A deep learning model for classification of chondroid tumors on CT images
The DLM reliably differentiates benign from malignant cartilaginous tumors and is particularly useful for the differentiation between ACTs and Enchondromas, which is challenging based on CT images only. However, the differentiation between ACTs and high-grade chondrosarcomas remains difficult, reflecting known diagnostic challenges in radiology.
Authors
- Nina Hesse (ORCID: https://orcid.org/0000-0003-3040-3110)
- Hans Roland Dürr (ORCID: https://orcid.org/0000-0002-1347-1173)
- Alexandra S. Gersing (ORCID: https://orcid.org/0000-0003-1687-5541)
- Johanna Luitjens
- Florian T. Gassert (ORCID: https://orcid.org/0000-0003-4126-462X)
- Stephanie E. Weissinger (ORCID: https://orcid.org/0000-0002-5863-2305)
- Alexander Klein (ORCID: https://orcid.org/0000-0002-5562-9872)
- Carolin Knebel (ORCID: https://orcid.org/0000-0003-2594-2419)
- Felix G. Gassert (ORCID: https://orcid.org/0000-0003-2260-8597)
- Carolin Mogler (ORCID: https://orcid.org/0000-0003-3400-7254)
- Jan Caspar Peeken (ORCID: https://orcid.org/0000-0003-2679-9853)
- Florian Hinterwimmer (ORCID: https://orcid.org/0000-0002-0273-0643)
- Stefan Bartzsch (ORCID: https://orcid.org/0000-0001-9550-9122)
- D. A. Lang
- Luca Kohll
Institutions
- University of California, San Francisco (US)
- TUM Klinikum (DE)
- Helmholtz Zentrum München (DE)
- LMU Klinikum (DE)
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1186/s12885-025-13951-1
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16
Funders
- Technische Universität München