Digital DiagnosticsDigital DiagnosticsDigital DiagnosticsDigital Diagnostics2712-84902712-8962Eco-Vector7030610.17816/DD70306Systematic reviewsСистематические обзоры系统评价Review ArticleRole of chest MRI for the diagnosis of malignant pulmonary nodules: a systematic review and a meta-analysisРоль магнитно-резонансной томографии в выявлении злокачественных лёгочных узлов: систематический обзор и метаанализ磁共振成像在恶性肺结节检测中的作用：系统回顾和荟萃分析https://orcid.org/0000-0002-0208-52184458-5608VasilevYuriy A.ВасильевЮрий АлександровичVasilevYuriy A.
Russian Federation

MD, Cand. Sci. (Med)

кандидат медицинских наук

MD, Cand. Sci. (Med)

dr.vasilev@me.comhttps://orcid.org/0000-0002-8684-775X572198373115504-8136PaninaOlga Y.ПанинаОльга ЮрьевнаPaninaOlga Y.
Russian Federation

Junior Scientist Researcher

мл. научный сотрудник

Junior Scientist Researcher

o.panina@npcmr.ruhttps://orcid.org/0000-0002-7908-39825558-7307GrikEvgeniia A.ГрикЕвгения АндреевнаGrikEvgeniia A.
Russian Federation

MD

MD

evgeniyagrik@gmail.comhttps://orcid.org/0000-0002-8235-93615891-4384AkhmadKate S.АхмадЕкатерина СергеевнаAkhmadKate S.
Russian Federation
e.ahmad@npcmr.ruhttps://orcid.org/0000-0003-4955-27499777-2067VasilevaYulia N.ВасильеваЮлия НиколаевнаVasilevaYulia N.
Russian Federation

MD, Cand. Sci. (Med.)

кандидат медицинских наук

MD, Cand. Sci. (Med.)

drugya@yandex.ruResearch and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health CareНаучно-практический клинический центр диагностики и телемедицинских технологийResearch and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health CareCity Clinical Oncological Hospital No. 1Городская клиническая онкологическая больница № 1City Clinical Oncological Hospital No. 1Moscow State University of Medicine and Dentistry named after A.I. EvdokimovМосковский государственный медико-стоматологический университет имени А.И. ЕвдокимоваMoscow State University of Medicine and Dentistry named after A.I. Evdokimov1609202115102021233013120705202104072021Copyright ©; 2021, Vasilev Y.A., Panina O.Y., Grik E.A., Akhmad K.S., Vasileva Y.N.Copyright ©; 2021, Васильев Ю.А., Панина О.Ю., Грик Е.А., Ахмад Е.С., Васильева Ю.Н.Copyright ©; 2021, Vasilev Y.A., Panina O.Y., Grik E.A., Akhmad K.S., Vasileva Y.N.2021Vasilev Y.A., Panina O.Y., Grik E.A., Akhmad K.S., Vasileva Y.N.Васильев Ю.А., Панина О.Ю., Грик Е.А., Ахмад Е.С., Васильева Ю.Н.Vasilev Y.A., Panina O.Y., Grik E.A., Akhmad K.S., Vasileva Y.N.https://creativecommons.org/licenses/by-nc-nd/4.0https://jdigitaldiagnostics.com/DD/article/view/70306

AIM: To evaluate the ability of magnetic resonance imaging (MRI) of the chest to detect malignant pulmonary nodules compared to compute tomography (CT).

MATERIALS AND METHODS: We searched the following databases with the final date of search on April 7th, 2021: PubMed, Google Scholar. We selected studies according to the inclusion and exclusion criteria that assessed the detection of malignant lung nodules by MRI and CT and included information about sensitivity and specificity. Method of the analysis and data grouping was chosen with regard to statistical heterogeneity of the studies included in the analysis. We used the χ2 test and I2 statistic to evaluate the heterogeneity.

RESULTS: We selected 168 articles for the systematic review from the PubMed and Google Scholar databases. We included 21 studies on 1,188 patients in the meta-analysis and revealed statistically significant heterogeneity (р<0,00001 for χ2 test; I2=99%) for sensitivity and specificity. Hence, we used a random-effect model for further analysis. As a result, values of sensitivity for detection of pulmonary nodules with MRI of 70.4%–100%, specificity ― from 60.6% to 100%.

CONCLUSIONS: Thus, MRI has sufficient sensitivity and specificity for detecting malignant pulmonary nodules primarily discovered with CT.

Цель ― оценка возможности метода магнитно-резонансной томографии (МРТ) органов грудной клетки для выявления лёгочных узлов, подозрительных в отношении злокачественности, в сравнении с компьютерной томографией (КТ).

Материалы и методы. Проведён поиск в базах данных PubMed и Google Scholar за период до 7 апреля 2021 г. включительно. В соответствии с критериями соответствия были отобраны исследования, в которых проводилась оценка способности МРТ и КТ к выявлению лёгочных узлов, подозрительных в отношении злокачественности. Выбор метода анализа и группировки данных о чувствительности и специфичности выполняли по результатам оценки гетерогенности исследований. Для оценки статистической гетерогенности исследований, включённых в метаанализ, применяли критерий согласия Пирсона χ2 и индекс гетерогенности I2.

Результаты. По результатам поиска было отобрано 168 работ, в метаанализ вошло 21 исследование. Отобранные работы включали 1188 пациентов. По результатам метаанализа выявлено наличие статистически значимой гетерогенности p <0,00001 по критерию χ2 и индекс гетерогенности I2=99% для чувствительности и специфичности. В связи с этим для анализа данных использовали метод случайных эффектов. Значения чувствительности для МРТ находились в диапазоне от 70,4 до 100%, специфичности ― от 60,6 до 100%.

Заключение. МРТ обладает достаточной чувствительностью и специфичностью для определения злокачественности лёгочных узлов, обнаруженных при КТ-диагностике.

目的是评估胸部MRT与CT检测肺结节的可能性，怀疑有恶性肿瘤。

材料与方法。截至 2021 年 4 月 7 日（含） 进行了 PubMed 和 Google Scholar 数据库 根据资格标准，选择了评估 MRI 和 CT 识别可疑恶性肺淋巴结能力的研究。 分析方法的选择和敏感性和特异性数据的分组是根据评估研究异质性的结果进行的。 为了评估荟萃分析中包括的研究的统计异质性，使用了 Pearson χ2 拟合检验和 I2 异质性指数。

结果。根据检索结果，筛选出 168 项研究，21 项研究纳入荟萃分析。 入选作品包括 1188 名患者。 根据 χ2 标准和异质性指数 I2 = 99% 的敏感性和特异性，荟萃分析显示存在统计学上显着的异质性 p <0.00001。 对此，采用随机效应的方法对数据进行分析。 MRT 的灵敏度值范围从 70.4 到 100%，特异性 - 从 60.6 到 100%。

结论。因此，MRI 具有足够的敏感性和特异性来确定 CT 诊断中发现的肺淋巴结的恶性程度。

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