Fifteen different 18F-FDG PET/CT qualitative and quantitative parameters investigated as pathological response predictors of locally advanced rectal cancer treated by neoadjuvant chemoradiation therapy

Abstract

The aim of this study was to correlate qualitative visual response and various PET quantification factors with the tumour regression grade (TRG) classification of pathological response to neoadjuvant chemoradiotherapy (CRT) proposed by Mandard. Included in this retrospective study were 69 consecutive patients with locally advanced rectal cancer (LARC). FDG PET/CT scans were performed at staging and after CRT (mean 6.7 weeks). Tumour SUVmax and its related arithmetic and percentage decrease (response index, RI) were calculated. Qualitative analysis was performed by visual response assessment (VRA), PERCIST 1.0 and response cut-off classification based on a new definition of residual disease. Metabolic tumour volume (MTV) was calculated using a 40 % SUVmax threshold, and the total lesion glycolysis (TLG) both before and after CRT and their arithmetic and percentage change were also calculated. We split the patients into responders (TRG 1 or 2) and nonresponders (TRG 3-5). SUVmax MTV and TLG after CRT, RI, ΔMTV% and ΔTLG% parameters were significantly correlated with pathological treatment response (p < 0.01) with a ROC curve cut-off values of 5.1, 2.1 cm(3), 23.4 cm(3), 61.8 %, 81.4 % and 94.2 %, respectively. SUVmax after CRT had the highest ROC AUC (0.846), with a sensitivity of 86 % and a specificity of 80 %. VRA and response cut-off classification were also significantly predictive of TRG response (VRA with the best accuracy: sensitivity 86 % and specificity 55 %). In contrast, assessment using PERCIST was not significantly correlated with TRG. FDG PET/CT can accurately stratify patients with LARC preoperatively, independently of the method chosen to interpret the images. Among many PET parameters, some of which are not immediately obtainable, the most commonly used in clinical practice (SUVmax after CRT and VRA) showed the best accuracy in predicting TRG.