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.

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Journal
Cancers
Published
2026-01-13
DOI
https://doi.org/10.3390/cancers18020237
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Radiomics from Routine CT and PET/CT Imaging in Laryngeal Squamous Cell Carcinoma: A Systematic Review with Radiomics Quality Score Assessment

Amar Rajgor, Aileen C. Mill, David W. Hamilton, Bogusław Obara et al.
Cancers
Radiomics and Machine Learning in Medical Imaging
article

Radiomics from Routine CT and PET/CT Imaging in Laryngeal Squamous Cell Carcinoma: A Systematic Review with Radiomics Quality Score Assessment

Amar Rajgor, Aileen C. Mill, David W. Hamilton, Bogusław Obara, Stephen P. Rushton, Terrenjit Gill, Eric Aboagye
article en

Abstract

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.

CancersVol. 18(2)
King's College London (GB), Newcastle upon Tyne Hospitals NHS Foundation Trust (GB), Hammersmith Hospital (GB), Freeman Hospital (GB), Newcastle University (GB)
Openalex Percentile: Top 91%
Radiomics and Machine Learning in Medical Imaging
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Radiomics from Routine CT and PET/CT Imaging in Laryngeal Squamous Cell Carcinoma: A Systematic Review with Radiomics Quality Score Assessment — Amar Rajgor, Aileen C. Mill, et al. · Cancers (2026) | TGRS Research Map | TGRS