cardiotomskСибирский журнал клинической и экспериментальной медициныSiberian Journal of Clinical and Experimental Medicine2713-29272713-265XTSU publishing10.29001/2073-8552-2022-37-4-114-123cardiotomsk-1625Research ArticleКЛИНИЧЕСКИЕ ИССЛЕДОВАНИЯCLINICAL STUDIESАвтоматический анализ поражения легких при COVID-19: сравнение стандартной и низкодозной компьютерной томографииAutomated analysis of lung lesions in COVID-19: comparison of standard and low-dose CThttps://orcid.org/0000-0002-2681-9378БлохинИ. А.BlokhinI. A.

Блохин Иван Андреевич, младший научный сотрудник, сектор исследований в лучевой диагностике

127051, Москва, ул. Петровка, 24, стр. 1

Ivan A. Blokhin, Junior Research Scientist, Diagnostic Radiology Research Sector

24, p. 1, Petrovka str., Moscow, 127051

i.blokhin@npcmr.ruhttps://orcid.org/0000-0003-4485-2638СоловьевА. В.SolovevA. V.

Соловьёв Александр Владимирович, младший научный сотрудник, отдел научных медицинских исследований

127051, Москва, ул. Петровка, 24, стр. 1

Alexander V. Solovev, Junior Research Scientist

24, p. 1, Petrovka str., Moscow, 127051

a.solovev@npcmr.ruhttps://orcid.org/0000-0002-2990-7736ВладзимирскийA. B.VladzymyrskyyA. V.

Владзимирский Антон Вячеславович, доктор медицинских наук, заместитель директора по научной работе

127051, Москва, ул. Петровка, 24, стр. 1

Anton V. Vladzymyrskyy, Dr. Sci. (Med.), Deputy Director for Research

24, p. 1, Petrovka str., Moscow, 127051

a.vladzimirsky@npcmr.ruhttps://orcid.org/0000-0002-0166-3768КоденкоМ. Р.KodenkoM. R.

Коденко Мария Романовна, младший научный сотрудник, отдел научных медицинских исследований

127051, Москва, ул. Петровка, 24, стр. 1

Maria R. Kodenko, Junior Research Scientist, Medical Research Department, Clinical Research and Practical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Department; Bauman Moscow State Technical University

24, p. 1, Petrovka str., Moscow, 127051; 5, 2nd Baumanskaya str., bldg. 1, Moscow,105005

m.kodenko@npcmr.ruhttps://orcid.org/0000-0002-8521-4045ШумскаяЮ. Ф.ShumskayaYu. F.

Шумская Юлия Федоровна, младший научный сотрудник, отделнаучных медицинских исследований, Научно-практический клинический центр диагностики и телемедицинских технологий; лаборант, кафедра госпитальной терапии № 1; Институтклинической медицины имени Н.В. Склифосовского, СеченовскийУниверситет

127051, Москва, ул. Петровка, 24, стр. 1; 119991, Москва, ул. Трубецкая, 8, стр. 2

 

Yuliya F. Shumskaya, Junior Research Scientist, Medical Research Department, Clinical Research and Practical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Department; Laboratory Technician, Department of Hospital Therapy No. 1, I.M. Sechenov First Moscow State Medical University (Sechenov University)

24, p. 1, Petrovka str., Moscow, 127051; 8, Trubetskaya str., p. 2, Moscow, 119991

ShumskayaYF@zdrav.mos.ruhttps://orcid.org/0000-0001-5161-6540ГончарА. П.GoncharA. P.

Гончар Анна Павловна, младший научный сотрудник, сектор исследований в лучевой диагностике

127051, Москва, ул. Петровка, 24, стр. 1

Anna P. Gonchar, Junior Research Scientist, Medical Research Department

24, p. 1, Petrovka str., Moscow, 127051

a.gonchar@npcmr.ruhttps://orcid.org/0000-0003-1816-1315ГомболевскийВ. А.GombolevskiyV. A.

Гомболевский Виктор Александрович, кандидат медицинских наук, директор ключевых исследовательских программ

105064, Москва, p. 19, Нижний Сусальный пер., 5

Victor A. Gombolevskiy, M.D., Ph.D., MPH, Head of Key Research Programs

5, 2nd Baumanskaya str., bldg. 1, Moscow,105005

g_victor@mail.ruНаучно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения города МосквыРоссияClinical Research and Practical Center for Diagnostics and Telemedicine Technologies of the Moscow Health DepartmentRussian FederationНаучно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения города Москвы; Московский государственный технический университет имени Н.Э. Баумана (национальный исследовательский университет)РоссияClinical Research and Practical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Department; Bauman Moscow State Technical UniversityRussian FederationНаучно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения города Москвы; Первый Московский государственный медицинский университет имени И.М. Сеченова Министерства здравоохранения Российской Федерации (Сеченовский Университет)РоссияClinical Research and Practical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Department; I.M. Sechenov First Moscow State Medical University (Sechenov University)Russian FederationИнститут искусственного интеллектаРоссияArtificial Intelligence Research InstituteRussian Federation202217012023374114123Copyright © Блохин И.А., Соловьев А.В., Владзимирский A.B., Коденко М.Р., Шумская Ю.Ф., Гончар А.П., Гомболевский В.А., 20232023Блохин И.А., Соловьев А.В., Владзимирский A.B., Коденко М.Р., Шумская Ю.Ф., Гончар А.П., Гомболевский В.А.Blokhin I.A., Solovev A.V., Vladzymyrskyy A.V., Kodenko M.R., Shumskaya Y.F., Gonchar A.P., Gombolevskiy V.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.sibjcem.ru/jour/article/view/1625Введение

Введение. В определении степени поражения легочной паренхимы при COVID-19 особую роль играет метод компьютерной томографии (КТ) органов грудной клетки (ОГК). При этом субъективность оценки объема поражения легких по шкале КТ 0–4 при COVID-19 и постепенное внедрение низкодозной КТ (НДКТ) требуют изучения точности полуавтоматической сегментации легких при НДКТ по сравнению с КТ.

Цель исследования

Цель исследования: сравнить точность расчета объема пораженной легочной ткани между КТ и НДКТ при COVID-19 с помощью полуавтоматической программы сегментации.

Материал и методы

Материал и методы. Ретроспективное исследование выполнено на данных ранее проведенного проспективного многоцентрового исследования, зарегистрированного на ClinicalTrials.gov, NCT04379531. Данные КТ и НДКТ обработаны в программе 3D Slicer с расширениями Lung CT Segmenter и Lung CT Analyzer, пороговым методом определены объем легких и объем пораженной легочной ткани.

Результаты

Результаты. Выборка 84 пациента с признаками COVID-19-ассоциированной пневмонии. Средний возраст составил 50,6 ± 13,3 лет, медиана индекса массы тела – 28,15 кг/м2 [24,85; 31,31]. Для стандартного протокола КТ эффективная доза составила 10,1 ± 3,26 мЗв, для разработанного протокола НДКТ – 2,64 мЗв [1,99; 3,67]. При анализе абсолютных значений объема поражения легочной ткани в кубических сантиметрах между КТ и НДКТ с помощью критерия Вилкоксона выявлены статистически значимые различия (p-value < 0,001). При анализе процента поражения легочной ткани (объем пораженной ткани/объем легких) между КТ и НДКТ критерий Вилкоксона статистически значимых различий не выявил (p-value = 0,95).

Заключение

Заключение. Надежность разработанного протокола НДКТ для пациентов с COVID-19 при полуавтоматическом расчете процента пораженной ткани в 3D Slicer с расширениями Lung CT Segmenter и Lung CT Analyzer сравнима со стандартным протоколом КТ ОГК.

Introduction

Introduction. Chest computed tomography (CT) plays a prominent role in determining the extent of pulmonary parenchymal lesions in COVID-19. At the same time, subjectivity of lung lesion volume assessment using 0-4 CT scale in COVID-19 and gradual introduction of low-dose CT (LDCT) requires an investigation of semi-automated lung segmentation accuracy in LDCT compared to CT.

Study Objective

Study Objective. To compare the accuracy of affected lung tissue volume calculation between CT and LDCT in COVID-19 using a semi-automatic segmentation program.

Material and Methods

Material and Methods. The retrospective study was performed on data from the earlier prospective multicenter study registered at ClinicalTrials.gov, NCT04379531. CT and LDCT data were processed in 3D Slicer software with Lung CT Segmenter and Lung CT Analyzer extensions, and the volume of affected lung tissue and lung volume were determined by thresholding.

Results

Results. The sample size was 84 patients with signs of COVID-19-associated pneumonia. Mean age was 50.6 ± 13.3 years, and the median body mass index (BMI) was 28.15 [24.85; 31.31] kg/m2. The effective doses were 10.1 ± 3.26 mSv for the standard CT protocol and 2.64 mSv [1.99; 3.67] for the developed LDCT protocol. The analysis of absolute lung lesion volume in cubic centimeters with Wilcoxon Signed Ranks Test revealed a statistically significant difference between CT and LDCT (p-value < 0.001). No statistically significant differences were found in the relative values of lung tissue lesion volume (lesion volume/lung volume) between CT and LDCT using Wilcoxon Signed Ranks Test (p-value = 0.95).

Conclusion

Conclusion. The reliability of developed LDCT protocol in COVID-19 for the semi-automated calculation of affected tissue percentage was comparable to the standard chest CT protocol when using 3D Slicer with Lung CT Segmenter and Lung CT Analyzer extensions.

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