Development and Validation of 18F-FDG PET/CT-Based Models for Predicting Successful Complete Cytoreduction During Primary Cytoreductive Surgery for Advanced Ovarian cancer

Abstract

Purpose The aim of this study was to develop an 18F-FDG PET/CT-based model to predict complete cytoreduction during primary cytoreductive surgery (CRS) for ovarian cancer (OC). Patients and Methods We retrospectively identified patients with stage III–IV OC who underwent primary CRS between June 2013 and February 2020 at 2 tertiary centers. Patients from each hospital were assigned to training and test sets. The abdominal cavity was divided into 3 sections, and data for the PET/CT-derived parameters were collected through image analysis. Various prediction models were constructed by combining clinicopathologic characteristics and PET/CT-derived parameters. The performance of the model with the highest area under the receiver operating characteristic curve (AUC) was externally validated. Results The training and test sets included 159 and 166 patients, respectively. The median age of patients in the test set was 55 years; 72.3% of them had stage III tumors, and 65.4% underwent complete cytoreduction. Metabolic tumor volume, total lesion glycolysis, and the number of metastatic lesions above the upper margin of the renal vein (area A) were selected among the PET/CT parameters. The best predictive multivariable model consisted of CA-125 (<750 or ≥750 IU/mL), number of metastatic lesions (<2 or ≥2), and metabolic tumor volume of area A, predicting complete cytoreduction with an AUC of 0.768. The model was validated using a test set. Its predictive performance yielded an AUC of 0.771. Conclusions We successfully developed and validated a preoperative model to predict complete cytoreduction in advanced OC. This model can facilitate patient selection for primary CRS in clinical practice.