Prospective Evaluation of Different Methods for Volumetric Analysis on [18F]FDG PET/CT in Pediatric Hodgkin Lymphoma

Abstract

Rationale: Therapy response evaluation by 18F-fluorodeoxyglucose PET/CT (FDG PET) has become a powerful tool for the discrimination of responders from non-responders in pediatric Hodgkin lymphoma (HL). Recently, volumetric analyses have been regarded as a valuable tool for disease prognostication and biological characterization in cancer. Given the multitude of methods available for volumetric analysis in HL, the AIEOP Hodgkin Lymphoma Study Group has designed a prospective analysis of the Italian cohort enrolled in the EuroNet-PHL-C2 trial. Methods: Primarily, the study aimed to compare the different segmentation techniques used for volumetric assessment in HL patients at baseline (PET1) and during therapy: early (PET2) and late assessment (PET3). Overall, 50 patients and 150 scans were investigated for the current analysis. A dedicated software was used to semi-automatically delineate contours of the lesions by using different threshold methods. More specifically, four methods were applied: (1) fixed 41% threshold of the maximum standardized uptake value (SUVmax) within the respective lymphoma site (V41%), (2) fixed absolute SUV threshold of 2.5 (V2.5); (3) SUVmax(lesion)/SUVmean liver >1.5 (Vliver); (4) adaptive method (AM). All parameters obtained from the different methods were analyzed with respect to response. Results: Among the different methods investigated, the strongest correlation was observed between AM and Vliver (rho > 0.9; p < 0.001 for SUVmean, MTV and TLG at all scan timing), along with V2.5 and AM or Vliver (rho 0.98, p < 0.001 for TLG at baseline; rho > 0.9; p < 0.001 for SUVmean, MTV and TLG at PET2 and PET3, respectively). To determine the best segmentation method, we applied logistic regression and correlated different results with Deauville scores at late evaluation. Logistic regression demonstrated that MTV (metabolic tumor volume) and TLG (total lesion glycolysis) computation according to V2.5 and Vliver significantly correlated to response to treatment (p = 0.01 and 0.04 for MTV and 0.03 and 0.04 for TLG, respectively). SUVmean also resulted in significant correlation as absolute value or variation. Conclusions: The best correlation for volumetric analysis was documented for AM and Vliver, followed by V2.5. The volumetric analyses obtained from V2.5 and Vliver significantly correlated to response to therapy, proving to be preferred thresholds in our pediatric HL cohort.

Keywords: FDG PET; Hodgkin’s lymphoma; interim evaluation; method comparison; pediatric; response assessment; volumetric analysis.

Figure 1

MIP (maximal intensity projection) image illustration of the four segmentation techniques applied in our study. From left to right: fixed 41% threshold (V41%), fixed absolute SUV threshold of 2.5 (V2.5), SUVmax(lesion)/SUVmean liver >1.5 (Vliver) and adaptive method (AM). Corresponding MTV (metabolic tumor volumes) at baseline are shown beneath each MIP image. The areas highlighted in red in the figures indicate the contours of the malignant lesions delineated by each segmentation technique.

Figure 2

Scatter plots for linear regression with corresponding correlation coefficient (rho) and p-values related to the different segmentation techniques used (left columns); Bland-Altman plots (right columns) for baseline SUVmean values showing mean and standard deviations (SD) lines, revealing some outliners in all methods. The closer the data with respect to the mean lines and the lower the variation between SD, the high the comparability of the methods. Herein, the calculated limits of agreement are substantial especially for the V41% method.

Figure 3

Scatter plots for linear regression with corresponding correlation coefficient (rho) and p-values related to the different segmentation techniques used (left columns); Bland-Altman plots (right columns) for baseline MTV values showing mean and standard deviations (SD) lines, revealing some outliners in all methods. The closer the data with respect to the mean lines and the lower the variation between SD, the high the comparability of the methods. Herein, the calculated limits of agreement are substantial especially for the V41% method.

Figure 4

Scatter plots for linear regression with corresponding correlation coefficient (rho) and p-values related to the different segmentation techniques used (left columns);Bland-Altman plots (right columns) for baseline TLG values showing mean and standard deviations (SD) lines, revealing some outliners in all methods. The closer the data with respect to the mean lines and the lower the variation between SD, the high the comparability of the methods. Herein, the calculated limits of agreement are substantial especially for the V41% method.