Радиомика — фронтир современной ревматологии: достижения и перспективы визуализации поражения органов грудной клетки

На примере ревматологии в статье рассмотрены современные тенденции развития цифровых технологий в медицине; обсуждается значение радиомики, совмещающей радиологию, математическое моделирование и глубокое машинное обучение. Текстурный анализ изображений компьютерной томографии и других методов визуализации более глубоко характеризует патофизиологические особенности тканей и может рассматриваться как неинвазивная «виртуальная биопсия». Показано, что радиомика увеличивает качество диагностического и предсказательного моделирования. Обсуждается потенциал применения радиомических моделей для изучения и прогнозирования поражения органов грудной клетки при различных патологических состояниях, включая иммуновоспалительные ревматические заболевания (РЗ). Прогресс в диагностике и лечении РЗ может быть обеспечен интеграцией радиомики и омиксных технологий. Цифровая эра, открывающая широкие перспективы для прогресса в ревматологии, несомненно, потребует комплексного решения новых проблем, технических, юридических и этических.

1. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: The process and the challenges. Magn Reson Imaging. 2012;30(9):1234-1248. doi: 10.1016/j.mri.2012.06.010

2. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762. doi: 10.1038/nrclinonc.2017.141

3. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: Definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483-1503. doi: 10.1148/rg.2017170056

4. Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, et al. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med. 2022;145:105467. doi: 10.1016/j.compbiomed.2022.105467

5. Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L. A review of original articles published in the emerging field of radiomics. Eur J Radiol. 2020;127:108991. doi: 10.1016/j.ejrad.2020.108991

6. Verma V, Simone CB 2nd, Krishnan S, Lin SH, Yang J, Hahn SM. The rise of radiomics and implications for oncologic management. J Natl Cancer Inst. 2017;109(7). doi: 10.1093/jnci/djx055

7. Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges. Theranostics. 2019;9(5):1303-1322. doi: 10.7150/thno.30309

8. Galloway MM. Texture analysis using gray level run lengths. Comp Graphics Image Process. 1975;4(2):172-179. doi: 10.1016/S0146-664X(75)80008-6

9. Kaizer H. A quantification of textures on aerial photographs. Tech Note. Boston University Research Lab. 1955;121:69484.

10. Hall EL, Kruger RP, Dwyer SJ, Hall DL, Mclaren RW, Lodwick GS. A survey of preprocessing and feature extraction techniques for radiographic images. IEEE Transact Comp. 1971;C-20(9):1032-1044. doi: 10.1109/T-C.1971.223399

11. Shur JD, Doran SJ, Kumar S, Ap Dafydd D, Downey K, O’Connor JPB, et al. Radiomics in oncology: A practical guide. Radiographics. 2021;41(6):1717-1732. doi: 10.1148/rg.2021210037

12. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441-446. doi: 10.1016/j.ejca.2011.11.036

13. Yang B, Guo L, Lu G, Shan W, Duan L, Duan S. Radiomic signature: A non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma. Cancer Manag Res. 2019;11:7825-7834. doi: 10.2147/CMAR.S217887

14. Литвин АА, Буркин ДА, Кропинов АА, Парамзин ФН. 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

15. Ye L, Miao S, Xiao Q, Liu Y, Tang H, Li B, et al. A predictive clinical-radiomics nomogram for diagnosing of axial spondyloarthritis using MRI and clinical risk factors. Rheumatology (Oxford). 2022;61(4):1440-1447. doi: 10.1093/rheumatology/keab542

16. Shi Z, Yang Z, Su D, Cheng J, Shi Z, Wang M, et al. Prediction of the activity of early ankylosing spondylitis using radiomics texture analysis on short tau inversion recovery (STIR). Clin Exp Rheumatol. 2024;42(7):1427-1434. doi: 10.55563/clinexprheumatol/99pc16

17. Jiang T, Lau SH, Zhang J, Chan LC, Wang W, Chan PK, et al. Radiomics signature of osteoarthritis: Current status and perspective. J Orthop Translat. 2024;45:100-106. doi: 10.1016/j.jot.2023.10.003

18. Davey MS, Davey MG, Kenny P, Gheiti AJC. The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee – A systematic review and meta-analysis. Irish J Med Sci. 2024;193(5):2525-2530. doi: 10.1007/s11845-024-03714-5

19. Zhang L, Chen Z, Feng L, Guo L, Liu D, Hai J, et al. Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy. BMC Med Imaging. 2021;21(1):115. doi: 10.1186/s12880-021-00647-8

20. Qin X, Xia L, Zhu C, Hu X, Xiao W, Xie X, et al. Noninvasive evaluation of lupus nephritis activity using a radiomics machine learning model based on ultrasound. J Inflamm Res. 2023;16:433-441. doi: 10.2147/JIR.S398399

21. Choi YH, Kim JE, Lee RW, Kim B, Shin HC, Choe M, et al. Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy. BMC Med Imaging. 2024;24(1):256. doi: 10.1186/s12880-024-01434-x

22. Venerito V, Manfredi A, Lopalco G, Lavista M, Cassone G, Scardapane A, et al. Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: A proof-of-concept study. Front Med (Lausanne). 2023;9:1069486. doi: 10.3389/fmed.2022.1069486

23. Насонов ЕЛ, Ананьева ЛП, Авдеев СН. Интерстициальные заболевания легких при ревматоидном артрите: мультидисциплинарная проблема ревматологии и пульмонологии. Научно-практическая ревматология. 2022;60(6):517-534. doi: 10.47360/1995-4484-2022-1

24. Mohammad AJ, Mortensen KH, Babar J, Smith R, Jones RB, Nakagomi D, et al. Pulmonary involvement in antineutrophil cytoplasmic antibodies (ANCA)-associated vasculitis: The influence of ANCA subtype. J Rheumatol. 2017;44(10):1458-1467. doi: 10.3899/jrheum.161224

25. Акулкина ЛА, Бровко МЮ, Шоломова ВИ, Янакаева АШ, Буланов НМ, Новиков ПИ, и др. АНЦА-ассоциированные интерстициальные заболевания легких: актуальные вопросы диагностики и лечения. Клиническая фармакология и терапия. 2019;28(4):76-82. doi: 10.32756/0869-5490-2019-4-76-82

26. Бекетова ТВ. Гранулематоз с полиангиитом, патогенетически связанный с антинейтрофильными цитоплазматическими антителами: особенности клинического течения. Научно-практическая ревматология. 2012;50(6):19-28. doi: 10.14412/1995-4484-2012-1288

27. Бекетова ТВ. Микроскопический полиангиит, ассоциированный с антинейтрофильными цитоплазматическими антителами: особенности клинического течения. Терапевтический архив. 2015;87(5):33-46. doi: 10.17116/terarkh201587533-46

28. Ronneberger O, Fischer P. Brox T. U-net: Convolutional networks for biomedical image segmentation. arXiv. 2015;1505.04597 doi: 10.1007/978-3-319-24574-4_28

29. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice? Insights Imag. 2012;3(6):573-589. doi: 10.1007/s13244-012-0196-6

30. O’Brien H, Williams MC, Rajani R, Niederer S. Radiomics and machine learning for detecting scar tissue on CT delayed enhancement imaging. Front Cardiovasc Med. 2022;9:847825. doi: 10.3389/fcvm.2022.847825

31. Shi L, Sheng M, Wei Z, Liu L, Zhao J. CT-based radiomics predicts the malignancy of pulmonary nodules: A systematic review and meta-analysis. Acad Radiol. 2023;30(12):3064-3075. doi: 10.1016/j.acra.2023.05.026

32. Shang H, Li J, Jiao T, Fang C, Li K, Yin D, et al. Differentiation of lung metastases originated from different primary tumors using radiomics features based on CT imaging. Acad Radiol. 2023;30(1):40-46. doi: 10.1016/j.acra.2022.04.008

33. Qi J, Deng Z, Sun G, Qian S, Liu L, Xu B. One-step algorithm for fast-track localization and multi-category classification of histological subtypes in lung cancer. Eur J Radiol. 2022;154:110443. doi: 10.1016/j.ejrad.2022.110443

34. Lovinfosse P, Ferreira M, Withofs N, Jadoul A, Derwael C, Frix AN, et al. Distinction of lymphoma from sarcoidosis on 18F-FDG PET/CT: Evaluation of radiomics-feature-guided machine learning versus human reader performance. J Nucl Med. 2022;63(12):1933-1940. doi: 10.2967/jnumed.121.263598

35. Li T, Gan T, Wang J, Long Y, Zhang K, Liao M. Application of CT radiomics in brain metastasis of lung cancer: A systematic review and meta-analysis. Clin Imaging. 2024;114:110275. doi: 10.1016/j.clinimag.2024.110275

36. Li Y, Lyu B, Wang R, Peng Y, Ran H, Zhou B, et al. Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis. Thorac Cancer. 2024;15(6):466-476. doi: 10.1111/1759-7714.15216

37. Dong Q, Wen Q, Li N, Tong J, Li Z, Bao X, et al. Radiomics combined with clinical features in distinguishing non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules. PeerJ. 2022;10:e14127. doi: 10.7717/peerj.14127

38. Feng B, Chen X, Chen Y, Lu S, Liu K, Li K, et al. Solitary solid pulmonary nodules: A CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas. Eur Radiol. 2020;30(12):6497-6507. doi: 10.1007/s00330-020-07024-z

39. Cui EN, Yu T, Shang SJ, Wang XY, Jin YL, Dong Y, et al. Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans. World J Clin Cases. 2020;8(21):5203-5212. doi: 10.12998/wjcc.v8.i21.5203

40. Zhao W, Xiong Z, Jiang Y, Wang K, Zhao M, Lu X, et al. Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses. J Cancer Res Clin Oncol. 2023;149(7):3395-3408. doi: 10.1007/s00432-022-04256-y

41. Yao W, Liao Y, Li X, Zhang F, Zhang H, Hu B, et al. Noninvasive method for predicting the expression of Ki67 and prognosis in non-small-cell lung cancer patients: Radiomics. J Healthc Eng. 2022;2022:7761589. doi: 10.1155/2022/7761589

42. Felfli M, Liu Y, Zerka F, Voyton C, Thinnes A, Jacques S, et al. Systematic review, meta-analysis and radiomics quality score assessment of CT radiomics-based models predicting tumor EGFR mutation status in patients with non-small-cell lung cancer. Int J Mol Sci. 2023;24(14):11433. doi: 10.3390/ijms241411433

43. Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol. 2023;93:97-113. doi: 10.1016/j.semcancer.2023.05.004

44. Hao P, Deng BY, Huang CT, Xu J, Zhou F, Liu ZX, et al. Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images. Front Oncol. 2022;12:994285. doi: 10.3389/fonc.2022.994285

45. Shao J, Ma J, Zhang S, Li J, Dai H, Liang S, et al. Radiogenomic system for non-invasive identification of multiple actionable mutations and PD-L1 expression in non-small cell lung cancer based on CT images. Cancers (Basel). 2022;14(19):4823. doi: 10.3390/cancers14194823

46. Zhu ZC, Chen MJ, Song L, Wang JH, Hu G, Han W, et al. [CT-based weighted radiomic score predicts tumor response to immunotherapy in non-small cell lung cancer]. Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2023;45(5):794-802 (In Chinese). doi: 10.3881/j.issn.1000-503X.15705

47. Chang R, Qi S, Zuo Y, Yue Y, Zhang X, Guan Y, et al. Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined? Front Oncol. 2022;12:915835. doi: 10.3389/fonc.2022.915835

48. Luo T, Yan M, Zhou M, Dekker A, Appelt AL, Ji Y, et al. Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters. Radiol Med. 2024 Nov 14. doi: 10.1007/s11547-024-01919-3

49. Schniering J, Maciukiewicz M, Gabrys HS, Brunner M, Blüthgen C, Meier C, et al. Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis. Eur Respir J. 2022;59(5):2004503. doi: 10.1183/13993003.04503-2020

50. Martini K, Baessler B, Bogowicz M, Blüthgen C, Mannil M, Tanadini-Lang S, et al. Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: Proof of concept. Eur Radiol. 2021;31(4):1987-1998. doi: 10.1007/s00330-020-07293-8

51. Haga A, Iwasawa T, Misumi T, Okudela K, Oda T, Kitamura H, et al. Correlation of CT-based radiomics analysis with pathological cellular infiltration in fibrosing interstitial lung diseases. Jpn J Radiol. 2024;42(10):1157-1167. doi: 10.1007/s11604-024-01607-2

52. Feng DY, Zhou YQ, Xing YF, Li CF, Lv Q, Dong J, et al. Selection of glucocorticoid-sensitive patients in interstitial lung disease secondary to connective tissue diseases population by radiomics. Ther Clin Risk Manag. 2018;14:1975-1986. doi: 10.2147/TCRM.S181043

53. Chen J, Meng T, Xu J, Ooi JD, Eggenhuizen PJ, Liu W, et al. Development of a radiomics nomogram to predict the treatment resistance of Chinese MPO-AAV patients with lung involvement: A two-center study. Front Immunol. 2023;14:1084299. doi: 10.3389/fimmu.2023.1084299

54. Falde S, Specks U, Bartholmai BJ, Rajagopalan S, Cartin-Ceba R, Peikert T. Predicting disease relapse in ANCA associated vasculitis with radiomics. Am J Respir Crit Care Med. 2024;209:A4502. doi: 10.1164/ajrccm-conference.2024.209.1_MeetingAbstracts.A4502

55. Renaud A, Pautre R, Morla O, Achille A, Durant C, Espitia O, et al. Thoracic lymphadenopathies in diffuse systemic sclerosis: An observational study on 48 patients using computed tomography. BMC Pulm Med. 2022;22(1):44. doi: 10.1186/s12890-022-01837-y

56. Rotondo C, Urso L, Praino E, Cacciapaglia F, Corrado A, Cantatore FP, et al. Thoracic lymphadenopathy as possible predictor of the onset of interstitial lung disease in systemic sclerosis patients without lung involvement at baseline visit: A retrospective analysis. J Scleroderma Relat Disord. 2020;5(3):210-218. doi: 10.1177/2397198320923545

57. Sgalla G, Larici AR, Golfi N, Calvello M, Farchione A, Del Ciello A, et al. Mediastinal lymph node enlargement in idiopathic pulmonary fibrosis: Relationships with disease progression and pulmonary function trends. BMC Pulm Med. 2020;20(1):249. doi: 10.1186/s12890-020-01289-2

58. Venerito V, Manfredi A, Lopalco G, Lavista M, Cassone G, Scardapane A, et al. Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: A proof-of-concept study. Front Med (Lausanne). 2023;9:1069486. doi: 10.3389/fmed.2022.1069486

59. Han N, Guo Z, Zhu D, Zhang Y, Qin Y, Li G, et al. A nomogram model combining computed tomography-based radiomics and Krebs von den Lungen-6 for identifying low-risk rheumatoid arthritis-associated interstitial lung disease. Front Immunol. 2024;15:1417156. doi: 10.3389/fimmu.2024.1417156

60. He W, Cui B, Chu Z, Chen X, Liu J, Pang X, et al. Radiomics based on HRCT can predict RP-ILD and mortality in anti-MDA5+dermatomyositis patients: A multi-center retrospective study. Respir Res. 2024;25(1):252. doi: 10.1186/s12931-024-02843-w

61. Li Y, Deng W, Zhou Y, Luo Y, Wu Y, Wen J, et al. A nomogram based on clinical factors and CT radiomics for predicting anti-MDA5+ DM complicated by RP-ILD. Rheumatology (Oxford). 2024;63(3):809-816. doi: 10.1093/rheumatology/kead263

62. Ryan SM, Fingerlin TE, Mroz M, Barkes B, Hamzeh N, Maier LA, et al. Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis. Eur Respir J. 2019;54(2):1900371. doi: 10.1183/13993003.00371-2019

63. Węcławek M, Ziora D, Jastrzębski D. Imaging methods for pulmonary sarcoidosis. Adv Respir Med. 2020;88(1):18-27. doi: 10.5603/ARM.2020.0074

64. Shaikh F, Abtin FG, Lau R, Saggar R, Belperio JA, Lynch JP 3rd. Radiographic and histopathologic features in sarcoidosis: A pictorial display. Semin Respir Crit Care Med. 2020;41(5):758-784. doi: 10.1055/s-0040-1712534

65. Osborne MT, Hulten EA, Murthy VL, Skali H, Taqueti VR, Dorbala S, et al. Patient preparation for cardiac fluorine-18 fluorodeoxyglucose positron emission tomography imaging of inflammation. J Nucl Cardiol. 2017;24(1):86-99. doi: 10.1007/s12350-016-0502-7

66. Mushari NA, Soultanidis G, Duff L, Trivieri MG, Fayad ZA, Robson P, et al. An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis. Front Nucl Med. 2024;4:1324698. doi: 10.3389/fnume.2024.1324698

67. Antonopoulos AS, Boutsikou M, Simantiris S, Angelopoulos A, Lazaros G, Panagiotopoulos I, et al. Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes. Sci Rep. 2021;11(1):23596. doi: 10.1038/s41598-021-02971-z

68. Chen Q, Li H, Xie W, Abudukeremu A, Wen K, Liu W, et al. Lipid-related radiomics of low-echo carotid plaques is associated with diabetic stroke and non-diabetic coronary heart disease. Int J Cardiovasc Imaging. 2025;41(1):123-136. doi: 10.1007/s10554-024-03296-4

69. Imamura H, Sekiguchi Y, Iwashita T, Dohgomori H, Mochizuki K, Aizawa K, et al. Painless acute aortic dissection. Diagnostic, prognostic and clinical implications. Circ J. 2011;75(1):59-66. doi: 10.1253/circj.cj-10-0183

70. Boileau A, Lindsay M, Michel JB, Devaux Y. Epigenetics in ascending thoracic aortic aneurysm and dissection. AORTA. 2018;06(01):1-12. doi: 10.1055/s-0038-1639610

71. Nienaber CA, Clough RE. Management of acute aortic dissection. The Lancet. 2015;385(9970):800-811. doi: 10.1016/S0140-6736(14)61005-9

72. Zhou Z, Yang J, Wang S, Li W, Xie L, Li Y, et al. The diagnostic value of a non-contrast computed tomography scan-based radiomics model for acute aortic dissection. Medicine (Baltimore). 2021;100(22):e26212. doi: 10.1097/MD.0000000000026212

73. Guo Y, Chen X, Lin X, Chen L, Shu J, Pang P, et al. Non-contrast CT-based radiomic signature for screening thoracic aortic dissections: A multicenter study. Eur Radiol. 2021;31(9):7067-7076. doi: 10.1007/s00330-021-07768-2

74. Liang L, Liu M, Martin C, Elefteriades JA, Sun W. A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm. Biomech Model Mechanobiol. 2017;16(5):1519-1533. doi: 10.1007/s10237-017-0903-9

75. Rezaeitaleshmahalleh M, Lyu Z, Mu N, Zhang X, Rasmussen TE, McBane RD 2nd, et al. Characterization of small abdominal aortic aneurysms’ growth status using spatial pattern analysis of aneurismal hemodynamics. Sci Rep. 2023;13(1):13832. doi: 10.1038/s41598-023-40139-z

76. Rezaeitaleshmahalleh M, Mu N, Lyu Z, Zhou W, Zhang X, Rasmussen TE, et al. Radiomic-based textural analysis of intraluminal thrombus in aortic abdominal aneurysms: A demonstration of automated workflow. J Cardiovasc Transl Res. 2023;16(5):1123-1134. doi: 10.1007/s12265-023-10404-7

77. Wang Y, Liu F, Wu S, Sun K, Gu H, Wang X. CTA-based radiomics and area change rate predict infrarenal abdominal aortic aneurysms patients events: A multicenter study. Acad Radiol. 2024;31(8):3165-3176. doi: 10.1016/j.acra.2024.01.017

78. Ma Z, Jin L, Zhang L, Yang Y, Tang Y, Gao P, et al. Diagnosis of acute aortic syndromes on non-contrast CT images with radiomics-based machine learning. Biology (Basel). 2023;12(3):337. doi: 10.3390/biology12030337

79. Salmasi MY, Al-Saadi N, Hartley P, Jarral OA, Raja S, Hussein M, et al. The risk of misdiagnosis in acute thoracic aortic dissection: A review of current guidelines. Heart. 2020;106(12):885-891. doi: 10.1136/heartjnl-2019-316322

80. Huellebrand M, Jarmatz L, Manini C, Laube A, Ivantsits M, Schulz-Menger J, et al. Radiomics-based aortic flow profile characterization with 4D phase-contrast MRI. Front Cardiovasc Med. 2023;10:1102502. doi: 10.3389/fcvm.2023.1102502

81. Aghayev A, Weber B, Lins de Carvalho T, Glaudemans AWJM, Nienhuis PH, van der Geest KSM, et al. Multimodality imaging to assess diagnosis and evaluate complications of large vessel arteritis. J Nucl Cardiol. 2024;37:101864. doi: 10.1016/j.nuclcard.2024.101864

82. Duff L, Scarsbrook AF, Mackie SL, Frood R, Bailey M, Morgan AW, et al. A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis. J Nucl Cardiol. 2022;29(6):3315-3331. doi: 10.1007/s12350-022-02927-4

83. Duff LM, Scarsbrook AF, Ravikumar N, Frood R, van Praagh GD, Mackie SL, et al. An automated method for artifical intelligence assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images. Biomolecules. 2023;13(2):343. doi: 10.3390/biom13020343

84. Badesha AS, Frood R, Bailey MA, Coughlin PM, Scarsbrook AF. A scoping review of machine-learning derived radiomic analysis of CT and PET imaging to investigate atherosclerotic cardiovascular disease. Tomography. 2024;10(9):1455-1487. doi: 10.3390/tomography10090108

85. Zaccagna F, Ganeshan B, Arca M, Rengo M, Napoli A, Rundo L, et al. CT texture-based radiomics analysis of carotid arteries identifies vulnerable patients: A preliminary outcome study. Neuroradiology. 2021;63(7):1043-1052. doi: 10.1007/s00234-020-02628-0

86. Le EPV, Wong MYZ, Rundo L, Tarkin JM, Evans NR, Weir-McCall JR, et al. Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach. Eur J Radiol Open. 2024;13:100594. doi: 10.1016/j.ejro.2024.100594

87. Liu M, Chang N, Zhang S, Du Y, Zhang X, Ren W, et al. Identification of vulnerable carotid plaque with CT-based radiomics nomogram. Clin Radiol. 2023;78(11):e856-e863. doi: 10.1016/j.crad.2023.07.018

88. Dong Z, Zhou C, Li H, Shi J, Liu J, Liu Q, et al. Radiomics versus conventional assessment to identify symptomatic participants at carotid computed tomography angiography. Cerebrovasc Dis. 2022;51(5):647-654. doi: 10.1159/000522058

89. Zhang R, Zhang Q, Ji A, Lv P, Zhang J, Fu C, et al. Identification of high-risk carotid plaque with MRI-based radiomics and machine learning. Eur Radiol. 2021;31(5):3116-3126. doi: 10.1007/s00330-020-07361-z

90. Xia H, Yuan L, Zhao W, Zhang C, Zhao L, Hou J, et al. Predicting transient ischemic attack risk in patients with mild carotid stenosis using machine learning and CT radiomics. Front Neurol. 2023;14:1105616. doi: 10.3389/fneur.2023.1105616

91. Chen C, Tang W, Chen Y, Xu W, Yu N, Liu C, et al. Computed tomography angiography-based radiomics model to identify high-risk carotid plaques. Quant Imaging Med Surg. 2023;13(9):6089-6104. doi: 10.21037/qims-23-158

92. Nie JY, Chen WX, Zhu Z, Zhang MY, Zheng YJ, Wu QD. Initial experience with radiomics of carotid perivascular adipose tissue in identifying symptomatic plaque. Front Neurol. 2024;15:1340202. doi: 10.3389/fneur.2024.1340202

93. Shan D, Wang S, Wang J, Lu J, Ren J, Chen J, et al. Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability. Front Neurol. 2023;14:1151326. doi: 10.3389/fneur.2023.1151326

94. Kafouris PP, Koutagiar IP, Georgakopoulos AT, Spyrou GM, Visvikis D, Anagnostopoulos CD. Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques. J Nucl Cardiol. 2021;28(5):1861-1871. doi: 10.1007/s12350-019-01943-1

95. Cilla S, Macchia G, Lenkowicz J, Tran EH, Pierro A, Petrella L, et al. CT angiography-based radiomics as a tool for carotid plaque characterization: A pilot study. Radiol Med. 2022;127(7):743-753. doi: 10.1007/s11547-022-01505-5

96. Van der Geest KSM, Gheysens O, Gormsen LC, Glaudemans AWJM, Tsoumpas C, Brouwer E, et al. Advances in PET imaging of large vessel vasculitis: An update and future trends. Semin Nucl Med. 2024;54(5):753-760. doi: 10.1053/j.semnuclmed.2024.03.001

97. Gu J, Jiang T. Ultrasound radiomics in personalized breast management: Current status and future prospects. Front Oncol. 2022;12:963612. doi: 10.3389/fonc.2022.963612

98. Shi S, An X, Li Y. Ultrasound radiomics-based logistic regression model to differentiate between benign and malignant breast nodules. J Ultrasound Med. 2023;42(4):869-879. doi: 10.1002/jum.16078

99. Wu L, Zhao Y, Lin P, Qin H, Liu Y, Wan D, et al. Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ. BMC Med Imaging. 2021;21(1):84. doi: 10.1186/s12880-021-00610-7

100. Cai L, Sidey-Gibbons C, Nees J, Riedel F, Schaefgen B, Togawa R, et al. Ultrasound radiomics features to identify patients with triple-negative breast cancer: A retrospective, single-center study. J Ultrasound Med. 2024;43(3):467-478. doi: 10.1002/jum.16377

101. Lu G, Tian R, Yang W, Liu R, Liu D, Xiang Z, et al. Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours. Front Med (Lausanne). 2024;11:1402967. doi: 10.3389/fmed.2024.1402967

102. Liebowitz JE. The metaverse: A new frontier for rheumatology. Rheumatology. 2024;63(2):267-268. doi: 10.1093/rheumatology/kead534

103. Wang G, Badal A, Jia X, Maltz JS, Mueller K, Myers KJ, et al. Development of metaverse for intelligent healthcare. Nat Mach Intell. 2022;4(11):922-929. doi: 10.1038/s42256-022-00549-6

104. Zanfardino M, Franzese M, Pane K, Cavaliere C, Monti S, Esposito G, et al. Bringing radiomics into a multi-omics frame-work for a comprehensive genotype-phenotype characterization of oncological diseases. J Transl Med. 2019;17(1):337. doi: 10.1186/s12967-019-2073-2