Uncertainty-aware automatic TNM staging classification for [18F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning
Our 'TNMu' classifier successfully extracts TNM staging information from internal and external PET-CT reports. We concluded that multi-task approaches result in the best performance, and better computational efficiency over single task PLM approaches. We believe these models can improve PET-CT services by assisting in auditing, creating research cohorts, and developing decision support systems. Our approach to handling uncertainty represents a novel first step but has room for further refinement.
Authors
- Yulan He (ORCID: https://orcid.org/0000-0003-3948-5845)
- Gary Cook (ORCID: https://orcid.org/0000-0002-8732-8134)
- Anna Barnes (ORCID: https://orcid.org/0000-0002-6902-214X)
- Sugama Chicklore (ORCID: https://orcid.org/0000-0002-5250-2139)
- Stephen Barlow (ORCID: https://orcid.org/0000-0001-9059-9974)
- Sébastien Ourselin (ORCID: https://orcid.org/0000-0002-5694-5340)
- Thomas Wagner (ORCID: https://orcid.org/0000-0002-3867-6829)
Institutions
- St Thomas' Hospital (GB)
- King's College London (GB)
- The Royal Free Hospital (GB)
- University of Warwick (GB)
- King's College School (GB)
- The Alan Turing Institute (GB)
- University College London (GB)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12911-024-02814-7
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Wellcome
- Cancer Research UK
- EPSRC Centre for Doctoral Training in Medical Imaging