alternatives><mixed-citation xml:lang="en">Dong J, Li L, Liang S, et al. Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach. Academic Radiology. 2021;28(3):318–327. doi: 10.1016/j.acra.2020.02.012</mixed-citation><mixed-citation xml:lang="ru">Dong J., Li L., Liang S., et al. Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach // Academic Radiology. 2021. Vol. 28, N 3. P. 318–327. doi: 10.1016/j.acra.2020.02.012</mixed-citation><mixed-citation xml:lang="zh">Dong J, Li L, Liang S, et al. Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach. Academic Radiology. 2021;28(3):318–327. doi: 10.1016/j.acra.2020.02.012</mixed-citation></citation-alternatives></ref><ref id="B37"><label>37.</label><citation-alternatives><mixed-citation xml:lang="en">Quon JL, Bala W, Chen LC, et al. Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study. American Journal of Neuroradiology. 2020;41(9):1718–1725. doi: 10.3174/ajnr.A6704</mixed-citation><mixed-citation xml:lang="ru">Quon J.L., Bala W., Chen L.C., et al. Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study // American Journal of Neuroradiology. 2020. Vol. 41, N 9. P. 1718–1725. doi: 10.3174/ajnr.A6704</mixed-citation><mixed-citation xml:lang="zh">Quon JL, Bala W, Chen LC, et al. Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study. American Journal of Neuroradiology. 2020;41(9):1718–1725. doi: 10.3174/ajnr.A6704</mixed-citation></citation-alternatives></ref><ref id="B38"><label>38.</label><citation-alternatives><mixed-citation xml:lang="en">Tam LT, Yeom KW, Wright JN, et al. MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study. Neuro-Oncology Advances. 2021;3(1):vdab042. doi: 10.1093/noajnl/vdab042</mixed-citation><mixed-citation xml:lang="ru">Tam L.T., Yeom K.W., Wright J.N., et al. MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study // Neuro-Oncology Advances. 2021. Vol. 3, N 1. P. vdab042. doi: 10.1093/noajnl/vdab042</mixed-citation><mixed-citation xml:lang="zh">Tam LT, Yeom KW, Wright JN, et al. MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study. Neuro-Oncology Advances. 2021;3(1):vdab042. doi: 10.1093/noajnl/vdab042</mixed-citation></citation-alternatives></ref><ref id="B39"><label>39.</label><citation-alternatives><mixed-citation xml:lang="en">Guo W, She D, Xing Z, et al. Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques. Frontiers in Oncology. 2022;12:796583. doi: 10.3389/fonc.2022.796583</mixed-citation><mixed-citation xml:lang="ru">Guo W., She D., Xing Z., et al. Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques // Frontiers in Oncology. 2022. Vol. 12. P. 796583 doi: 10.3389/fonc.2022.796583</mixed-citation><mixed-citation xml:lang="zh">Guo W, She D, Xing Z, et al. Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques. Frontiers in Oncology. 2022;12:796583. doi: 10.3389/fonc.2022.796583</mixed-citation></citation-alternatives></ref><ref id="B40"><label>40.</label><citation-alternatives><mixed-citation xml:lang="en">Shboul ZA, Chen J, Iftekharuddin KM. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Scientific Reports. 2020;10(1):3711. doi: 10.1038/s41598-020-60550-0</mixed-citation><mixed-citation xml:lang="ru">Shboul Z.A., Chen J., Iftekharuddin K.M. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features // Scientific Reports. 2020. Vol. 10, N 1. P. 3711. doi: 10.1038/s41598-020-60550-0</mixed-citation><mixed-citation xml:lang="zh">Shboul ZA, Chen J, Iftekharuddin KM. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Scientific Reports. 2020;10(1):3711. doi: 10.1038/s41598-020-60550-0</mixed-citation></citation-alternatives></ref><ref id="B41"><label>41.</label><citation-alternatives><mixed-citation xml:lang="en">Wagner MW, Hainc N, Khalvati F, et al. Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors. American Journal of Neuroradiology. 2021;42(4):759–765. doi: 10.3174/ajnr.A6998</mixed-citation><mixed-citation xml:lang="ru">Wagner M.W., Hainc N., Khalvati F., et al. Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors // American Journal of Neuroradiology. 2021. Vol. 42, N 4. P. 759–765. doi: 10.3174/ajnr.A6998</mixed-citation><mixed-citation xml:lang="zh">Wagner MW, Hainc N, Khalvati F, et al. Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors. American Journal of Neuroradiology. 2021;42(4):759–765. doi: 10.3174/ajnr.A6998</mixed-citation></citation-alternatives></ref><ref id="B42"><label>42.</label><citation-alternatives><mixed-citation xml:lang="en">Lassaletta A, Zapotocky M, Mistry M, et al. Therapeutic and Prognostic Implications of BRAF V600E in Pediatric Low-Grade Gliomas. Journal of Clinical Oncology. 2017;35(25):2934–2941. doi: 10.1200/JCO.2016.71.8726</mixed-citation><mixed-citation xml:lang="ru">Lassaletta A., Zapotocky M., Mistry M., et al. Therapeutic and Prognostic Implications of BRAF V600E in Pediatric Low-Grade Gliomas // Journal of Clinical Oncology. 2017. Vol. 35, N 25. P. 2934–2941. doi: 10.1200/JCO.2016.71.8726</mixed-citation><mixed-citation xml:lang="zh">Lassaletta A, Zapotocky M, Mistry M, et al. Therapeutic and Prognostic Implications of BRAF V600E in Pediatric Low-Grade Gliomas. Journal of Clinical Oncology. 2017;35(25):2934–2941. doi: 10.1200/JCO.2016.71.8726</mixed-citation></citation-alternatives></ref><ref id="B43"><label>43.</label><citation-alternatives><mixed-citation xml:lang="en">Khalid F, Goya-Outi J, Escobar T, et al. Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities. Frontiers in Medicine. 2023;10:1071447. doi: 10.3389/fmed.2023.1071447</mixed-citation><mixed-citation xml:lang="ru">Khalid F., Goya-Outi J., Escobar T., et al. Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities // Frontiers in Medicine. 2023. Vol. 10. P. 1071447. doi: 10.3389/fmed.2023.1071447</mixed-citation><mixed-citation xml:lang="zh">Khalid F, Goya-Outi J, Escobar T, et al. Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities. Frontiers in Medicine. 2023;10:1071447. doi: 10.3389/fmed.2023.1071447</mixed-citation></citation-alternatives></ref><ref id="B44"><label>44.</label><citation-alternatives><mixed-citation xml:lang="en">Danilov GV, Pronin IN, Korolev VV, et al. MR-guided non-invasive typing of brain gliomas using machine learning. Voprosy neyrokhirurgii imeni N.N. Burdenko. 2022;86(6):36–42. (In Russ.) EDN: JDQJJB doi: 10.17116/neiro20228606136</mixed-citation><mixed-citation xml:lang="ru">Данилов Г.В., Пронин И.Н., Королев В.В., и др. Первые результаты неинвазивного типирования глиом головного мозга по данным магнитно-резонансной томографии с помощью машинного обучения // Вопросы нейрохирургии им. Н.Н. Бурденко. 2022. Т. 86, № 6. С. 36–42. EDN: JDQJJB doi: 10.17116/neiro20228606136</mixed-citation><mixed-citation xml:lang="zh">Danilov GV, Pronin IN, Korolev VV, et al. MR-guided non-invasive typing of brain gliomas using machine learning. Voprosy neyrokhirurgii imeni N.N. Burdenko. 2022;86(6):36–42. (In Russ.) EDN: JDQJJB doi: 10.17116/neiro20228606136</mixed-citation></citation-alternatives></ref><ref id="B45"><label>45.</label><citation-alternatives><mixed-citation xml:lang="en">Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlentherapie Und Onkologie. 2020;196(10):856–867. doi: 10.1007/s00066-020-01626-8</mixed-citation><mixed-citation xml:lang="ru">Kocher M., Ruge M.I., Galldiks N., Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors // Strahlentherapie Und Onkologie. 2020. Vol. 196, N 10. P. 856–867. doi: 10.1007/s00066-020-01626-8</mixed-citation><mixed-citation xml:lang="zh">Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlentherapie Und Onkologie. 2020;196(10):856–867. doi: 10.1007/s00066-020-01626-8</mixed-citation></citation-alternatives></ref><ref id="B46"><label>46.</label><citation-alternatives><mixed-citation xml:lang="en">Kirpichev YuS, Semenov SS, Golub SV, Andreychenko AE. Radiomic in radiation treatment planning. Medical Physics. 2022;(1):36–37. EDN: GPDLZI</mixed-citation><mixed-citation xml:lang="ru">Кирпичев Ю.С., Семенов С.С., Голуб С.В., Андрейченко А.Е. Радиомика при планировании лучевой терапии // Медицинская физика. 2022. № 1. С. 36–37. EDN: GPDLZI</mixed-citation><mixed-citation xml:lang="zh">Kirpichev YuS, Semenov SS, Golub SV, Andreychenko AE. Radiomic in radiation treatment planning. Medical Physics. 2022;(1):36–37. EDN: GPDLZI</mixed-citation></citation-alternatives></ref><ref id="B47"><label>47.</label><citation-alternatives><mixed-citation xml:lang="en">Zhuge Y, Krauze AV, Ning H, et al. Brain tumor segmentation using holistically nested neural networks in MRI images. Medical Physics. 2017;44(10):5234–5243. doi: 10.1002/mp.12481</mixed-citation><mixed-citation xml:lang="ru">Zhuge Y., Krauze A.V., Ning H., et al. Brain tumor segmentation using holistically nested neural networks in MRI images // Medical Physics. 2017. Vol. 44, N 10. P. 5234–5243. doi: 10.1002/mp.12481</mixed-citation><mixed-citation xml:lang="zh">Zhuge Y, Krauze AV, Ning H, et al. Brain tumor segmentation using holistically nested neural networks in MRI images. Medical Physics. 2017;44(10):5234–5243. doi: 10.1002/mp.12481</mixed-citation></citation-alternatives></ref></ref-list></back></article>
