Definitive chemoradiation is currently the standard treatment for locally advanced head and neck squamous cell carcinoma (LAHNSCC). However, patient responses to the treatment vary, with some experiencing progression within five years of diagnosis. Consequently, there is a pressing need to stratify the risk of progression at diagnosis better, assisting clinicians in making informed treatment decisions. The aim is to employ imaging biomarkers extracted from baseline CT scans to classify patients into high or low-risk progression categories.
In a single-center, retrospective study, baseline CT scans and clinical data from 171 LAHNSCC patients treated with definitive chemoradiation were collected. Tumor segmentation was manually done by Quibim technicians using the Quibim Precision® platform under a radiologist’s supervision. Imaging biomarkers were extracted from each lesion, and the data underwent feature reduction. Following a 5-fold cross-validation, we tested several models for their predictive capacity for 5-year progression at diagnosis.
A XGBoost model, incorporating 12 radiomic features and four clinical variables (primary tumor site [oral cavity], TNM, age, and smoking), emerged as the most predictive for 5-year progression. This model yielded an AUC of 0.74, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.66. Our efforts focus now on increasing the sample size to validate this model and assess progression-free survival outcomes, aiming to provide clinicians with a risk score for more precise patient categorization based on diagnostic CT scans.
Reference:
Bruixola et al. J Clin Oncol 41, 2023 (suppl 16; abstr 6076)
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