The predictive power of baseline metabolic and volumetric [ 18 F]FDG PET parameters with different thresholds for early therapy failure and mortality risk in DLBCL patients undergoing CAR-T-cell therapy.

Objective: [ ¹⁸ F]FDG imaging is an integral part of patient management in CAR-T-cell therapy for recurrent or therapy-refractory DLBCL. The calculation methods of predictive power of specific imaging parameters still remains elusive. With this retrospective study, we sought to evaluate the predictive power of the baseline metabolic parameters and tumor burden calculated with automated segmentation via different thresholding methods for early therapy failure and mortality risk in DLBCL patients.
Materials and Methods: Eighteen adult patients were enrolled, who underwent CAR-T-cell therapy accompanied by at least one pretherapeutic and two posttherapeutic [ ¹⁸ F]FDG PET scans within 30 and 90 days between December 2018 and October 2023. We performed single-click automatic segmentation within VOIs in addition to extracting the SUV parameters to calculate the MTVs and TLGs by applying thresholds based on the concepts of a fixed absolute threshold with an SUV _max > 4.0, a relative absolute threshold with an isocontour of > 40 % of the SUV _max , a background threshold involving the addition of the liver SUV value and its 2 SD values, and only the liver SUV value.
Results: For early therapy failure, baseline metabolic parameters such as the SUV _max , SUV _peak and SUV _mean tended to have greater predictive power than did the baseline metabolic burden. However, the baseline metabolic burden was superior in the prediction of mortality risk regardless of the thresholding method used.
Conclusion: This study revealed that automated delineation methods of metabolic tumor burden using different thresholds do not differ in outcome substantially. Therefore, the current clinical standard with a fixed absolute threshold value of SUV > 4.0 seems to be a feasible option.
Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: BNB received travel support from Kite Gilead and Medac and speaker honoraria from Incyte. He has membership at GLA and EBMT and an advisory role at Kite Gilead. GK received honoraria from MSD, Pfizer, Amgen, Novartis, Gilead, BMSCelgene, Abbvie, Biotest, Takeda, Eurocept, Jazz, Medac, and Eurocept. He received lecture fees from MSD, Pfizer, Amgen, Novartis, Gilead, BMSCelgene, Abbvie, Biotest, Takeda, Eurocept, and Jazz. The other authors declare no conflicts of interest regarding this manuscript. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2024 The Authors.)