rentradВестник рентгенологии и радиологииJournal of radiology and nuclear medicine0042-46762619-0478Limited Liability Company "LUCHEVAYA DIAGNOSTIKA", Russian Association of Radiologists10.20862/0042-4676-2023-104-4-270-278rentrad-822Research ArticleОБЗОРЫREVIEWSРадиомика в диагностике узловых образований щитовидной железыRadiomics in the Diagnosis of Thyroid Noduleshttps://orcid.org/0009-0006-2708-0305ТокмачеваА. А.TokmachevaA. A.

Токмачева Ангелина Алексеевна, ассистент кафедры

ул. Ленина, 3, Уфа, 450008

Angelina A. Tokmacheva, Assistant Professor

ul. Lenina, 3, Ufa, 450008

vkomissiya@inbox.ruhttps://orcid.org/0009-0001-6668-8544ВялкинД. С.VyalkinD. S.

Вялкин Дмитрий Сергеевич, ассистент кафедры

ул. Студенческая, 10, Воронеж, 394036

Dmitry S. Vyalkin, Assistant Professor

ul. Studencheskaya, 10, Voronezh, 394036

https://orcid.org/0009-0002-4901-3382ТроцА. А.TrotsA. A.

Троц Алина Алексеевна, ординатор

пер. Нахичеванский, 29, Ростов-на-Дону, 344022

Alina A. Trots, Resident

Nakhichevanskiy pereulok, 29, Rostov-on-Don, 344022

https://orcid.org/0000-0002-3753-3412ТаракановаЕ. Е.TarakanovaE. E.

Тараканова Елизавета Евгеньевна, студентка

ул. Ленина, 3, Уфа, 450008

Elizaveta E. Tarakanova, Student

ul. Lenina, 3, Ufa, 450008

https://orcid.org/0000-0003-1967-7487ДавлетоваЮ. И.DavletovaYu. I.

Давлетова Юлия Ильдаровна, студентка

ул. Ленина, 3, Уфа, 450008

Yulia I. Davletova, Student

ul. Lenina, 3, Ufa, 450008

https://orcid.org/0000-0003-0016-3312АбдуллинаЭ. Л.AbdullinaE. L.

Абдуллина Эллина Ленаровна, студентка

ул. Ленина, 3, Уфа, 450008

Ellina L. Abdullina, Student

ul. Lenina, 3, Ufa, 450008

https://orcid.org/0000-0002-4586-2021СтепанадзеВ. Б.StepnadzeV. B.

Степнадзе Вахтанг Бежанович, студент

Московский пр-т, 15, Чебоксары, 428015

Vakhtang B. Stepnadze, Student

Moskovskiy prospekt, 15, Cheboksary, 428015

https://orcid.org/0009-0003-0385-209XАхметоваА. И.AkhmetovaA. I.

Ахметова Альмира Ильгамовна, ординатор кафедры

ул. Ленина, 3, Уфа, 450008

Almira I. Akhmetova, Resident

ul. Lenina, 3, Ufa, 450008

https://orcid.org/0000-0003-0119-4516ШагиеваН. Э.ShagievaN. E.

Шагиева Нурия Эльвировна, студентка

ул. Ленина, 3, Уфа, 450008

Nuriya E. Shagieva, Student

ul. Lenina, 3, Ufa, 450008

https://orcid.org/0000-0003-2225-5218УсковаВ. Д.UskovaV. D.

Ускова Варвара Дмитриевна, студентка

ул. Ленина, 3, Уфа, 450008

Varvara D. Uskova, Student

ul. Lenina, 3, Ufa, 450008

https://orcid.org/0000-0001-9497-0419КоноваловаВ. С.KonovalovaV. S.

Коновалова Виктория Сергеевна, студентка

ул. Ленина, 3, Уфа, 450008

Victoria S. Konovalova, Student

ul. Lenina, 3, Ufa, 450008

https://orcid.org/0000-0002-3753-3412МагдановаА. Р.MagdanovaA. R.

Магданова Айгуль Рустемовна – студентка

ул. Ленина, 3, Уфа, 450008

Aigul R. Magdanova, Student

ul. Lenina, 3, Ufa, 450008

ФГБОУ ВО «Башкирский государственный медицинский университет» Минздрава РоссииРоссияBashkir State Medical UniversityRussian FederationФГБОУ ВО «Воронежский государственный медицинский университет им. Н.Н. Бурденко» Минздрава РоссииРоссияBurdenko Voronezh State Medical UniversityRussian FederationФГБОУ ВО «Ростовский государственный медицинский университет» Минздрава РоссииРоссияRostov State Medical UniversityRussian FederationФГБОУ ВО «Чувашский государственный университет им. И.Н. Ульянова»РоссияUlyanov Chuvash State UniversityRussian Federation2023220120241044270278Copyright © Токмачева А.А., Вялкин Д.С., Троц А.А., Тараканова Е.Е., Давлетова Ю.И., Абдуллина Э.Л., Степанадзе В.Б., Ахметова А.И., Шагиева Н.Э., Ускова В.Д., Коновалова В.С., Магданова А.Р., 20242024Токмачева А.А., Вялкин Д.С., Троц А.А., Тараканова Е.Е., Давлетова Ю.И., Абдуллина Э.Л., Степанадзе В.Б., Ахметова А.И., Шагиева Н.Э., Ускова В.Д., Коновалова В.С., Магданова А.Р.Tokmacheva A.A., Vyalkin D.S., Trots A.A., Tarakanova E.E., Davletova Y.I., Abdullina E.L., Stepnadze V.B., Akhmetova A.I., Shagieva N.E., Uskova V.D., Konovalova V.S., Magdanova A.R.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.russianradiology.ru/jour/article/view/822

Узлы щитовидной железы (УЩЖ) широко распространены во всем мире: по данным патологоанатомических исследований, их могут иметь от 50% до 60% взрослых. В настоящее время в клинике для диагностики УЩЖ обычно используются ультразвуковое исследование, компьютерная томография, магнитно-резонансная томография и радионуклидная диагностика (например, позитронно-эмиссионная томография, совмещенная с компьютерной томографией). Эти методы в основном применяются для диагностики доброкачественности и злокачественности узлов, степени инвазии в прилегающие ткани и метастазирования в лимфатические узлы. Благодаря развитию искусственного интеллекта, машинного обучения и усовершенствованию оборудования для медицинской визуализации в последние годы популярным направлением исследований стала радиомика. Она позволяет получать из медицинских изображений различные количественные характеристики, выделяя невидимые черты и значительно расширяя возможности идентификации и прогнозирования. Радиомика обладает высоким потенциалом в выявлении и прогнозировании УЩЖ. В статье представлена информация о разработке и рабочем процессе радиомики. Обобщены данные о применении различных методов визуализации для выявления доброкачественных и злокачественных УЩЖ, оценки инвазивности и метастазирования в лимфатические узлы, а также о некоторых новых достижениях в области молекулярного уровня и глубокого обучения. Также приведены недостатки методики радиомики и перспективы ее дальнейшего развития.

The thyroid nodules (TNs) are widespread throughout the world: according to the pathological studies, they can be found in 50–60% of adults. Currently, ultrasound, computed tomography, magnetic resonance imaging and radionuclide diagnostics, such as positron emission tomography with computed tomography, are usually used to diagnose TNs in clinic. These techniques are mainly used to diagnose the nodile benignity and malignancy, the degree of invasion into adjacent tissues and metastases to lymph nodes. Thanks to the development of artificial intelligence, machine learning and the improvement of medical imaging equipment, radiomics has become a popular area of research in recent years. It allowes to obtain various quantitative characteristics from medical images, highlighting invisible features and significantly expanding the possibilities of identifying and predicting. Radiomics has a high potential in detecting and predicting TNs. We present the information on the development and workflow of radiomics. The article summarizes the application of various imaging techniques to identify benign and malignant TNs, determine invasiveness and metastases to lymph nodes, as well as some new advances in the field of molecular level and deep learning. The disadvantages of radiomics method are also given as well as prospects for its further development.

узлы щитовидной железырак щитовидной железырадиомикакомпьютерная томографиямагнитно-резонансная томографияультразвуковое исследованиеискусственный интеллектобзорthyroid nodulesthyroid cancerradiomicscomputed tomographymagnetic resonance imagingultrasoundartificial intelligencereviewReferencesМиронов С.П., Сергиенко В.Б. Сцинтиграфия при узловой патологии щитовидной железы. Вестник рентгенологии и радиологии. 2022; 103(4–6): 108–16. https://doi.org/10.20862/0042-4676-2022-103-4-6-108-116.Mironov SP, Sergienko VB. Scintigraphy in thyroid nodular pathology. Journal of Radiology and Nuclear Medicine. 2022; 103(4–6): 108–16 (in Russ). https://doi.org/10.20862/0042-4676-2022-103-4-6-108-116.Станякина Е.Е., Романов И.С., Гогиева Э.Х. и др. Эффективность метода определения уровня тиреоглобулина в смыве из пункционной иглы в дифференциальной диагностике метастазов высокодифференцированного рака щитовидной железы в лимфатических узлах шеи. Опухоли головы и шеи. 2022; 12(3): 10–6. https://doi.org/10.17650/2222-1468-2022-12-3-10-16.Stanyakina EE, Romanov IS, Gogieva EK, et al. The effectiveness of the method for determining the level of thyroglobulin in needle washouts of fine-needle aspiration biopsy in the differential diagnosis of metastases of highly differentiated thyroid cancer in the lymph nodes of the neck. Head and Neck Tumors. 2022; 12(3): 10–6 (in Russ). https://doi.org/10.17650/2222-1468-2022-12-3-10-16.Гордиенко В.П., Побережский А.В. Эпидемиологическая характеристика рака щитовидной железы у жителей восточных регионов Российской Федерации. Тихоокеанский медицинский журнал. 2022; 4: 63–71. https://doi.org/10.34215/1609-1175-2022-4-63-71.Gordienko VP, Poberezhestsky AV. Thyroid cancer in the Far Eastern Federal District of Russia. Pacific Medical Journal. 2022; 4: 63–71 (in Russ). https://doi.org/10.34215/1609-1175-2022-4-63-71.Gul M, Bonjoc KC, Gorlin D, et al. Diagnostic utility of radiomics in thyroid and head and neck cancers. Front Oncol. 2021; 11: 639326. https://doi.org/10.3389/fonc.2021.639326.Gul M, Bonjoc KC, Gorlin D, et al. Diagnostic utility of radiomics in thyroid and head and neck cancers. Front Oncol. 2021; 11: 639326. https://doi.org/10.3389/fonc.2021.639326.Sun L, Li M. TSH suppression therapy may affect bone health. How to manage differentiated thyroid cancer scientifically? Chinese General Practice. 2021; 24(8): 941–6. https://doi.org/10.12114/j.issn.1007-9572.2021.00.074.Sun L, Li M. TSH suppression therapy may affect bone health. How to manage differentiated thyroid cancer scientifically? Chinese General Practice. 2021; 24(8): 941–6. https://doi.org/10.12114/j.issn.1007-9572.2021.00.074.Grani G, Sponziello M, Pecce V, et al. Contemporary thyroid nodule evaluation and management. J Clin Endocrinol Metab. 2020; 105(9): 2869–83. https://doi.org/10.1210/clinem/dgaa322.Grani G, Sponziello M, Pecce V, et al. Contemporary thyroid nodule evaluation and management. J Clin Endocrinol Metab. 2020; 105(9): 2869–83. https://doi.org/10.1210/clinem/dgaa322.Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017; 14(5): 587–95. https://doi.org/10.1016/j.jacr.2017.01.046.Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017; 14(5): 587–95. https://doi.org/10.1016/j.jacr.2017.01.046.Fresilli D, David E, Pacini P, et al. Thyroid nodule characterization: how to assess the malignancy risk. Update of the literature. Diagnostics (Basel). 2021; 11(8): 1374. https://doi.org/10.3390/diagnostics11081374.Fresilli D, David E, Pacini P, et al. Thyroid nodule characterization: how to assess the malignancy risk. Update of the literature. Diagnostics (Basel). 2021; 11(8): 1374. https://doi.org/10.3390/diagnostics11081374.Liang J, Huang X, Hu H, et al. Predicting malignancy in thyroid nodules: radiomics score versus 2017 American College of Radiology thyroid imaging, reporting and data system. Thyroid. 2018; 28(8): 1024–33. https://doi.org/10.1089/thy.2017.0525.Liang J, Huang X, Hu H, et al. Predicting malignancy in thyroid nodules: radiomics score versus 2017 American College of Radiology thyroid imaging, reporting and data system. Thyroid. 2018; 28(8): 1024–33. https://doi.org/10.1089/thy.2017.0525.Yoon J, Lee E, Kang SW, et al. Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology. Eur Radiol. 2021; 31(7): 5059–67. https://doi.org/10.1007/s00330-020-07670-3.Yoon J, Lee E, Kang SW, et al. Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology. Eur Radiol. 2021; 31(7): 5059–67. https://doi.org/10.1007/s00330-020-07670-3.Shi Y, Jiang Y, Wang J, et al. Prediction of benign and malignant nodules of thyroid TI-RADS 4~5 based on ultrasonographic nomogram. Chin J Ultrasound Med. 2021; 37(12): 1328–32.Shi Y, Jiang Y, Wang J, et al. Prediction of benign and malignant nodules of thyroid TI-RADS 4~5 based on ultrasonographic nomogram. Chin J Ultrasound Med. 2021; 37(12): 1328–32.Wang Q, Shi Q, Xian X, et al. Application of machine learning in differential diagnosis of nodular hashimoto thyroiditis and thyroid micropapillary carcinoma based on small sample thyroid high b-value diffusion-weighted imaging omics. Chin J Med Imaging. 2021; 29(11): 1064–9.Wang Q, Shi Q, Xian X, et al. Application of machine learning in differential diagnosis of nodular hashimoto thyroiditis and thyroid micropapillary carcinoma based on small sample thyroid high b-value diffusion-weighted imaging omics. Chin J Med Imaging. 2021; 29(11): 1064–9.Xia L, Liang Z, Yao G, et al. Value of support vector machine model based on magnetic resonance texture analysis for differential diagnosis of thyroid nodule larger than 1 cm. Shandong Med J. 2021; 61(31): 77–80.Xia L, Liang Z, Yao G, et al. Value of support vector machine model based on magnetic resonance texture analysis for differential diagnosis of thyroid nodule larger than 1 cm. Shandong Med J. 2021; 61(31): 77–80.Wu Y, Jin J, Feng Y. Analysis of the texture features of enhanced CT in the identification of benign and malignant thyroid nodules. J Southeast Univ (Medical Edition). 2016; 35(1): 112–6.Wu Y, Jin J, Feng Y. Analysis of the texture features of enhanced CT in the identification of benign and malignant thyroid nodules. J Southeast Univ (Medical Edition). 2016; 35(1): 112–6.Guo W, Luo H, Zhao Y, et al. CT texture analysis technology to identify benign and malignant thyroid nodules feasibility study. Int J Med Radiol. 2017; 40(1): 3–5.Guo W, Luo H, Zhao Y, et al. CT texture analysis technology to identify benign and malignant thyroid nodules feasibility study. Int J Med Radiol. 2017; 40(1): 3–5.Hu Y, Huang Z, Xie Y, Wang X. Value of first-order ct texture analysis in differentiating benign and malignant thyroid nodules. J Clin Radiol. 2019; 38(3): 422–5.Hu Y, Huang Z, Xie Y, Wang X. Value of first-order ct texture analysis in differentiating benign and malignant thyroid nodules. J Clin Radiol. 2019; 38(3): 422–5.Zhang DAW. Differential diagnosis of benign and malignant thyroid micronodules based on CT image omics. Anhui Medical University. 2020; 56(4): 652655.Zhang DAW. Differential diagnosis of benign and malignant thyroid micronodules based on CT image omics. Anhui Medical University. 2020; 56(4): 652655.Du D, Li X, Liu B, et al. CT image omics of thyroid adenoma and the diagnostic value of papillary carcinoma. J Anhui Med Univ. 2019; 54(6): 950–3.Du D, Li X, Liu B, et al. CT image omics of thyroid adenoma and the diagnostic value of papillary carcinoma. J Anhui Med Univ. 2019; 54(6): 950–3.Zhou S, Liu T, Zhou J. Preliminary study on application of radiomics in thyroid carcinoma. Oncoradiology. 2017; 26(2): 102–5.Zhou S, Liu T, Zhou J. Preliminary study on application of radiomics in thyroid carcinoma. Oncoradiology. 2017; 26(2): 102–5.Wang X, Agyekum EA, Ren Y, et al. A radiomic nomogram for the ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma. Front Oncol. 2021; 11: 625646. https://doi.org/10.3389/fonc.2021.625646.Wang X, Agyekum EA, Ren Y, et al. A radiomic nomogram for the ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma. Front Oncol. 2021; 11: 625646. https://doi.org/10.3389/fonc.2021.625646.Tong Y, Li J, Huang Y, et al. Ultrasound-based radiomic nomogram for predicting lateral cervical lymph node metastasis in papillary thyroid carcinoma. Acad Radiol. 2021; 28(12): 1675–84. https://doi.org/10.1016/j.acra.2020.07.017.Tong Y, Li J, Huang Y, et al. Ultrasound-based radiomic nomogram for predicting lateral cervical lymph node metastasis in papillary thyroid carcinoma. Acad Radiol. 2021; 28(12): 1675–84. https://doi.org/10.1016/j.acra.2020.07.017.Zhang H, Zhang H, Shu Z, et al. The value of MRI T2WI image texture analysis in predicting cervical lymph node metastasis of thyroid papillary carcinoma. Chin J Clin Imaging. 2020; 31(8): 568–71.Zhang H, Zhang H, Shu Z, et al. The value of MRI T2WI image texture analysis in predicting cervical lymph node metastasis of thyroid papillary carcinoma. Chin J Clin Imaging. 2020; 31(8): 568–71.Yao X, Zhou Q, Zhang Z, Zhou J. Value of MRI texture combined with cripto-1 and SOX2 proteins in the diagnosis of cervical lymph node metastasis of thyroid papillary carcinoma. Chin J Med Phys. 2022; 39(2): 224–8.Yao X, Zhou Q, Zhang Z, Zhou J. Value of MRI texture combined with cripto-1 and SOX2 proteins in the diagnosis of cervical lymph node metastasis of thyroid papillary carcinoma. Chin J Med Phys. 2022; 39(2): 224–8.Qin H, Que Q, Lin P, et al. Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery. Radiol Med. 2021; 126(10): 1312–7. https://doi.org/10.1007/s11547-021-01393-1.Qin H, Que Q, Lin P, et al. Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery. Radiol Med. 2021; 126(10): 1312–7. https://doi.org/10.1007/s11547-021-01393-1.Shen S, Han D, Zhao L, et al. Prediction of lymph node metastasis in the central group based on small ripple analysis of CT venous phase image of thyroid papillary carcinoma nodules. Chin J Radiol. 2019; 11: 946–51.Shen S, Han D, Zhao L, et al. Prediction of lymph node metastasis in the central group based on small ripple analysis of CT venous phase image of thyroid papillary carcinoma nodules. Chin J Radiol. 2019; 11: 946–51.Su GY, Xu XQ, Zhou Y, et al. Texture analysis of dual-phase contrast-enhanced CT in the diagnosis of cervical lymph node metastasis in patients with papillary thyroid cancer. Acta Radiol. 2021; 62(7): 890–6. https://doi.org/10.1177/0284185120946711.Su GY, Xu XQ, Zhou Y, et al. Texture analysis of dual-phase contrast-enhanced CT in the diagnosis of cervical lymph node metastasis in patients with papillary thyroid cancer. Acta Radiol. 2021; 62(7): 890–6. https://doi.org/10.1177/0284185120946711.He J, Lu Q, Xu X, Hu S. Study on the value of CT imaging features in predicting cervical lymph node metastasis of papillary thyroid. J Shanghai Jiao Tong Univ (Medical Science). 2021; 41(9): 1233–9.He J, Lu Q, Xu X, Hu S. Study on the value of CT imaging features in predicting cervical lymph node metastasis of papillary thyroid. J Shanghai Jiao Tong Univ (Medical Science). 2021; 41(9): 1233–9.Zhao H, Ye J, Duan S, et al. CT image omics of thyroid papillary carcinoma and adenoma identify. Chin J Med Imaging. 2021; 27(2): 102–6.Zhao H, Ye J, Duan S, et al. CT image omics of thyroid papillary carcinoma and adenoma identify. Chin J Med Imaging. 2021; 27(2): 102–6.Zhou Y, Su GY, Hu H, Ge YQ. Diagnosis of cervical lymph node metastasis of papillary thyroid carcinoma based on dual energy CT iodogram. J Int Med Radiol. 2021; 44(1): 119.Zhou Y, Su GY, Hu H, Ge YQ. Diagnosis of cervical lymph node metastasis of papillary thyroid carcinoma based on dual energy CT iodogram. J Int Med Radiol. 2021; 44(1): 119.Zhou H, Jin Y, Dai L, et al. Differential diagnosis of benign and malignant thyroid nodules using deep learning radiomics of thyroid ultrasound images. Eur J Radiol. 2020; 127: 108992. https://doi.org/10.1016/j.ejrad.2020.10899.Zhou H, Jin Y, Dai L, et al. Differential diagnosis of benign and malignant thyroid nodules using deep learning radiomics of thyroid ultrasound images. Eur J Radiol. 2020; 127: 108992. https://doi.org/10.1016/j.ejrad.2020.10899.

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