18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma.

PURPOSE: Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. MATERIALS AND METHODS: Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. RESULTS: A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. CONCLUSION: When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.

Development and Testing of a Machine Learning Model Using 18F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma.

OBJECTIVE: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18F-fluorodeoxyglucose (18F-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 18F-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 18F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

Characteristics of surgically transposed ovaries on 18F-FDG PET/CT among patients with cancer.

OBJECTIVE: Fertility preservation in women with cancer is important for improving their quality of life. Successful ovarian transposition protects the ovary from radiation and preserves ovarian endocrine function and fertility. With the increasing use of 18F-FDG PET/CT in gynecologic malignancies, the findings of transposed ovaries sometimes vary. This study aimed to characterize the 18F-FDG PET/CT findings of surgically transposed ovaries among a large number of patients with various medical conditions. METHODS: We retrospectively reviewed the medical records, including surgical history, and analyzed the findings of the transposed ovaries of patients who underwent ovarian transposition. Quantitative analysis was performed, and the maximum standardized uptake values (SUVs) were recorded. The Hounsfield unit (HU) and size (measured using the long diameter on the axial image) of the transposed ovary were evaluated. RESULTS: No significant change was found in the SUV of the transposed ovaries in relation to age and time after surgery. In two cases in which metastasis occurred in the transposed ovary, the lesions showed higher SUVs and HUs than did the other non-metastatic transposed ovaries. In several serial follow-up cases, varying 18F-FDG uptake was observed. CONCLUSION: The 18F-FDG uptake pattern of the transposed ovary did not differ from that of the normal ovary. Misinterpretation should be avoided by considering surgical records, presence of surgical clips, and patients' disease state. If there is a high uptake in the transposed ovary, it is necessary to check for soft tissue lesions and differentiate metastasis from the physiologic uptake.

Prognostic Value of Metabolic Activity Measured by (18)F-FDG PET/CT in Patients with Advanced Endometrial Cancer.

PURPOSE: We evaluated the potential prognostic value of (18)F-fluorodeoxyglucose (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. CONCLUSIONS: FDG uptake of primary endometrial cancer and lymph nodes might be a prognostic factor in advanced endometrial cancer. More aggressive therapy could be considered in patients with stage IV endometrial cancer and high SUVt and/or high SUVn.