Prognostic value of initial [18F]FDG PET/computed tomography volumetric and texture analysis-based parameters in patients with head and neck squamous cell carcinoma

Aim of workTo determine the predictive value of initial [F-18]FDG PET/computed tomography (CT) volumetric and radiomics-derived analyses in patients with head and neck squamous cell carcinoma (HNSCC). MethodsForty-six adult patients had pathologically proven HNSCC and underwent pretherapy [F-18]FDG PET/CT were enrolled. Semi-quantitative PET-derived volumetric [(maximum standardized uptake value (SUVmax) and mean SUV (SUVmean), total lesion glycolysis (TLG) and metabolic tumor volume (MTV)] and radiomics analyses using LIFEx 6.73.3 software were performed. ResultsIn the current study group, the receiver operating characteristic curve marked a cutoff point of 21.105 for primary MTV with area under the curve (AUC) of 0.727, sensitivity of 62.5%, and specificity of 86.8% (P value 0.041) to distinguish responders from non-responders, while no statistically significant primary SUVmean or max or primary TLG cut off points could be determined. It also marked the cutoff point for survival prediction of 10.845 for primary MTV with AUC 0.728, sensitivity of 80%, and specificity of 77.8% (P value 0.026). A test of the synergistic performance of PET-derived volumetric and textural features significant parameters was conducted in an attempt to develop the most accurate and stable prediction model. Therefore, multivariate logistic regression analysis was performed to detect independent predictors of mortality. With a high specificity of 97.1% and an overall accuracy of 89.1%, the combination of primary tumor MTV and the textural feature gray-level co-occurrence matrix correlation provided the most accurate prediction of mortality (P value < 0.001). ConclusionTextural feature indices are a noninvasive method for capturing intra-tumoral heterogeneity. In our study, a PET-derived prediction model was successfully generated with high specificity and accuracy.