A.S. Kultaev ¹, I.A. Zakiryarov ², D.A. Abdieva ², A.S. Imambetova ², A.K. Akhmetbayeva ²

1. «Kazakh Institute of Oncology and Radiology» JSC, Almaty, the Republic of Kazakhstan;
2. «Kazakh-Russian Medical University» Non-Profit Educational Institution, Almaty, the Republic of Kazakhstan

DOI: https://www.doi.org/10.52532/2663-4864-2023-1-67-19-25

UDC: 616.441-006:073.43

Year: 2023 issure: 67 number: 1 pages: 19-25

Download PDF:

ABSTRACT

Relevance: Thyroid cancer (TC) is the most common oncological pathology of the endocrine organs.
According to the International Agency for Research on Cancer (IARC), 567,233 new cases of thyroid cancer were registered worldwide in 2018. According to IARC, in 2018, 486 new cases were detected in Kazakhstan, which accounted for 1.4% of all cases in Asian countries.
TC ranks 10th in the overall structure of cancer incidence globally; TC accounts for 3.1% of all cases of primary malignant tumors. Despite the relatively low incidence, the problems of pathogenesis have been extremely relevant in recent decades due to the increasing prevalence of thyroid cancer.
Samsung Medison introduced AI-based S-Detect to improve sensitivity, specificity, and accuracy in the differential diagnosis of thyroid masses.
The study aimed to explore the S-Detect program capacities in differential diagnostics of thyroid masses.
Methods: 75 patients with focal lesions in the thyroid gland were examined using the Samsung Medison RS85 ultrasound machine equipped with the S-Detect program; additionally, Doppler and non-Doppler methods were used.
Results: The S-Detect program made it possible to make a correct diagnosis in 97% of patients (73 of 75), which was confirmed by the results of morphological verification (histology, cytology). The sonoelastography method showed correct results in 91% of patients (68 of 75).
Conclusion: The use of the S-Detect program for thyroid examination positively affects the diagnostic value of ultrasound in the differential diagnosis of thyroid masses, increasing the sensitivity, specificity, and accuracy of diagnosis, as well as avoiding redundant biopsies.
Keywords: ultrasound diagnostics, S-Detect, Ti-RADS, sonoelastography, thyroid formations.

List of sources used:
1. Тлегенов А.Ш. OMICS – исследования злокачественных и узловых новообразований щитовидной железы в Казахстане // дис. … док. философии (PhD): 6D110100. – Алматы, КазНМУ им. С. Асфендиярова, 2021 [Tlegenov A.Sh., OMICS – issledovaniya zlokachestvennyx i uzlovyx novoobrazovanij shhitovidnoj zhelezy v Kazaxstane // dis. … dok. filosofii (PhD): 6D110100. – Almaty, KazNMU im. S. Asfendiyarova, 2021 (in Russ.)]. https://kaznmu.kz/rus/wp-content/uploads/2021/02/dissertaciya-tlegenova-a.sh..pdf
2. Ерланкызы М., Сахамов Б.С., Туребаев Д.К., Кульмирзаева Д.М., Уразова С.Н., Аманшаева А.К.. Билялова З.А., Игиссинов Н.С. Оценка заболеваемости раком щитовидной железы в Казахстане // Медицина. – 2020. – № 11-12. – С. 221-222 [Erlankyzy M., Saxamov B.S., Turebaev D.K., Kul’mirzaeva D.M., Urazova S.N., Amanshaeva A.K.. Bilyalova Z.A., Igissinov N.S. Ocenka zabolevaemosti rakom shhitovidnoj zhelezy v Kazaxstane. // Medicina. – 2020. – № 11-12. – S. 221-222 (in Russ.)]. https://doi.org/10.31082/1728-452X-2020-221-222-11-12-2-7
3. Фисенко Е.П., Сыч Ю.П., Ветшева Н.Н. К вопросу о классификации TI-RADS и стратификации признаков рака щитовидной железы по данным ультразвукового исследования // Мед. визуал. – 2017. – №5. – С. 29-38 [Fisenko E.P., Sych Yu.P., Vetsheva N.N. K voprosu o klassifikacii TI-RADS i stratifikacii priznakov raka shhitovidnoj zhelezy po dannym ul’trazvukovogo issledovaniya // Med. vizual. – 2017. – №5. – S. 29-38 (in Russ.)]. https://doi.org/10.24835/1607-0763-2017-5-29-38
4. Новосад С. В., Рогова М. О., Мартиросян Н. С., Петунина Н. А. Современные прогностические модели стратификации риска рака щитовидной железы // Доктор.Ру. – 2016. – № 8 (125) – 9 (126). – С. 52-56 [Novosad S. V., Rogova M. O., Martirosyan N. S., Petunina N. A. Sovremennye prognosticheskie modeli stratifikacii riska raka shhitovidnoj zhelezy // Doktor.Ru. – 2016. – № 8 (125) – 9 (126). – S. 52-56 (in Russ.)]. https://journaldoctor.ru/catalog/endokrinologiya/sovremennye-prognosticheskie-modeli/
5. Прошакова М.А. Эластография – перспективная методика ультразвуковой диагностики // Бюлл. Мед. интернет-конф. – 2018. – Т. 8, № 11. – С. 534 [Proshakova M.A. E’lastografiya – perspektivnaya metodika ul’trazvukovoj diagnostiki // Byull. Med. internet-konf. – 2018. – T. 8, № 11. – S. 534 (in Russ.)]. https://cyberleninka.ru/article/n/elastografiya-perspektivnaya-metodika-ultrazvukoy-diagnostiki
6. Samsung Medison. S-Detect Thyroid – диагностика образований щитовидной железы [Samsung Medison. S-Detect Thyroid – diagnostika obrazovanij shhitovidnoj zhelezy. Nauchnyj razdel texnologiya (in Russ.)]. https://www.medison.ru/tn/sd-thyroid.htm. 21.02.2023.
7. Chong-Ke Z., Hui-Xiong X. Ультразвуковая эластография щитовидной железы: принципы и современное состояние. Часть вторая. – 09.04.2019. https://rh.org.ru/statti/ultrazvukovaya-elastografiya-shhitovidnoj-zhelezy-principy-i-sovremennoe-sostoyanie-chast-vtoraya/. 09.02.2023.
8. Young J. Y., Eun J.H., Yoon J.C., Hye L.K., Miran H., So Y.K. Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience. // Korean J. Radiol. – 2018. – Vol. 19(4). – P. 665-672. https://doi.org/10.3348/kjr.2018.19.4.665
9. Eun Y.J., Hye L.K., Eun J.H., Seon Y.P., Yoon J.C., Miran H. Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators // Eur. Radiol. – 2019. – Vol. 29 (4). – P. 1978-1985. https://pubmed.ncbi.nlm.nih.gov/30350161/
10. Southam M., Warren F. A Comparison as to the Advocacy and Inter-Observer Agreement of Using S-Detect™ against Sonographers Classifying Thyroid Lesions Using the British Thyroid Association (BTA) Guidelines // SonoAce Ultrasound. – 2021. – Vol. 33. – P. 95-100. https://www.medison.ru/si/art494.htm
11. Култаев А.С., Закиряров И.А. Функция S-Detect как новейший метод ультразвукового исследования образований молочных желез: Сравнительная характеристика // Онкология и Радиология Казахстана. – 2022. – №4 (66). – С. 24-32 [Kultaev A.S., Zakiryarov I.A. Funkciya S-Detect kak novejshij metod ul’trazvukovogo issledovaniya obrazovanij molochnyx zhelez: Sravnitel’naya xarakteristika // Onkologiya i Radiologiya Kazaxstana. – 2022. – №4 (66). – S. 24-32 (in Russ.)]. https://doi.org/10.52532/2521-6414-2022-4-66-24-32
12. Choi Y.J., Baek J.H., Park H.S., Shim W.H., Kim T.Y., Shong Y.K., Lee J.H. A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment // Thyroid. – 2017. – Vol. 27(4). – P. 546-552. https://doi.org/10.1089/thy.2016.0372
13. Zhong L., Wang C. Diagnostic accuracy of S-Detect in distinguishing benign and malignant thyroid nodules: A meta-analysis // PLoS One. – 2022. – Vol. 17(8). – Art. ID: e0272149. https://doi.org/10.1371/journal.pone.0272149