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<article 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" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Digital Diagnostics</journal-id><journal-title-group><journal-title xml:lang="en">Digital Diagnostics</journal-title><trans-title-group xml:lang="ru"><trans-title>Digital Diagnostics</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>Digital Diagnostics</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2712-8490</issn><issn publication-format="electronic">2712-8962</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">678877</article-id><article-id pub-id-type="doi">10.17816/DD678877</article-id><article-id pub-id-type="edn">PLIIZJ</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Original Study Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Оригинальные исследования</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="zh"><subject>原创性科研成果</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Texture analysis of magnetic resonance imaging in prediction of muscle invasive bladder cancer: a cross-sectional study</article-title><trans-title-group xml:lang="ru"><trans-title>Текстурный анализ магнитно-резонансных томограмм в прогнозировании мышечной инвазии при раке мочевого пузыря: одномоментное исследование</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>磁共振图像纹理分析预测膀胱癌肌层浸润：一项横断面研究</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8276-3594</contrib-id><contrib-id contrib-id-type="spin">6158-0090</contrib-id><name-alternatives><name xml:lang="en"><surname>Kovalenko</surname><given-names>Anastasia A.</given-names></name><name xml:lang="ru"><surname>Коваленко</surname><given-names>Анастасия Андреевна</given-names></name><name xml:lang="zh"><surname>Kovalenko</surname><given-names>Anastasia A.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>nastua_kovalenko@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5649-2193</contrib-id><contrib-id contrib-id-type="spin">8449-6590</contrib-id><name-alternatives><name xml:lang="en"><surname>Sinitsyn</surname><given-names>Valentin E.</given-names></name><name xml:lang="ru"><surname>Синицын</surname><given-names>Валентин Евгеньевич</given-names></name><name xml:lang="zh"><surname>Sinitsyn</surname><given-names>Valentin E.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><bio xml:lang="ru"><p>д-р мед. наук, профессор</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><email>vsini@mail.ru</email><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-8391-2771</contrib-id><contrib-id contrib-id-type="spin">7730-7420</contrib-id><name-alternatives><name xml:lang="en"><surname>Petrovichev</surname><given-names>Victor S.</given-names></name><name xml:lang="ru"><surname>Петровичев</surname><given-names>Виктор Cергеевич</given-names></name><name xml:lang="zh"><surname>Petrovichev</surname><given-names>Victor S.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>канд. мед. наук</p></bio><bio xml:lang="zh"><p>MD, Cand. Sci. (Medicine)</p></bio><email>petrovi4ev@gmail.com</email><xref ref-type="aff" rid="aff4"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-8314-9307</contrib-id><contrib-id contrib-id-type="spin">2384-2473</contrib-id><name-alternatives><name xml:lang="en"><surname>Kovalenko</surname><given-names>Zahar A.</given-names></name><name xml:lang="ru"><surname>Коваленко</surname><given-names>Захар Андреевич</given-names></name><name xml:lang="zh"><surname>Kovalenko</surname><given-names>Zahar A.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>канд. мед. наук</p></bio><bio xml:lang="zh"><p>MD, Cand. Sci. (Medicine)</p></bio><email>zahar_kovalenko@mail.ru</email><xref ref-type="aff" rid="aff4"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Central Clinical Hospital of the Management Affair of President Russian Federation</institution></aff><aff><institution xml:lang="ru">Центральная клиническая больница с поликлиникой Управления делами Президента Российской Федерации</institution></aff><aff><institution xml:lang="zh">Central Clinical Hospital of the Management Affair of President Russian Federation</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies</institution></aff><aff><institution xml:lang="ru">Научно-практический клинический центр диагностики и телемедицинских технологий</institution></aff><aff><institution xml:lang="zh">Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Lomonosov Moscow State University</institution></aff><aff><institution xml:lang="ru">Московский государственный университет имени М.В. Ломоносова</institution></aff><aff><institution xml:lang="zh">Lomonosov Moscow State University</institution></aff></aff-alternatives><aff-alternatives id="aff4"><aff><institution xml:lang="en">National Medical Research Center "Treatment and Rehabilitaion Center", Moscow</institution></aff><aff><institution xml:lang="ru">Национальный медицинский исследовательский центр «Лечебно-реабилитационный центр», Москва</institution></aff><aff><institution xml:lang="zh">National Medical Research Center "Treatment and Rehabilitaion Center", Moscow</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2026-06-05" publication-format="electronic"><day>05</day><month>06</month><year>2026</year></pub-date><pub-date date-type="pub" iso-8601-date="2026-07-21" publication-format="electronic"><day>21</day><month>07</month><year>2026</year></pub-date><volume>7</volume><issue>2</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><issue-title xml:lang="zh"/><fpage>171</fpage><lpage>186</lpage><history><date date-type="received" iso-8601-date="2025-04-24"><day>24</day><month>04</month><year>2025</year></date><date date-type="accepted" iso-8601-date="2026-04-03"><day>03</day><month>04</month><year>2026</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2026, Eco-Vector</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2026, Эко-вектор</copyright-statement><copyright-statement xml:lang="zh">Copyright ©; 2026, Eco-Vector</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="en">Eco-Vector</copyright-holder><copyright-holder xml:lang="ru">Эко-вектор</copyright-holder><copyright-holder xml:lang="zh">Eco-Vector</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc-nd/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://jdigitaldiagnostics.com/DD/article/view/678877">https://jdigitaldiagnostics.com/DD/article/view/678877</self-uri><abstract xml:lang="en"><p><bold>BACKGROUND: </bold>Bladder cancer is the most common malignant neoplasm of the urinary system. The mean age at diagnosis is 73 years, which suggests high risks of comorbidity and clinical risks during invasive diagnostic procedures. Tumor invasion into musculature is a key factor governing treatment choice that necessitates a histological examination. The overall quality of histological examinations relies heavily on biopsy sample adequacy. In this regard, the development and implementation of novel biomarkers based on advanced imaging techniques remain highly relevant, as they may improve the accuracy of T-stage assessment and help predict the clinical course of bladder cancer. From this perspective, the potential application of magnetic resonance imaging texture analysis is being actively discussed.</p> <p><bold>AIM: </bold>To develop and validate clinical and 2D radiomics models for predicting muscle invasion in patients with bladder cancer.</p> <p><bold>METHODS: </bold>A retrospective, cross-sectional, multicenter study was conducted. We randomly assigned 80% of the sample to the training set and 20% to the test set. The results of magnetic resonance imaging of the pelvic organs obtained with intravenous contrast according to a standard protocol on tomographs with a magnetic field induction of 1.5 or 3 T were analyzed. All images were processed using a fixed voxel size of 1 × 1 × 1 mm. Clinical and imaging data were analyzed, and texture-based (radiomics) analysis was performed.</p> <p><bold>RESULTS: </bold>This study included 84 patients. The median age of the patients was 68.5 years [60.75; 75.0]. The clinical imaging model or predicting muscle invasion was based on 6 parameters: Vesical Imaging-Reporting and Data System (VI-RADS) score of 4 or 5, tumor grade, maximum tumor size, age, VI-RADS score of 1 or 2, and number of tumors. Despite satisfactory specificity (78.6%) and accuracy (72.2%) in the test data, the sensitivity of the model was only 50.0%. The radiomics model included 4 features of muscle invasion selected via the LASSO regression method. The radiomics model demonstrated superior performance against the clinical imaging model, achieving higher accuracy (77.8%) and sensitivity (75.0%) for detecting muscle invasion.</p> <p><bold>CONCLUSION: </bold>Texture analysis of magnetic resonance imaging images can be used to differentiate between muscle-invasive and non-muscle-invasive forms of bladder cancer. This study demonstrated the significant scientific and clinical potential of texture analysis for diagnosing muscle invasion in bladder cancer. Our results underscore the potential for further research and substantiate the need for clinical validation.</p></abstract><trans-abstract xml:lang="ru"><p><bold>Обоснование. </bold>Рак мочевого пузыря — наиболее распространённое заболевание среди злокачественных новообразований мочевыделительной системы. Средний возраст постановки диагноза достигает 73 лет, что предполагает высокую частоту коморбидной патологии и возможные клинические риски при применении инвазивных диагностических процедур. Инвазия опухоли в мышечный слой является важным фактором, определяющим лечебную тактику. Для её выявления обязательным диагностическим методом является гистологическое исследование. Однако на его информативность влияют определённые факторы, связанные прежде всего с качеством забора материала. В связи с вышеизложенным сохраняют свою актуальность разработка и внедрение новых маркёров, в том числе на основе современных методов лучевой диагностики, позволяющих уточнить Т-стадию и прогнозировать течение рака мочевого пузыря. С этих позиций активно обсуждают возможности использования текстурного анализа изображений магнитно-резонансной томографии.</p> <p><bold>Цель исследования. </bold>Разработка и проверка клинико-инструментальной и 2D-радиомической моделей для предсказания мышечной инвазии у пациентов с раком мочевого пузыря.</p> <p><bold>Методы. </bold>Проведено многоцентровое одномоментное исследование с анализом ретроспективных данных. Все обследованные разделены случайным образом на обучающую и тестовую выборки в соотношении 80:20. Проанализированы результаты магнитно-резонансной томографии органов малого таза, полученные при внутривенном контрастировании по стандартному протоколу на томографах с индукцией магнитного поля 1,5 или 3 Тл. Предварительную обработку всех изображений выполняли с использованием унифицированного размера вокселя 1 × 1 × 1 мм. Оценивали клинико-инструментальные данные, а также проводили текстурный (радиомический) анализ.</p> <p><bold>Результаты. </bold>В исследование включены данные 84 пациентов. Средний возраст составил 68,5 года [60,75; 75,0]. Разработана клинико-инструментальная модель для прогнозирования мышечной инвазии, включающая шесть переменных: категорию 4–5 по шкале Vesical Imaging-Reporting and Data System (VI-RADS), степень злокачественности опухоли, максимальный размер опухоли, возраст, VI-RADS 1–2, количество опухолей. На тестовой выборке при удовлетворительных показателях специфичности (78,6%) и точности (72,2%) чувствительность модели составляла только 50%. С помощью метода наименьшего абсолютного сжатия и отбора признаков (LASSO-регрессии) в итоговую радиомическую модель включены четыре текстурных признака мышечной инвазии. Установлено, что радиомическая модель превосходит клинико-инструментальную по показателям точности (77,8%) и чувствительности (75,0%) в выявлении мышечной инвазии.</p> <p><bold>Заключение. </bold>Текстурный анализ изображений магнитно-резонансной томографии возможно использовать для дифференциальной диагностики мышечно-инвазивных и немышечно-инвазивных форм рака мочевого пузыря. Проведённое исследование продемонстрировало высокую потенциальную научную и практическую значимость текстурного анализа в диагностике мышечной инвазии при раке мочевого пузыря, что обусловливает перспективность дальнейших исследований в данном направлении и обосновывает целесообразность последующей клинической валидации полученных результатов.</p></trans-abstract><trans-abstract xml:lang="zh"><p><bold>论证：</bold>膀胱癌是泌尿系统最常见的恶性肿瘤。诊断时的平均年龄达到73岁，这表明合并症发生率高，且在应用有创诊断程序时存在潜在的临床风险。肿瘤侵袭肌层是决定治疗策略的重要因素。为检测肌层浸润， 强制性诊断方法是组织学检查。然而，其诊断信息量受某些因素影响，首先与取材质量有关。鉴于上述情况，开发和引入新的标志物，包括基于现代影像学方法（允许明确T分期和预测膀胱癌病程）的标志物，仍然具有现实意义。基于此， 积极讨论了使用磁共振成像（MRI）图像纹理分析的可能性。</p> <p><bold>目的：</bold>开发和验证用于预测膀胱癌患者肌层浸润的临床-影像学模型和二维放射组学模型。</p> <p><bold>方法：</bold>进行了一项多中心横断面研究，并分析了回顾性数据。所有检查病例按80:20的比例随机分为训练集和测试集。分析了在1.5T或3T扫描仪上，按照标准协议进行静脉内对比增强后获得的小骨盆器官磁共振成像结果。所有图像的预处理均使用统一的1×1×1 mm体素尺寸执行。评估了临床-影像学数据，并进行了纹理（放射组学）分析。</p> <p><bold>结果：</bold>研究纳入了84名患者的数据。平均年龄为68.5岁[60.75; 75.0]。开发了一个临床-影像学模型用于预测肌层浸润，包含六个变量：根据膀胱影像报告和数据系统（VI-RADS）的4-5类、肿瘤恶性程度、肿瘤最大尺寸、年龄、VI-RADS 1-2类、肿瘤数量。在测试集上，虽然模型的特异性（78.6%）和准确性（72.2%）令人满意，但灵敏度仅为50%。通过最小绝对收缩和选择算子（LASSO回归）方法，最终的放射组学模型包含了四个与肌层浸润相关的纹理特征。已确定放射组学模型在检测肌层浸润方面优于临床-影像学模型，其准确性（77.8%） 和敏感度（75.0%）更高。</p> <p><bold>结论：</bold>磁共振成像图像的纹理分析可用于鉴别诊断膀胱癌的肌层浸润性和非肌层浸润性形式。本研究展示了纹理分析在膀胱癌肌层浸润诊断中的高度潜在科学和实际意义，这决定了该方向进一步研究的前景，并证明了后续临床验证所获结果的合理性。</p></trans-abstract><kwd-group xml:lang="en"><kwd>radiomics</kwd><kwd>texture analysis</kwd><kwd>bladder cancer</kwd><kwd>MRI</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>радиомика</kwd><kwd>текстурный анализ</kwd><kwd>рак мочевого пузыря</kwd><kwd>МРТ</kwd></kwd-group><kwd-group xml:lang="zh"><kwd>放射组学</kwd><kwd>纹理分析</kwd><kwd>膀胱癌</kwd><kwd>磁共振成像（MRI）</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Halaseh SA, Halaseh S, Alali Y, et al. 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