Comparison of different SUV-based methods for response prediction to neoadjuvant radiochemotherapy in locally advanced rectal cancer by FDG-PET and MRI.

PURPOSE

The aim of this study was to compare different analysis methods of 2-deoxy-2-[(18)F]fluoro-D-glucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI) data for prediction of histopathological response (HPR) to neoadjuvant radiochemotherapy (RCTx) in patients with advanced rectal cancer.

PROCEDURES

Twenty-eight patients of a previously published clinical trial underwent serial FDG-PET/computed tomography scans at baseline, 14 days after initiation, and after completion of RCTx. In addition, MRI was performed at baseline and after the end of therapy. Response prediction was correlated with different image analysis algorithms comprising pure metabolic parameters taking into account the FDG uptake, volume-based parameters measuring the lesion volume in either MRI or PET data, and integrated parameters combining metabolic and volumetric information. The established two-dimensional (2D) regions of interest (ROI; diameter 1.5 cm) served as standard of reference. Changes between the parameters at the defined time points were calculated and analyzed for their potential to predict HPR to RCTx using receiver operating characteristic (ROC) analysis. Additionally, the interobserver reliability of fixed-size algorithms was analyzed.

RESULTS

Histopathology classified eight of 28 patients as non-responders and 20 patients as responders to RCTx. ROC analysis of the standard 2D ROI technique revealed areas under the curve (AUCs) of 0.64 and 0.71 for the early and late time points. Corresponding AUCs for three-dimensional (3D) volume of interest technique resulted in AUCs of 0.75 for both early and late time points, respectively. Volumetric parameters showed AUCs ranging from 0.52 to 0.57 (early time points) and 0.46 to 0.76 (later time points), respectively. Corresponding AUCs for the integrated parameters were ranging between 0.70 and 0.73 (early time points) and 0.66 and 0.76 (late time points). Analysis of intra-class correlation coefficients (ICC) for three different readers resulted in the best intra-class correlation values for the changes of 3D standard uptake value (SUV(3D)), for both early (ICC = 0.96) and late (ICC = 0.96) time points, respectively.

CONCLUSIONS

Our study emphasizes that 3D-based approaches for assessing SUV values consistently belonged to the group of parameters with the highest AUC values for prediction of HPR to neoadjuvant RCTx in patients with rectal cancer. MRI was not a good predictor for therapy response; hence, the MRI information derived from combined anatomic and metabolic parameters showed unsatisfying results too.