Machine learning-based radiomics for bladder cancer staging: evaluating the role of imaging timing in differentiating T2 from T3 disease

ObjectivesAccurate preoperative staging of bladder cancer is essential for therapeutic decision-making, particularly in distinguishing between organ-confined (T2) and extravesical (T3) disease. This study aimed to develop a CT-based radiomics model to differentiate T2 from T3 tumors and to evaluate...

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Автори:	Christoph G. Lisson, Luisa Gallee, Konstantin Müller, Sabitha Manoj, Hannah Stöckl, Friedemann Zengerling, Christian Bolenz, Meinrad Beer, Michael Götz, Catharina S. Lisson
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Frontiers Media S.A. 2025-09-01
Серія:	Frontiers in Oncology
Предмети:	radiomics machine learning bladder cancer tumor staging computed tomography artificial intelligence
Онлайн доступ:	https://www.frontiersin.org/articles/10.3389/fonc.2025.1591742/full
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Machine learning-based radiomics for bladder cancer staging: evaluating the role of imaging timing in differentiating T2 from T3 disease

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