Ontology highlight
ABSTRACT: Prospero id
CRD420251117983. Methods: MEDLINE and EMBASE databases were searched in May 2025.Inclusion criteria
studies published between 1 January 2010 and 31 January 2024, extracted radiomic features from CT, PET/CT, or MRI, and analysed outcomes related to diagnosis, staging, survival, recurrence, or treatment response in laryngeal cancer.Exclusion criteria
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). Results: 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. Conclusions: 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.
SUBMITTER: Rajgor A
PROVIDER: S-EPMC12839367 | biostudies-literature | 2026 Jan
REPOSITORIES: biostudies-literature

Cancers 20260113 2
<b>Background/Objectives</b>: Radiomics, the high-throughput extraction of quantitative features from medical imaging, offers a promising method for identifying laryngeal cancer imaging biomarkers. We aim to systematically review the literature on radiomics in laryngeal squamous cell carcinoma, assessing applications in tumour staging, prognosis, recurrence prediction, and treatment response evaluation.<h4>Prospero id</h4>CRD420251117983. <b>Methods</b>: MEDLINE and EMBASE databases were searche ...[more]