ketendoКлиническая и экспериментальная тиреоидологияClinical and experimental thyroidology1995-54722310-3787Endocrinology Research Centre10.14341/ket12730ketendo-12730Research ArticleОбзоры литературыReview of literatureРоль искусственного интеллекта в дифференциальной ультразвуковой диагностике узловых образований щитовидной железыThe role of artificial intelligence in the differential thyroid nodules ultrasound diagnosticshttps://orcid.org/0000-0001-5592-4727ТрухинА. А.TrukhinA. A.

Трухин Алексей Андреевич; eLibrary SPIN: 4398-9536

117036 Москва, ул. Дм. Ульянова, д. 11, к. 2

Alexey A. Trukhin; eLibrary SPIN: 4398-9536

11 bld 2, Dm. Ulyanova street, 117036 Moscow

Alexey.trukhin12@gmail.comhttps://orcid.org/0000-0001-6059-2827ЗахароваС. М.ZakharovaS. M.

Захарова Светлана Михайловна - кандидат медицинских наук; eLibrary SPIN-код: 9441-4035.

Москва

Svetlana M. Zakharova - MD, PhD; eLibrary SPIN-код: 9441-4035.

Moscow

smzakharova@mail.ruhttps://orcid.org/0000-0001-6059-2827ДунаевМ. Е.DunaevM. Y.

Дунаев Максим Евгеньевич

Москва

Maxim E. Dunaev

Moscow

max.dunaev@mail.ruhttps://orcid.org/0000-0002-9963-6783ИсаеваМ. П.IsaevaM. P.

Исаева Мария Петровна; eLibrary SPIN: 6205-5170.

Москва

Maria P. Isaeva, MD; eLibrary SPIN: 6205-5170.

Moscow

impdoctorx@gmail.comhttps://orcid.org/0000-0002-1129-7220ГармашА. А.GarmashA. A.

Гармаш Александр Александрович

Москва

Alexandr A. Garmash

Moscow

AAGarmash@mephi.ruhttps://orcid.org/0000-0002-8520-8702ТрошинаЕ. А.TroshinaE. A.

Трошина Екатерина Анатольевна - доктор медицинских наук, профессор, член-корр. РАН; eLibrary SPIN: 8821-8990.

Москва

Ekaterina A. Troshina - MD, PhD, Professor; eLibrary SPIN: 8821-8990.

Moscow

troshina@inbox.ruНациональный медицинский исследовательский центр эндокринологии; Национальный исследовательский ядерный университет МИФИРоссияEndocrinology Research Centre; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)Russian FederationНациональный медицинский исследовательский центр эндокринологииРоссияEndocrinology Research CentreRussian FederationНациональный исследовательский ядерный университет МИФИРоссияNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute)Russian Federation2022241220221823238Copyright © Трухин А.А., Захарова С.М., Дунаев М.Е., Исаева М.П., Гармаш А.А., Трошина Е.А., 20222022Трухин А.А., Захарова С.М., Дунаев М.Е., Исаева М.П., Гармаш А.А., Трошина Е.А.Trukhin A.A., Zakharova S.M., Dunaev M.Y., Isaeva M.P., Garmash A.A., Troshina E.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.cet-endojournals.ru/jour/article/view/12730

Достижения в разработке средств обработки медицинских изображений дают возможность выделять клинически значимые характеристики, ранее не доступные классическим методам медицинской визуализации. Огромным потенциалом для анализа медицинских изображений обладает ультразвуковая диагностика узловых образований щитовидной железы. В статье представлен обзор существующих систем классификаций стратификации риска злокачественности узловых образований щитовидной железы при ультразвуковом исследовании TIRADS (Thyroid Imaging Reporting and Data System).

Advances in the development and improvement of medical technologies and methods of processing medical images make it possible to highlight clinically significant characteristics that were not previously available to classical methods of medical imaging. Ultrasound diagnostics of thyroid gland nodules has a huge potential medical images processing. The article presents an overview of the existing ultrasound classification systems for thyroid nodules malignancy and the prospects for the development of intellectual tools TIRADS (Thyroid Imaging Reporting and Data System) classification system.

ультразвуковая диагностикаинтеллектуальные технологииискусственный интеллектузловые образования щитовидной железыTIRADSсегментацияклассификацияultrasound diagnosticsintelligent technologiesartificial intelligencethyroid nodulesTI-RADSsegmentationclassificationМатериал подготовлен по гранту Российского научного фонда в рамках реализации проекта №22-15-00135 «Научное обоснование, разработка и внедрение новых технологий диагностики коморбидных йододефицитных и аутоиммунных заболеваний щитовидной железы с использованием возможностей искусственного интеллекта», при подготовке рукописи авторы сохранили независимость мненийReferencesБельцевич Д.Г., Ванушко В.Э., Мельниченко Г.А., Румянцев П.О., Фадеев В.В. Клинические рекомендации Российской ассоциации эндокринологов по диагностике и лечению (много) узлового зоба у взрослых (2015 год) // Эндокринная хирургия. — 2016. — Т. 10. — №1. — С. 5-12. doi: https://doi.org/10.14341/serg201615-12Bel'tsevich DG, Vanushko VE, Mel'nichenko GA, Rumyantsev PO, Fadeyev VV. Russian Association of Endocrinologists Clinic Guidelines for Thyroid Nodules Diagnostic and Treatment. Endocrine Surgery. 2016;10(1):5-12. (In Russ.). doi: https://doi.org/10.14341/serg201615-12Guth S, Theune U, Aberle J, et al. Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination. Eur J Clin Invest. 2009;39(8):699-706. doi: https://doi.org/10.1111/j.1365-2362.2009.02162.xGuth S, Theune U, Aberle J, et al. Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination. Eur J Clin Invest. 2009;39(8):699-706. doi: https://doi.org/10.1111/j.1365-2362.2009.02162.xЗлокачественные новообразования в России в 2020 году (заболеваемость и смертность) / Под ред. Каприна А.Д., Старинского В.В., Петровой Г.В. — М.: МНИОИ им. П.А. Герцена; 2021. – 252 с.Zlokachestvennye novoobrazovaniya v Rossii v 2020 godu (zabolevaemost' i smertnost') / Pod red. Kaprina A.D., Starinskogo V.V., Petrovoi G.V. — Moscow: MNIOI im. P.A. Gertsena; 2021. – 252 p. (In Russ.).Haugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: What is new and what has changed? Cancer. 2017; 123(3):372-381. doi: https://doi.org/10.1002/cncr.30360Haugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: What is new and what has changed? Cancer. 2017; 123(3):372-381. doi: https://doi.org/10.1002/cncr.30360Russ G. Risk stratification of thyroid nodules on ultrasonography with the French TI-RADS: description and reflections. Ultrasonography. 2016;35(1):25-38. doi: https://doi.org/10.14366/usg.15027Russ G. Risk stratification of thyroid nodules on ultrasonography with the French TI-RADS: description and reflections. Ultrasonography. 2016;35(1):25-38. doi: https://doi.org/10.14366/usg.15027Horvath E, Majlis S, Rossi R, et al. An Ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J Clin Endocrinol Metab. 2009;94(5):1748-1751. doi: https://doi.org/10.1210/jc.2008-1724Horvath E, Majlis S, Rossi R, et al. An Ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J Clin Endocrinol Metab. 2009;94(5):1748-1751. doi: https://doi.org/10.1210/jc.2008-1724Russ G, Royer B, Bigorgne C, et al. Prospective evaluation of thyroid imaging reporting and data system on 4550 nodules with and without elastography. Eur J Endocrinol. 2013;168(5):649-655. doi: https://doi.org/10.1530/EJE-12-0936Russ G, Royer B, Bigorgne C, et al. Prospective evaluation of thyroid imaging reporting and data system on 4550 nodules with and without elastography. Eur J Endocrinol. 2013;168(5):649-655. doi: https://doi.org/10.1530/EJE-12-0936Na DG, Baek JH, Sung JY, et al. Thyroid imaging reporting and data system risk stratification of thyroid nodules: Categorization based on solidity and echogenicity. Thyroid. 2016;26(4):562-572. doi: https://doi.org/10.1089/thy.2015.0460Na DG, Baek JH, Sung JY, et al. Thyroid imaging reporting and data system risk stratification of thyroid nodules: Categorization based on solidity and echogenicity. Thyroid. 2016;26(4):562-572. doi: https://doi.org/10.1089/thy.2015.0460Haugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid. 2016;26(1):1-133. doi: https://doi.org/10.1089/thy.2015.0020Haugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid. 2016;26(1):1-133. doi: https://doi.org/10.1089/thy.2015.0020Gharib H, Papini E, Garber JR, et al. American Association of Clinical Endocrinologists, American College of Endocrinology, and Associazione Medici Endocrinologi Medical Guidelines for clinical practice for the diagnosis and management of thyroid nodules — 2016 update appendix. Endocr Pract. 2016;22(1):1-60. doi: https://doi.org/10.4158/EP161208.GLGharib H, Papini E, Garber JR, et al. American Association of Clinical Endocrinologists, American College of Endocrinology, and Associazione Medici Endocrinologi Medical Guidelines for clinical practice for the diagnosis and management of thyroid nodules — 2016 update appendix. Endocr Pract. 2016;22(1):1-60. doi: https://doi.org/10.4158/EP161208.GLTessler FN, Middleton WD, Grant EG. Thyroid Imaging Reporting and Data System (TI-RADS): A User’s Guide. Radiology. 2018;287(1):29-36. doi: https://doi.org/10.1148/radiol.2017171240Tessler FN, Middleton WD, Grant EG. Thyroid Imaging Reporting and Data System (TI-RADS): A User’s Guide. Radiology. 2018;287(1):29-36. doi: https://doi.org/10.1148/radiol.2017171240Persichetti A, Di Stasio E, Coccaro C, et al. Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study. Thyroid. 2020;30(2):237-242. doi: https://doi.org/10.1089/thy.2019.0360Persichetti A, Di Stasio E, Coccaro C, et al. Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study. Thyroid. 2020;30(2):237-242. doi: https://doi.org/10.1089/thy.2019.0360Choi, S.H.; Kim, E.K.; Kwak, J.Y.; Kim, M.J.; Son, E.J. Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules. Thyroid 2010, 20, 167–172.Choi, S.H.; Kim, E.K.; Kwak, J.Y.; Kim, M.J.; Son, E.J. Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules. Thyroid 2010, 20, 167–172.Eoin F. Cleere, Matthew G. Davey, Shane O’Neil, Mel Corbett, John P O’Donnell, Sean Hacking Ivan J. Keogh, Aoife J. Lowery, Michael J. Kerin. Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography—A Systematic Review and Meta-Analysis Diagnostics 2022 Mar 24;12(4):794Eoin F. Cleere, Matthew G. Davey, Shane O’Neil, Mel Corbett, John P O’Donnell, Sean Hacking Ivan J. Keogh, Aoife J. Lowery, Michael J. Kerin. Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography—A Systematic Review and Meta-Analysis Diagnostics 2022 Mar 24;12(4):794Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with Deep Neural Networks. Med Image Anal. 2017;35:18-31. doi: https://doi.org/10.1016/j.media.2016.05.004Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with Deep Neural Networks. Med Image Anal. 2017;35:18-31. doi: https://doi.org/10.1016/j.media.2016.05.004Cheng C-H, Liu W-X. Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method. J Clin Med. 2018;7(6):124. doi: https://doi.org/10.3390/jcm7060124Cheng C-H, Liu W-X. Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method. J Clin Med. 2018;7(6):124. doi: https://doi.org/10.3390/jcm7060124Hržić F, Štajduhar I, Tschauner S, et al. Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection. Entropy. 2019;21(4):338. doi: https://doi.org/10.3390/e21040338Hržić F, Štajduhar I, Tschauner S, et al. Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection. Entropy. 2019;21(4):338. doi: https://doi.org/10.3390/e21040338Remonti LR, Kramer CK, Leitão CB, et al. Thyroid Ultrasound Features and Risk of Carcinoma: A Systematic Review and Meta-Analysis of Observational Studies. Thyroid. 2015;25(5):538-550. doi: https://doi.org/10.1089/thy.2014.0353Remonti LR, Kramer CK, Leitão CB, et al. Thyroid Ultrasound Features and Risk of Carcinoma: A Systematic Review and Meta-Analysis of Observational Studies. Thyroid. 2015;25(5):538-550. doi: https://doi.org/10.1089/thy.2014.0353Virmani V, Hammond I. Sonographic Patterns of Benign Thyroid Nodules: Verification at Our Institution. Am J Roentgenol. 2011;196(4):891-895. doi: https://doi.org/10.2214/AJR.10.5363Virmani V, Hammond I. Sonographic Patterns of Benign Thyroid Nodules: Verification at Our Institution. Am J Roentgenol. 2011;196(4):891-895. doi: https://doi.org/10.2214/AJR.10.5363Brito JP, Gionfriddo MR, Al Nofal A, et al. The Accuracy of Thyroid Nodule Ultrasound to Predict Thyroid Cancer: Systematic Review and Meta-Analysis. J Clin Endocrinol Metab. 2014;99(4):1253-1263. doi: https://doi.org/10.1210/jc.2013-2928Brito JP, Gionfriddo MR, Al Nofal A, et al. The Accuracy of Thyroid Nodule Ultrasound to Predict Thyroid Cancer: Systematic Review and Meta-Analysis. J Clin Endocrinol Metab. 2014;99(4):1253-1263. doi: https://doi.org/10.1210/jc.2013-2928Nguyen DT, Kang JK, Pham TD, et al. Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence. Sensors. 2020;20(7):1822. doi: https://doi.org/10.3390/s20071822Nguyen DT, Kang JK, Pham TD, et al. Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence. Sensors. 2020;20(7):1822. doi: https://doi.org/10.3390/s20071822Varga B, Farkas J, Malis A, et al. RFC 9037. Deterministic Networking (DetNet) Data Plane: MPLS over IEEE 802.1 Time-Sensitive Networking (TSN) [Internet]. 2021. Available from: https://www.rfc-editor.org/info/rfc9037Varga B, Farkas J, Malis A, et al. RFC 9037. Deterministic Networking (DetNet) Data Plane: MPLS over IEEE 802.1 Time-Sensitive Networking (TSN) [Internet]. 2021. Available from: https://www.rfc-editor.org/info/rfc9037Роннебергер О., Фишер Ф., Брокс Т. U-Net: сверточные сети для сегментации биомедицинских изображений. 2015. Доступно по: https://github.com/qubvel/segmentation_models.pytorch#examplesRonneberger O, Fisher F, Broks T. U-Net: svertochnye seti dlja segmentacii biomedicinskih izobrazhenij. 2015 Available from: https://github.com/qubvel/segmentation_models.pytorch#examples. (In Russ.)Zhou Z, Siddiquee MR, Tajbakhsh N, Liang J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Arizona State University. 2018. Available from: https://arxiv.org/pdf/1807.10165.pdfZhou Z, Siddiquee MR, Tajbakhsh N, Liang J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Arizona State University. 2018. Available from: https://arxiv.org/pdf/1807.10165.pdfPeng S, Liu Y, Lv W, et al. Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study. Lancet Digit Heal. 2021;3(4):e250-e259. doi: https://doi.org/10.1016/S2589-7500(21)00041-8Peng S, Liu Y, Lv W, et al. Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study. Lancet Digit Heal. 2021;3(4):e250-e259. doi: https://doi.org/10.1016/S2589-7500(21)00041-8Fan T, Wang G, Li Y, Wang H. MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation. IEEE Access. 2020;8(4):179656-179665. doi: https://doi.org/10.1109/ACCESS.2020.3025372Fan T, Wang G, Li Y, Wang H. MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation. IEEE Access. 2020;8(4):179656-179665. doi: https://doi.org/10.1109/ACCESS.2020.3025372Chaurasia A, Culurciello E. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation, 2017. Available from: https://arxiv.org/abs/1707.03718Chaurasia A, Culurciello E. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation, 2017. Available from: https://arxiv.org/abs/1707.03718

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