Prospects for the application of radiomics to brain tumors
Olga S. Regentova , Roman A. Parkhomenko , Nikolay I. Sergeyev , Vladimir K. Bozhenko , Pavel V. Polushkin , Vladimir А. Solodkiy
Digital Diagnostics ›› 2024, Vol. 5 ›› Issue (3) : 567 -577.
Prospects for the application of radiomics to brain tumors
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.
malignant tumors / radiomics / radiogenomics / artificial intelligence / brain / neurooncology
| [1] |
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 |
| [2] |
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 |
| [3] |
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 |
| [4] |
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 |
| [5] |
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 |
| [6] |
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 |
| [7] |
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 |
| [8] |
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 |
| [9] |
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 |
| [10] |
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 |
| [11] |
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 |
| [12] |
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 |
| [13] |
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 |
| [14] |
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 |
| [15] |
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 |
| [16] |
Chernobrivtseva VV, Misyurin AS. New technologies in radiology diagnostics. Practical Oncology. 2022;23(4):203–210. EDN: DUCVOW doi: 10.31917/2304203 |
| [17] |
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 |
| [18] |
Chernobrivtseva VV, Misyurin AS. New technologies in radiology diagnostics. Practical Oncology. 2022;23(4):203–210. EDN: DUCVOW doi: 10.31917/2304203 |
| [19] |
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 |
| [20] |
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 |
| [21] |
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 |
| [22] |
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 |
| [23] |
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 |
| [24] |
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 |
| [25] |
Prokop M. Multislice CT: technical principles and future trends. European Radiology. 2003;13 Suppl. 5:M3–13. doi: 10.1007/s00330-003-2178-z |
| [26] |
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 |
| [27] |
Prokop M. Multislice CT: technical principles and future trends. European Radiology. 2003;13 Suppl. 5:M3–13. doi: 10.1007/s00330-003-2178-z |
| [28] |
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 |
| [29] |
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 |
| [30] |
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 |
| [31] |
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 |
| [32] |
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 |
| [33] |
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 |
| [34] |
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 |
| [35] |
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 |
| [36] |
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 |
| [37] |
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 |
| [38] |
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 |
| [39] |
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 |
| [40] |
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 |
| [41] |
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 |
| [42] |
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 |
| [43] |
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 |
| [44] |
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 |
| [45] |
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 |
| [46] |
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 |
| [47] |
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. |
| [48] |
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 |
| [49] |
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 |
| [50] |
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 |
| [51] |
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 |
| [52] |
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 |
| [53] |
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 |
| [54] |
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 |
| [55] |
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 |
| [56] |
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 |
| [57] |
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 |
| [58] |
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 |
| [59] |
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 |
| [60] |
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 |
| [61] |
Wang Y, Jin ZY. Radiomics approaches in gastric cancer. Chinese Medical Journal. 2019;132(16):1983–1989. doi: 10.1097/CM9.0000000000000360 |
| [62] |
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 |
| [63] |
Wang Y, Jin ZY. Radiomics approaches in gastric cancer. Chinese Medical Journal. 2019;132(16):1983–1989. doi: 10.1097/CM9.0000000000000360 |
| [64] |
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 |
| [65] |
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 |
| [66] |
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 |
| [67] |
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 |
| [68] |
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 |
| [69] |
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 |
| [70] |
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 |
| [71] |
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 |
| [72] |
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 |
| [73] |
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 |
| [74] |
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 |
| [75] |
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 |
| [76] |
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 |
| [77] |
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 |
| [78] |
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 |
| [79] |
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 |
| [80] |
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 |
| [81] |
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 |
| [82] |
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 |
| [83] |
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 |
| [84] |
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 |
| [85] |
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 |
| [86] |
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 |
| [87] |
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 |
| [88] |
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 |
| [89] |
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 |
| [90] |
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 |
| [91] |
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 |
| [92] |
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 |
| [93] |
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 |
| [94] |
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 |
| [95] |
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 |
| [96] |
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 |
| [97] |
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 |
| [98] |
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 |
| [99] |
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 |
| [100] |
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 |
| [101] |
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 |
| [102] |
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 |
| [103] |
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 |
| [104] |
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 |
| [105] |
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 |
| [106] |
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 |
| [107] |
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 |
| [108] |
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 |
| [109] |
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 |
| [110] |
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 |
| [111] |
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 |
| [112] |
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 |
| [113] |
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 |
| [114] |
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 |
| [115] |
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 |
| [116] |
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 |
| [117] |
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 |
| [118] |
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 |
| [119] |
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 |
| [120] |
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 |
| [121] |
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 |
| [122] |
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 |
| [123] |
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 |
| [124] |
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 |
| [125] |
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 |
| [126] |
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 |
| [127] |
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 |
| [128] |
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 |
| [129] |
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 |
| [130] |
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 |
| [131] |
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 |
| [132] |
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 |
| [133] |
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 |
| [134] |
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 |
| [135] |
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 |
| [136] |
Kirpichev YuS, Semenov SS, Golub SV, Andreychenko AE. Radiomic in radiation treatment planning. Medical Physics. 2022;(1):36–37. EDN: GPDLZI |
| [137] |
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 |
| [138] |
Kirpichev YuS, Semenov SS, Golub SV, Andreychenko AE. Radiomic in radiation treatment planning. Medical Physics. 2022;(1):36–37. EDN: GPDLZI |
| [139] |
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 |
| [140] |
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 |
| [141] |
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 |
Eco-Vector
/
| 〈 |
|
〉 |