rentradВестник рентгенологии и радиологииJournal of radiology and nuclear medicine0042-46762619-0478Limited Liability Company "LUCHEVAYA DIAGNOSTIKA", Russian Association of Radiologists10.20862/0042-4676-2020-101-3-136-146rentrad-562Research ArticleОРИГИНАЛЬНЫЕ СТАТЬИORIGINAL RESEARCHЦветовое картирование серой шкалы с математическим анализом ультразвукового изображения в дифференциальной диагностике кистозных и солидных образований молочной железыGrayscale Color Mapping with the Mathematical Analysis of an Ultrasound Image in the Differential Diagnosis of Cystic and Solid Breast Masseshttps://orcid.org/0000-0003-1888-2307ПасынковД. В.PasynkovD. V.

Пасынков Дмитрий Валерьевич, к. м. н., заведующий отделением лучевой диагностики

ул. Осипенко, 22, Йошкар-Ола, 424037пл. Ленина, 1, Йошкар-Ола, 424000

Dmitriy V. Pasynkov, Сand. Med. Sc., Head of Radiology Department

ul. Osipenko, 22, Yoshkar-Ola, 424037ploshchad’ Lenina, 1, Yoshkar-Ola, 424000 

passynkov@mail.ruhttps://orcid.org/0000-0003-0717-0734ЕгошинИ. А.EgoshinI. A.

Егошин Иван Александрович, аспирант

пл. Ленина, 1, Йошкар-Ола, 424000

Ivan A. Egoshin, Postgraduate

ploshchad’ Lenina, 1, Yoshkar-Ola, 424000

https://orcid.org/0000-0002-1692-2558КолчевА. А.KolchevA. A.

Колчев Алексей Анатольевич, к. физ.-мат. н., доцент кафедры радиоастрономии

пл. Ленина, 1, Йошкар-Ола, 424000ул. Кремлевская, 18, Казань, 420008

Aleksey A. Kolchev, Cand. Phys.-Math. Sc., Associate Professor of Radioastronomy Chair

ploshchad’ Lenina, 1, Yoshkar-Ola, 424000ul. Kremlevskaya, 18, Kazan, 420008 

https://orcid.org/0000-0001-5052-2921КлюшкинИ. В.KlyushkinI. V.

Клюшкин Иван Владимирович, д. м. н., профессор кафедры общей хирургии

ул. Бутлерова, 49, Казань, 420012

Ivan V. Klyushkin, Dr. Med. Sc., Professor of General Surgery Chair

ul. Butlerova, 49, Kazan, 420012

https://orcid.org/0000-0001-9117-8151ПасынковаО. О.PasynkovaO. O.

Пасынкова Ольга Олеговна, к. м. н., доцент кафедры фундаментальной медицины

пл. Ленина, 1, Йошкар-Ола, 424000

Ol’ga O. Pasynkova, Cand. Med. Sc., Associate Professor of Fundamental Medicine Chair

ploshchad’ Lenina, 1, Yoshkar-Ola, 424000

ГБУ «Республиканский онкологический диспансер Республики Марий Эл» Минздрава Республики Марий Эл; ФГБОУ ВО «Марийский государственный университет»РоссияOncology Dispensary of Mari El Republic, Ministry of Health of Mari El Republic; Mari State UniversityRussian FederationФГБОУ ВО «Марийский государственный университет»РоссияMari State UniversityRussian FederationФГБОУ ВО «Марийский государственный университет»; ФГАОУ ВПО «Казанский (Приволжский) федеральный университет»РоссияMari State University; Kazan (Volga region) Federal UniversityRussian FederationФГБОУ ВО «Казанский государственный медицинский университет» Минздрава РоссииРоссияKazan State Medical University, Ministry of Health of the Russian FederationRussian Federation2020300620201013136146Copyright © Пасынков Д.В., Егошин И.А., Колчев А.А., Клюшкин И.В., Пасынкова О.О., 20202020Пасынков Д.В., Егошин И.А., Колчев А.А., Клюшкин И.В., Пасынкова О.О.Pasynkov D.V., Egoshin I.A., Kolchev A.A., Klyushkin I.V., Pasynkova O.O.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/562

Цель исследования – разработка системы автоматизированного анализа ультразвуковых изображений в режиме «серой шкалы», которая на основе принципов математической обработки позволила бы повысить специфичность диагностики атипичных кист молочной железы.

Материал и методы

Материал и методы. Авторами разработан программный пакет CystChecker 1.0. Для тестирования данной системы был использован набор из 217 ультразвуковых изображений: 107 кистозных (включая 53 атипичных, которые с трудом подвергались дифференциальной диагностике стандартными методами) и 110 солидных (как доброкачественных, так и злокачественных) образований. Все образования были верифицированы цито- и/или гистологически. Визуальная оценка проводилась путем анализа изображений в серой шкале, а также в режимах цветовой/энергетической допплерографии и эластографии.

Результаты

Результаты. Использование разработанной системы позволило правильно идентифицировать все 107 (100%) типичных кист, 107 (97,3%) из 110 солидных образований и 50 (94,3%) из 53 атипичных кист. Напротив, стандартная визуальная оценка дала возможность правильно идентифицировать все 107 (100%) типичных кист, 96 (87,3%) из 110 солидных образований и 32 (60,4%; p < 0,05) из 53 атипичных кист. Соответствующие значения общей специфичности автоматизированной методики и визуальной оценки составили 98 и 87%.

Заключение

Заключение. Использование разработанной авторами системы автоматизированного анализа обеспечивает более высокую специфичность, нежели визуальная оценка ультразвукового изображения, выполняемая квалифицированным специалистом.

Objective. Atypical breast cysts are often quite a serious problem in noninvasive ultrasound differential diagnosis. To develop a system for automated analysis of grayscale ultrasound images, which on the principles of mathematical processing would make it possible to increase the specificity of diagnosis in this situation.Material and methods. The authors developed the CystChecker 1.0 software package. To test this system, they used a set of 217 ultrasound images: 107 cystic (including 53 atypical lesions that were hardly differentially diagnosed by standard methods) and 110 solid (both benign and malignant) breast masses. All the masses were verified by cytology and/or histology. Visual assessment was carried out analyzing grayscale ultrasound, color/power Doppler, and elastography images.Results. Using the system developed by the authors could correctly identify all (n = 107 (100%)) typical cysts, 107 (97.3%) of 110 solid masses, and 50 (94.3%) of 53 atypical cysts. On the contrary, the standard visual assessment provided a possibility of correctly identifying all (n = 107 (100%)) typical cysts, 96 (87.3%) of 110 solid masses, and 32 (60.4%) of 53 atypical cysts (p < 0.05). The corresponding values of the overall specificity of automated and visual assessments were 98 and 87%, respectively.Conclusion. Using the system developed by the authors for automated analysis provides a higher specificity than the visual assessment of an ultrasound image, which is carried out by a qualified specialist.

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