18F-FDG PET/CT May Predict Tumor Type and Risk Score in Gestational Trophoblastic Disease. (30th March 2022) - Bezzi, Carolina; Monaco, Lavinia; Ghezzo, Samuele; Mathoux, Gregory; Bergamini, Alice; Zambella, Enrica; Fallanca, Federico; Samanes Gajate, Ana Maria; Presotto, Luca; Sabetta, Giulia; Mangili, Giorgia; Cioffi, Raffaella; et al.
Abstract : Purpose: The aim of this study was to investigate the role of 18 F-FDG PET/CT in predicting pathological prognostic factors, including tumor type and International Federation of Gynecology and Obstetrics (FIGO) score, in gestational trophoblastic disease (GTD). Methods: Retrospective monocentric study including 24 consecutive patients who underwent to 18 F-FDG PET/CT from May 2005 to March 2021 for GTD staging purpose. The following semiquantitative PET parameters were measured from the primary tumor and used for the analysis: maximum standardized uptake value (SUVmax ), SUVmean, metabolic tumor volume (MTV) and total lesion glycolisis (TLG). Statistical analysis included Spearman correlation coefficient to evaluate the correlations between imaging parameters and tumor type (nonmolar trophoblastic vs postmolar trophoblastic tumors) and risk groups (high vs low, defined according to the FIGO score), whereas area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the predictive value of the PET parameters. Mann-Whitney U test was used to further describe the parameter's potential in differentiating the populations. Results: SUVmax and SUVmean resulted fair (AUC, 0.783; 95% confidence interval [CI], 0.56–0.95) and good (AUC, 0.811; 95% CI, 0.59–0.97) predictors of tumor type, respectively, showing a low (ρ = 0.489, adjusted P = 0.030) and moderate (ρ = 0.538, adjusted P = 0.027) correlation. According to FIGO score, TLG was instead a fair predictor (AUC, 0.770; 95% CI, 0.50–0.99) for patient risk stratification. Conclusions: 18 F-FDG PET parameters have a role in predicting GTD pathological prognostic factors, with SUVmax and SUVmean being predictive for tumor type and TLG for risk stratification.