Machine learning-based radiomics for bladder cancer staging: evaluating the role of imaging timing in differentiating T2 from T3 disease
This study demonstrates the feasibility of CT-based radiomics using full-volume 3D tumor segmentation to distinguish between T2 and T3 bladder cancer. The integration of clinical biomarkers and consideration of imaging timing significantly improved model performance. These findings support the development of temporally optimized, multimodal prediction models for individualized bladder cancer staging and treatment planning.
Authors
- Sabitha Manoj (ORCID: https://orcid.org/0009-0001-8643-9721)
- Michael Götz (ORCID: https://orcid.org/0000-0003-0984-224X)
- Friedemann Zengerling (ORCID: https://orcid.org/0000-0002-6150-2675)
- Konstantin Müller
- Christoph Gerhard Lisson
- Meinrad Beer (ORCID: https://orcid.org/0000-0001-7523-1979)
- Christian Bolenz (ORCID: https://orcid.org/0000-0002-6171-6040)
- Catharina Silvia Lisson
- Luisa Gallée (ORCID: https://orcid.org/0000-0001-5556-7395)
- Hannah Stöckl
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Ulm (DE)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-26
- DOI
- https://doi.org/10.3389/fonc.2025.1591742
- Citations
- 1
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.01