International Journal For Multidisciplinary Research

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Advancing Cancer Care: PET/CT Radiomics for Assessing Treatment Response to Chemoradiotherapy in Head and Neck Cancers

Author(s) Dr. Collins Gilbert, Dr. Sayed Ali Sajad Tabibi
Country India
Abstract Head and neck cancers (HNC), comprising malignancies of the oral cavity, pharynx, and larynx, are among the most aggressive and functionally debilitating cancers worldwide. With over 8.5 lakh new cases annually, they present significant morbidity and mortality challenges. Concurrent chemoradiotherapy (CRT) remains the cornerstone of treatment for locally advanced HNC. However, timely and accurate assessment of treatment response is critical to guide subsequent management strategies such as treatment de-escalation, salvage surgery, or immunotherapy. Current assessment modalities, including Response Evaluation Criteria in Solid Tumors (RECIST) and qualitative [^18F]FDG-PET/CT interpretations, often suffer from interobserver variability, delayed changes post-treatment, and insufficient sensitivity in detecting subtle or early biological changes.
This systematic review explores the role of radiomics-based analysis of PET/CT imaging in assessing treatment response to CRT in HNC patients. It aims to summarize current evidence on the prognostic and predictive value of radiomic features extracted from PET/CT, comparing them with conventional metrics such as SUVmax and RECIST-based evaluations.
A comprehensive literature search was conducted using PubMed, Embase, and Scopus databases for studies published between January 2010 and March 2025. Inclusion criteria encompassed original research articles evaluating radiomic features extracted from pre-, mid-, or post-treatment PET/CT scans in HNC patients undergoing CRT. Studies reporting treatment response, disease-free survival (DFS), progression-free survival (PFS), or overall survival (OS) as endpoints were included.
Twenty-eight eligible studies were included in the final analysis. Texture-based radiomic features derived from gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and shape descriptors emerged as strong predictors of CRT response. Radiomic models demonstrated superior prognostic accuracy compared to SUVmax alone. However, heterogeneity in image acquisition protocols, feature extraction methods, and lack of external validation limited clinical applicability.
Radiomics applied to PET/CT imaging holds promise as a non-invasive tool for individualized response assessment in HNC. Future multicentric prospective trials with standardized protocols and harmonized radiomics workflows are imperative for successful clinical translation.
Keywords Radiomics, PET/CT, head and neck cancer, chemoradiotherapy, response prediction, texture analysis, SUVmax, treatment assessment
Field Biology
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-08
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.45410
Short DOI https://doi.org/g9s89r
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