Prognostic Value of Tumor Heterogeneity and SUVmax of Pretreatment 18F-FDG PET/CT for Salivary Gland Carcinoma with High-Risk Histology

Purpose Previous studies have shown that SUVmax on 18F-FDG PET/CT predicts prognosis in patients with salivary gland carcinoma (SGC). Here, we sought to evaluate whether texture features extracted from 18F-FDG PET/CT images may provide additional prognostic information for SGC with high-risk histology. Methods We retrospectively examined pretreatment 18F-FDG PET/CT images obtained from 85 patients with nonmetastatic SGC showing high-risk histology. All patients were treated with curative intent. We used the fixed threshold of 40% of SUVmax for tumor delineation. PET texture features were extracted by using histogram analysis, normalized gray-level co-occurrence matrix, and gray-level size zone matrix. Optimal cutoff points for each PET parameter were derived from receiver operating characteristic curve analyses. Recursive partitioning analysis was used to construct a prognostic model for overall survival (OS). Results Receiver operating characteristic curve analyses revealed that SUVmax, SUV entropy, uniformity, entropy, zone-size nonuniformity, and high-intensity zone emphasis were significantly associated with OS. The strongest associations with OS were found for high SUVmax (>6.67) and high SUV entropy (>2.50). Multivariable Cox analysis identified high SUVmax, high SUV entropy, performance status, and N2c-N3 stage as independent predictors of survival. A prognostic model derived from multivariable analysis revealed that patients with high SUVmax and SUV entropy or with the presence of poor performance status or N2c-N3 were associated with worse OS. Conclusions A prognostic model that includes SUVmax and SUV entropy is useful for risk stratification and supports the additional benefit of texture analysis for SGC with high-risk histology.