Tumor SUVmax Normalized to Liver Uptake on (18)F-FDG PET/CT Predicts the Pathologic Complete Response After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Abstract

This study investigates the feasibility of using (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) to predict the pCR (pathologic complete response) rate after neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer. A total of 88 patients with locally advanced rectal cancer were retrospectively analyzed. All patients were treated with NCRT, followed by radical surgery, and (18)F-FDG PET/CT was performed before and after NCRT. For a semiquantitative assessment, a volume of interest was drawn, including the whole tumor region, and the maximum SUV (SUVmax), SUVmax normalized to liver uptake (SLR), SUVmax normalized to blood pool uptake (SBR), the metabolic tumor volume at SUV 2.0 (MTV[2.0]), SUV 2.5 (MTV[2.5]), and SUV 3.0 (MTV[3.0]) were measured. In addition, their percentage changes after NCRT were assessed. The pCR was verified through a histologic examination of postsurgical specimens. A receiver operating characteristic curve analysis was conducted to predict the pCR by using these PET parameters. The pCR was predicted in 17 patients (19%). The values of the area under the curve (AUC) for predicting the pCR were 0.774 for SUVmax after NCRT, 0.826 for SLR after NCRT, 0.815 for SBR after NCRT, 0.724 for MTV(2.5) after NCRT, 0.729 for the percentage change in SUVmax, 0.700 for the percentage change in SLR, and 0.749 for the percentage change in MTV (2.5). Among these PET parameters, SLR after NCRT showed the highest AUC value. The optimal criterion, sensitivity, specificity, and accuracy of SLR after NCRT for predicting the pCR were ≤1.41, 88%, 65%, and 68%, respectively. F-FDG PET was found to be useful for predicting the pCR after NCRT in patients with locally advanced rectal cancer. Among various PET parameters, SUVmax normalized to liver uptake after NCRT was the best predictor of the pCR.