This study investigated whether radiomic features derived from preoperative PET images could predict both tumor biology and prognosis in women with invasive squamous cell carcinoma of the vulva. Methods: Patients were retrospectively included if they had a unifocal primary cancer at least 2.6 cm in diameter, received a preoperative18F-FDG PET/CT scan followed by surgery, and had at least 6 mo of follow-up data.18F-FDG PET images were analyzed by semiautomatically drawing a volume of interest on the primary tumor in each PET image, followed by extraction of 83 radiomic features. Unique radiomic features were identified by principal-component analysis (PCA), after which they were compared with histopathology using nonpairwise group comparison and linear regression. Univariate and multivariate Cox regression analyses were used to correlate the identified features with progression-free survival (PFS) and overall survival (OS). Survival curves were estimated using the Kaplan–Meier method. Results: Forty women were included. PCA revealed 4 unique radiomic features, which were not associated with histopathologic characteristics such as grade, depth of invasion, lymph-vascular space invasion, and metastatic lymph nodes. No statistically significant correlation was found between the identified features and PFS. However, Moran’s I, a feature that identifies global spatial autocorrelation, correlated with OS (P 5 0.03). Multivariate Cox regression analysis showed that extracapsular invasion of the metastatic lymph nodes and Moran’s I were independent prognostic factors for PFS and OS. Conclusion: Our data show that PCA is usable to identify specific radiomic features. Although the identified features did not correlate strongly with tumor biology, Moran’s I was found to predict patient prognosis. Larger studies are required to establish the clinical relevance of the observed findings.