Radiomics from Routine CT and PET/CT Imaging in Laryngeal Squamous Cell Carcinoma: A Systematic Review with Radiomics Quality Score Assessment
case reports, abstracts, editorials, reviews, or conference proceedings, exclusive focus on preclinical or animal models, lack of a clear radiomics methodology, or did not include imaging-based feature extraction. Results were synthesised narratively by modelling objective, alongside formal assessment of methodological quality using the Radiomics Quality Score (RQS). <b>Results</b>: Twenty studies met the inclusion criteria, with most using CT-based radiomics. Seven incorporated PET/CT. Radiomic models demonstrated moderate-to-high accuracy across tasks including T-staging, thyroid cartilage invasion, survival prediction, and local failure. Key predictive features included first-order entropy, skewness, and texture metrics such as size zone non-uniformity and GLCM correlation. Methodological variability, limited external validation, and small samples were frequent limitations. <b>Conclusions</b>: Radiomics holds strong promise as a non-invasive biomarker for laryngeal cancer. However, methodological heterogeneity identified through formal quality assessment indicates that improved standardisation, reproducibility, and multicentre validation are required before widespread clinical implementation.
Authors
- Amar Rajgor
- Aileen C. Mill (ORCID: https://orcid.org/0000-0002-7400-6064)
- David W. Hamilton (ORCID: https://orcid.org/0000-0002-9653-6453)
- Bogusław Obara (ORCID: https://orcid.org/0000-0003-4084-7778)
- Stephen P. Rushton (ORCID: https://orcid.org/0000-0003-1146-2174)
- Terrenjit Gill
- Eric Aboagye
Institutions
- King's College London (GB)
- Newcastle upon Tyne Hospitals NHS Foundation Trust (GB)
- Hammersmith Hospital (GB)
- Freeman Hospital (GB)
- Newcastle University (GB)
Publication Details
- Journal
- Cancers
- Published
- 2026-01-13
- DOI
- https://doi.org/10.3390/cancers18020237
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00