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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">zdor</journal-id><journal-title-group><journal-title xml:lang="ru">Проблемы здоровья и экологии</journal-title><trans-title-group xml:lang="en"><trans-title>Health and Ecology Issues</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2220-0967</issn><issn pub-type="epub">2708-6011</issn><publisher><publisher-name>Gomel State Medical University</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.51523/2708-6011.2022-19-3-08</article-id><article-id custom-type="elpub" pub-id-type="custom">zdor-2312</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>КЛИНИЧЕСКАЯ МЕДИЦИНА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>CLINICAL MEDICINE</subject></subj-group></article-categories><title-group><article-title>Роль магнитно-резонансной томографии в прогнозировании отдаленных результатов лечения рака шейки матки</article-title><trans-title-group xml:lang="en"><trans-title>Role of magnetic resonance imaging in predicting long-term outcomes of cervical cancer treatment</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7576-9928</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Жук</surname><given-names>Е. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Zhuk</surname><given-names>E. G.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Жук Елена Георгиевна, к.м.н., доцент, доцент кафедры лучевой диагностики, ГУО «Белорусская медицинская академия последипломного образования»; врач МРТ рентгеновского отделения, ГУ «Республиканский научно-практический центр онкологии и медицинской радиологии им. Н. Н. Александрова»</p><p>г. Минск</p></bio><bio xml:lang="en"><p>Elena G. Zhuk, PhD (Med), Associate Professor, Associate Professor at the Diagnostic Radiology Department, Belarussian Medical Academy of Postgraduate Education; MRI physician at the X-Ray Department, N.N. Alexandrov National Cancer Centre</p><p>Minsk</p></bio><email xlink:type="simple">elenazhuk.03@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Белорусская медицинская академия последипломного образования; Республиканский научно-практический центр онкологии и медицинской радиологии им. Н. Н. Александрова</institution></aff><aff xml:lang="en"><institution>Belarusian Medical Academy of Postgraduate Education; N. N. Alexandrov National Cancer Centre of Belarus</institution></aff></aff-alternatives><pub-date pub-type="collection"><year>2022</year></pub-date><pub-date pub-type="epub"><day>14</day><month>09</month><year>2022</year></pub-date><volume>19</volume><issue>3</issue><fpage>58</fpage><lpage>64</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Жук Е.Г., 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="ru">Жук Е.Г.</copyright-holder><copyright-holder xml:lang="en">Zhuk E.G.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://journal.gsmu.by/jour/article/view/2312">https://journal.gsmu.by/jour/article/view/2312</self-uri><abstract><p>Цель исследования. Изучить ценность математической модели метастатического поражения лимфатических узлов (ЛУ) по данным магнитно-резонансной томографии (МРТ) при раке шейки матки (РШМ) в сравнительном аспекте с традиционным МРТ-критерием метастатического поражения ЛУ (размер по короткой оси ≥ 1,0 см) оценки прогноза заболевания.Материалы и методы. Для оценки прогноза РШМ анализу подверглись показатели однолетней, пятилетней раково-специфической выживаемости (РСВ) 100 пациентов в сравнительном аспекте: при выявлении метастатических лимфатических узлов (МТЛУ) по данным МРТ на основе применения математической модели диагностики МТЛУ и традиционного МРТ-критерия.Результаты. Сравнение показателей пятилетней РСВ для групп пациентов благоприятного прогноза (N0) c использованием традиционного критерия и математической модели выявило статистически значимую разницу (р &lt; 0,001).Заключение. Разработанная математическая модель метастатического поражения ЛУ по данным МРТ-исследования позволяет прогнозировать неблагоприятное развития РШМ, а также служить руководством для индивидуальной терапии.</p></abstract><trans-abstract xml:lang="en"><p>Objective. To study the value of a mathematical model of lymph node (LN) metastasis according to magnetic resonance imaging (MRI) data in cervical cancer (CC) in a comparative aspect with the traditional MRI criterion of LN metastasis (the size on the short axis being ≥ 1.0 cm) for assessing the prognosis of the disease.Materials and methods. To assess the CC prognosis, the indices of one-year, five-year cancer-specific survival (CSS) rates of 100 patients were analyzed in a comparative aspect: if metastatic lymph nodes (MLNs) are detected according to MRI data based on the use of the mathematical model for the MLN diagnosis and the traditional MRI criterion.Results. The comparison of five-year CSS indices for groups of patients with a favorable prognosis (N0) using the traditional criterion and the mathematical model has revealed a statistically significant difference (р &lt; 0.001).Conclusion. The developed mathematical model of LN metastasis according to MRI data makes it possible to predict the unfavorable development of CC, as well as serves as a guide for individual therapy.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>рак шейки матки</kwd><kwd>магнитно-резонансная томография</kwd><kwd>лимфатический узел</kwd><kwd>метастатический лимфатический узел</kwd><kwd>раково-специфическая выживаемость</kwd></kwd-group><kwd-group xml:lang="en"><kwd>cervical cancer</kwd><kwd>magnetic resonance imaging</kwd><kwd>lymph node</kwd><kwd>metastatic lymph node</kwd><kwd>cancer-specific survival</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. 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