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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">Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care</journal-id><journal-title-group><journal-title xml:lang="en">Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care</journal-title><trans-title-group xml:lang="ru"><trans-title>Российский вестник детской хирургии, анестезиологии и реаниматологии</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2219-4061</issn><issn publication-format="electronic">2587-6554</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">2005</article-id><article-id pub-id-type="doi">10.17816/psaic2005</article-id><article-id pub-id-type="edn">WJFALA</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="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Preoperative three-dimensional modeling for planning sublobar lung resections in children</article-title><trans-title-group xml:lang="ru"><trans-title>Предоперационное трёхмерное моделирование для планирования сублобарных резекций лёгких у детей</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>儿童肺亚肺叶切除术前三维建模规划</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-8857-7810</contrib-id><contrib-id contrib-id-type="spin">7848-8251</contrib-id><name-alternatives><name xml:lang="en"><surname>Vydysh</surname><given-names>Sofiia V.</given-names></name><name xml:lang="ru"><surname>Выдыш</surname><given-names>София Витальевна</given-names></name><name xml:lang="zh"><surname>Vydysh</surname><given-names>Sofiia V.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD</p></bio><bio xml:lang="zh"><p>MD</p></bio><email>vydysh.sofia@gmail.com</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5302-0502</contrib-id><contrib-id contrib-id-type="spin">6332-2849</contrib-id><name-alternatives><name xml:lang="en"><surname>Topilin</surname><given-names>Oleg G.</given-names></name><name xml:lang="ru"><surname>Топилин</surname><given-names>Олег Григорьевич</given-names></name><name xml:lang="zh"><surname>Topilin</surname><given-names>Oleg G.</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>doc.topilin@gmail.com</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/0000-0001-6871-6804</contrib-id><contrib-id contrib-id-type="spin">2381-5969</contrib-id><name-alternatives><name xml:lang="en"><surname>Pikin</surname><given-names>Oleg V.</given-names></name><name xml:lang="ru"><surname>Пикин</surname><given-names>Олег Валентинович</given-names></name><name xml:lang="zh"><surname>Pikin</surname><given-names>Oleg V.</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>pikin_ov@mail.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff4"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0006-4092-1957</contrib-id><name-alternatives><name xml:lang="en"><surname>Chukanova</surname><given-names>Anna V.</given-names></name><name xml:lang="ru"><surname>Чуканова</surname><given-names>Анна Витальевна</given-names></name><name xml:lang="zh"><surname>Chukanova</surname><given-names>Anna V.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>chukanovaaaa@gmail.com</email><xref ref-type="aff" rid="aff5"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3831-768X</contrib-id><contrib-id contrib-id-type="spin">9674-1049</contrib-id><name-alternatives><name xml:lang="en"><surname>Sokolov</surname><given-names>Yurij Y.</given-names></name><name xml:lang="ru"><surname>Соколов</surname><given-names>Юрий Юрьевич</given-names></name><name xml:lang="zh"><surname>Sokolov</surname><given-names>Yurij Y.</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>sokolov-surg@yandex.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Russian Medical Academy of Continuous Professional Education</institution></aff><aff><institution xml:lang="ru">Российская медицинская академия непрерывного профессионального образования</institution></aff><aff><institution xml:lang="zh">Russian Medical Academy of Continuous Professional Education</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Morozovskaya Children’s City Clinical Hospital</institution></aff><aff><institution xml:lang="ru">Морозовская детская городская клиническая больница</institution></aff><aff><institution xml:lang="zh">Morozovskaya Children’s City Clinical Hospital</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Pirogov Russian National Research Medical University</institution></aff><aff><institution xml:lang="ru">Российский национальный исследовательский медицинский университет им. Н.И. Пирогова</institution></aff><aff><institution xml:lang="zh">Pirogov Russian National Research Medical University</institution></aff></aff-alternatives><aff-alternatives id="aff4"><aff><institution xml:lang="en">National Medical Research Center of Radiology</institution></aff><aff><institution xml:lang="ru">Национальный медицинский исследовательский центр радиологии</institution></aff><aff><institution xml:lang="zh">National Medical Research Center of Radiology</institution></aff></aff-alternatives><aff-alternatives id="aff5"><aff><institution xml:lang="en">I.M. Sechenov First Moscow State Medical University</institution></aff><aff><institution xml:lang="ru">Первый Московский государственный медицинский университет им. И.М. Сеченова</institution></aff><aff><institution xml:lang="zh">I.M. Sechenov First Moscow State Medical University</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2026-05-02" publication-format="electronic"><day>02</day><month>05</month><year>2026</year></pub-date><volume>26</volume><issue>1</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>5</fpage><lpage>18</lpage><history><date date-type="received" iso-8601-date="2026-02-11"><day>11</day><month>02</month><year>2026</year></date><date date-type="accepted" iso-8601-date="2026-03-12"><day>12</day><month>03</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,</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><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://rps-journal.ru/jour/article/view/2005">https://rps-journal.ru/jour/article/view/2005</self-uri><abstract xml:lang="en"><p><bold>BACKGROUND:</bold> Sublobar lung resections in children include wedge (nonanatomic) resection and anatomic segmentectomy. Segmentectomy is used less frequently due to its technical complexity and the lack of objective patient selection criteria. This study aims to address this gap. The authors evaluated whether patient-specific 3D models can standardize preoperative anatomical assessment and thereby support selection of the optimal extent of resection in pediatric patients.</p> <p><bold>AIM:</bold> This study aimed to determine the impact of preoperative 3D modeling on surgical strategy and outcomes in minimally invasive lung resections in children and to identify anatomical and clinical predictors of the extent of resection.</p> <p><bold>METHODS:</bold> The study included 32 children (0–17 years) who underwent minimally invasive lung resection for congenital malformations or benign neoplasms (2020–2025). Patients were divided into a 3D modeling group (<italic>n</italic> = 16) and a standard computed tomography–based planning group (<italic>n</italic> = 16). Outcomes assessed included type of resection, complications, operative time, and concordance between planned and performed resection. Stepwise logistic regression was used to identify predictors of the extent of resection. In an additional analysis, patients were stratified by type of resection (wedge resection, segmentectomy, lobectomy). Quantitative parameters derived from 3D models—such as lesion-to-lobe volume ratio, zoning, and number of involved segments—were used to refine surgical planning algorithms.</p> <p><bold>RESULTS:</bold> 3D-based planning significantly increased the likelihood of performing segmentectomy (odds ratio ≈ 25; <italic>p</italic> = 0.001), while maintaining high concordance between planned and performed procedures without increasing complication rates. The most significant quantitative predictor of resection extent was the lesion-to-lobe volume ratio (optimal threshold 0.205; AUC = 0.922). The type of resection was also associated with age, number of affected segments, and anatomical location.</p> <p><bold>CONCLUSION:</bold> 3D modeling facilitated more frequent use of segmentectomy without increasing complications. The lesion-to-lobe volume ratio and anatomical factors may be used to guide selection of resection type. Incorporation of quantitative 3D parameters may improve the quality of preoperative planning in pediatric lung resections.</p></abstract><trans-abstract xml:lang="ru"><p><bold>Обоснование.</bold> На сегодняшний день к сублобарным резекциям лёгких у детей отнесены краевая (атипичная) резекция и анатомическая сегментэктомия. При этом сегментэктомию применяют значительно реже из-за её технической сложности и отсутствия объективных критериев отбора пациентов. Настоящее исследование направлено на восполнение этого пробела. Авторы оценили, позволяет ли использование индивидуальных 3D-моделей стандартизировать предоперационную оценку анатомии и тем самым обосновать выбор оптимального объёма резекции у пациентов детского возраста.</p> <p><bold>Цель исследования.</bold> Определить влияние предоперационного 3D-моделирования на хирургическую тактику и исходы при мини-инвазивных резекциях лёгких у детей, а также выявить анатомические и клинические предикторы объёма резекции.</p> <p><bold>Методы.</bold> В исследование были включены 32 ребёнка (0–17 лет), перенёсших мини-инвазивную резекцию лёгких по поводу врождённого порока или доброкачественного новообразования (2020–2025 гг.). Пациенты были разделены на группы 3D-моделирования (<italic>n</italic>=16) и стандартного планирования по компьютерной томографии (<italic>n</italic>=16). Оценивали тип резекции, осложнения, время операции и соответствие между планом и выполненной резекцией. Для выявления предикторов объёма резекции применена пошаговая логистическая регрессия. В дополнительном анализе пациенты были разделены по типу резекции — атипичная, сегментэктомия, лобэктомия. Для уточнения алгоритмов хирургического планирования использовали количественные параметры, определённые по 3D-модели, такие как отношение объёма очага к доле, зонирование, число вовлечённых сегментов.</p> <p><bold>Результаты.</bold> 3D-планирование увеличивало вероятность выполнения сегментэктомии (отношение шансов ≈25, <italic>p</italic>=0,001), при этом обеспечивало высокое соответствие между запланированным и выполненным вмешательством и не повышало частоту осложнений. Наиболее значимым количественным предиктором объёма резекции оказалось отношение объёма патологии к доле (оптимальный порог 0,205; AUC=0,922). Тип резекции также зависел от возраста, числа поражённых сегментов и анатомической локализации.</p> <p><bold>Заключение.</bold> 3D-моделирование способствовало более частому выполнению сегментэктомии без увеличения числа осложнений, а отношение объёма патологии к доле и анатомические факторы можно использовать для выбора типа резекции. Учитывание количественных 3D-параметров может повысить качество предоперационного планирования при резекциях лёгких у детей.</p></trans-abstract><trans-abstract xml:lang="zh"><p>论证。目前在小儿肺部手术中，边缘性（非典型）切除术和解剖性肺段切除术被归为肺亚肺叶切除术范畴。然而，由于肺段切除术技术难度较高且缺乏客观的患者筛选标准，其应用频率显著偏低。本研究旨在填补这一空白。作者评估了采用个体化3D模型技术能否标准化术前解剖结构评估，从而为儿科患者确定最佳切除范围提供依据。</p> <p>目的。评估术前三维建模对儿童微创肺切除术中手术策略及预后的影响，并识别影响切除范围的解剖学与临床预测因素。</p> <p>方法。本研究纳入了32名（0-17岁）因先天性肺病或良性肿瘤接受微创肺切除术的儿童（2020-2025年）。 患者被分为三维重建规划组（n=16）和常规CT规划组（n=16）。评估指标包括切除方式、并发症、手术时间以及规划与实施切除的吻合度。采用逐步逻辑回归分析识别切除范围的预测因子。在附加分析中， 按切除类型（非典型切除、肺段切除、肺叶切除）对患者进行分组。利用三维模型确定的量化参数优化手术规划算法，包括病灶体积与肺叶体积比、区域分布特征及受累肺段数量。</p> <p>结果。 研究结果显示：三维规划使肺段切除实施概率显著提升（优势比≈25，p=0.001），在确保规划与实施高度吻合的同时未增加并发症发生率。病灶体积与肺叶体积比是切除范围的最显著量化预测因子（最佳临界值0.205；AUC=0.922）。切除方式同时还受年龄、受累肺段数量和解剖定位等因素影响。</p> <p>结论。 三维建模技术促进了更频繁实施肺段切除术，且未增加并发症发生率；同时，病灶体积与肺叶体积比及解剖学因素可作为选择切除术式的依据。考虑量化3D参数可提高儿童肺切除手术术前规划的质量。</p></trans-abstract><kwd-group xml:lang="en"><kwd>congenital lung malformations</kwd><kwd>benign lung neoplasms</kwd><kwd>thoracoscopy</kwd><kwd>video-assisted thoracoscopic surgery</kwd><kwd>sublobar resection</kwd><kwd>anatomic segmentectomy</kwd><kwd>3D modeling</kwd><kwd>children</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>врождённые пороки развития лёгких</kwd><kwd>доброкачественные новообразования лёгких</kwd><kwd>торакоскопия</kwd><kwd>видеоассистированная торакоскопическая хирургия</kwd><kwd>сублобарная резекция</kwd><kwd>анатомическая сегментэктомия</kwd><kwd>3D-моделирование</kwd><kwd>дети</kwd></kwd-group><kwd-group xml:lang="zh"><kwd>先天性肺发育畸形</kwd><kwd>肺良性肿瘤</kwd><kwd>胸腔镜手术</kwd><kwd>视频辅助胸腔镜外科</kwd><kwd>肺段以下切除</kwd><kwd>解剖性肺段切除</kwd><kwd>三维重建技术</kwd><kwd>儿童群体</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution xml:lang="ru">Московский центр инноваций в здравоохранении</institution></institution-wrap><institution-wrap><institution xml:lang="en">Moscow Center for Healthcare Innovations</institution></institution-wrap><institution-wrap><institution xml:lang="zh">Moscow Center for Healthcare Innovations</institution></institution-wrap></funding-source><award-id>1603-36/23</award-id></award-group><funding-statement xml:lang="en">The authors declare that this study was financially supported by the Moscow Center for Healthcare Innovations (Agreement No. 1603-36/23).</funding-statement><funding-statement xml:lang="ru">Авторы заявляют, что финансовая поддержка исследования была предоставлена Московским центром инноваций в здравоохранении (Соглашение № 1603-36/23).</funding-statement><funding-statement xml:lang="zh">The authors declare that this study was financially supported by the Moscow Center for Healthcare Innovations (Agreement No. 1603-36/23).</funding-statement></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Kunisaki SM. 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