Surgical staging is the most confidential method for prognosis prediction. However, in which stage the surgery is needed and the treatment management of these patients is controversial. Presentation of new determinant factors with imaging methods for prediction of poor prognosis can provide better disease management. The aim of our study was to demonstrate the ability of metabolic tumor volume and total lesion glycolysis as a prognostic factor to predict the disease-free survival time, necessity of adjuvant radiotherapy–chemotherapy, and the association of these parameters with the clinicopathological features.
Forty-four endometrial cancer diagnosed patients whose PET/CT scans were performed for treatment planning were included in our study. Metabolic parameters (SUVmax, metabolic tumor volume, total lesion glycolysis) of the primary tumor were calculated. Abdominal hysterectomy was performed for all patients. Histopathologic findings were noted. Patients were followed for 31.4 ± 14.8 months.
Metabolic tumor volume and total lesion glycolysis were significant prognostic factors for disease-free survival, whereas SUVmax did not effect disease-free survival. According to regression analysis, only metabolic tumor volume was found significant for radiotherapy planning (cutoff metabolic tumor volume; 26.30 ml). There was significant association between metabolic tumor volume, total lesion glycolysis and early-stage, myometrial invasion, and lymph node positivity. We observed only weak association between SUVmax and myometrial invasion. ROC curve calculated metabolic tumor volume and total lesion glycolysis cutoff values as 19.6 ml and 90 g for early-stage, 14.3 ml and 173.4 g for myometrial invasion, and 29.7 ml and 283.1 g for lymph node positivity, respectively.
Metabolic tumor volume and total lesion glycolysis may be used as prognostic factors for endometrial cancer. The association between SUVmax and clinical findings, disease-free survival, histopathological features are weak. Further studies are needed for demonstrating the prognostic value of metabolic volumetric parameters.