Purpose We evaluated the potential prognostic value of 18 Ffluorodeoxyglucose (FDG) positron emission tomography/ computed tomography (PET/CT) in patients with stage IIIC/ IV endometrial cancer. Methods Patients with stage IIIC/IV endometrial cancer who had undergone FDG PET/CT workup for staging were enrolled. Maximum standardized uptake values (SUVmax) measured from regions of interest (ROIs) of the primary tumor (SUVt) and lymph nodes (SUVn) were correlated with overall survival (OS). The SUVn was defined as the highest SUVmax of the metastatic lymph nodes. Survival probability was assessed using the Kaplan-Meier method. Results A total of 42 patients with a median age of 55.5 years (range 32-76 years) were included. Twenty-nine percent (n =12) of patients were premenopausal and 71 % (n =30) were postmenopausal. The average SUVt was 12.9 (range 1.8-36.5), and the average SUVn was 7.3 (range 2.0-22.5). Median follow-up time was 25.9 months (range 1-84 months). Using a SUVt of 9.5 as a cutoff value, two groups with different rates were determined (P =0.026). In addition, patients with a low SUVn had significantly better OS than those with a high SUVn (P =0.003). Patients in the International Federation of Obstetrics and Gynecology (FIGO) stage IV group with SUVt≥9.5 or SUVn≥7.3 showed a significantly longer OS than the other groups.

Objective To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18 F-fluorodeoxyglucose ( 18 F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18 F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46–1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18 F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

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