Poon, David

AbstractBackgroundQuantitative methods of Fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) interpretation, including the percent change in FDG uptake from baseline (ΔSUV), are under investigation in lymphoma to overcome challenges associated with visual scoring systems (VSS) such as the Deauville 5‐point scale (5‐PS).MethodsIn CALGB 50303, patients with DLBCL received frontline R‐CHOP or DA‐EPOCH‐R, and although there were no significant associations between interim PET responses assessed centrally after cycle 2 (iPET) using 5‐PS with progression‐free survival (PFS) or overall survival (OS), there were significant associations between central determinations of iPET ∆SUV with PFS/OS. In this patient cohort, we retrospectively compared local vs central iPET readings and evaluated associations between local imaging data and survival outcomes.ResultsAgreement between local and central review was moderate (kappa = 0.53) for VSS and high (kappa = 0.81) for ∆SUV categories (<66% vs. ≥66%). ∆SUV ≥66% at iPET was significantly associated with PFS (p = 0.03) and OS (p = 0.002), but VSS was not. Associations with PFS/OS when applying local review vs central review were comparable.ConclusionsThese data suggest that local PET interpretation for response determination may be acceptable in clinical trials. Our findings also highlight limitations of VSS and call for incorporation of more objective measures of response assessment in clinical trials.

Background FDG-PET is used in staging and response assessments in patients (pts) with DLBCL. An analysis of 158 pts with DLBCL receiving R-CHOP or DA-EPOCH-R on CALGB/Alliance 50303 failed to show an association between the interim PET (iPET, after cycle 2 of therapy) visual 5-point scale score (5-PS) by central review and progression-free survival (PFS) or overall survival (OS), but did show a significant association between percent change in FDG uptake from baseline to after cycle 2 (∆SUV) and PFS/OS (Schoder et al. 2020). As central review of FDG-PET is not feasible in routine practice, we retrospectively compared local vs central readings, and determined associations from local imaging data with PFS/OS. Methods 126 pts at baseline and 109 at iPET had local imaging data submitted across 21 institutions. Visual scores 1-3 defined PET- and scores 4-5 defined PET+ disease. ∆SUV was defined as the percent change in the maximum SUV at any site (maxSUV) from baseline to iPET. Continuous ∆SUV and categorized ∆SUV (&lt;66% vs &gt;66%) were analyzed. Differences in quantitative measures were calculated as the local minus central result. The Kappa statistic estimated agreement in PET and ∆SUV groups between local and central review. PFS and OS distributions for local PET and ∆SUV groups were landmarked at iPET, estimated with the Kaplan-Meier method, and compared using the log-rank test. Results At baseline, maxSUV was reported locally and centrally in 118 pts. Median maxSUV was 24.3 (range: 5.9-77.3) by local review and 24.3 (range: 5.9-77.0) by central review. The median difference in maxSUV was 0 (range: -12.9 to 21.9), with 50% of differences between -1.1 and 0.1, and 90% of differences between -7.8 and 4.1. In 106 pts with visual iPET 5-PS results, 52 (49.1%) were PET+ by local review and 37 (34.9%) by central review. Agreement in local and central review was moderate (kappa=0.53), occurring in 81 pts (76.4%; 32 PET+ and 49 PET-). Disagreement occurred in 25 patients, 20 with local PET+ but central PET- and 5 with local PET- but central PET+. iPET maxSUV was reported locally and centrally in 90 pts. Median maxSUV was 3.4 (range: 0-18.3) by local review and 3.3 (range: 1.2-18.2) by central review. The median difference in maxSUV was 0 (range: -8.6 to 2.4), with 50% of differences between -0.3 and 0.4, and 90% of differences between -4.4 and 1.8. ∆SUV was calculated in 87 pts. Median ∆SUV was 84.6% (range: -3.0% to 95.9%) by local review and 85.1% (range: -34.9% to 95.8%) by central review. The median difference in ∆SUV was -0.3 (range: -19.6 to 31.9), with 50% of differences between -2.1 and 1.1, and 90% of differences between -7.1 and 12.3. ∆SUV was &lt;66% in 12 (13.8%) pts by local review and 12 pts by central review. Agreement in local and central review was high (kappa=0.81), occurring in 83 pts (95.4%; 10 with ∆SUV &lt;66%, 73 with ∆SUV &gt;66%). Disagreement occurred in 4 pts, 2 in each direction. Using local data, PFS and OS estimates were numerically lower in pts with iPET+ vs iPET- disease, but did not reach statistical significance (p=0.12 and p=0.15). Two-year estimates for PFS were 79% (95% CI 68-91%) and 89% (95% CI 81-98%), and 2-year estimates for OS were 84% (95% CI 75-95%) and 96% (95% CI 91-100%) in pts with iPET+ and iPET-, respectively. In contrast, ∆SUV groups were significantly associated with PFS and OS (p=0.03 and p=0.002). Two-year estimates for PFS were 56% (95% CI 34-94%) and 87% (95% CI 79-95%) and 2-year estimates for OS were 56% (95% CI 34-94%) and 93% (95% CI 88-99%) in pts with ∆SUV &lt;66% and ∆SUV &gt;66%, respectively. Conclusion Agreement in visual 5-PS at iPET was moderate; most discrepancies arose from a local PET+ result when central review called PET-. Differences in maxSUV and ∆SUV tended to be small. Grouping ∆SUV resulted in only 4 discrepancies (4.6%). Similar to the previous analysis using central data, the separation in PFS and OS curves using local data for ∆SUV was greater than by visual 5-PS. Encouragingly, the agreement in ∆SUV of &lt;66% vs ≥66% was high between local and central review. Overall agreement however may be unduly influenced by the large proportion of pts with ∆SUV ≥66%, with less certainty in agreement for pts with ∆SUV &lt;66%. Larger prospective studies comparing local and central imaging data are warranted to best determine the utility of local imaging in clinical trials. Support: P30 CA008748, U10CA180821, U10CA180882; https://acknowledgments.alliancefound.org. ClinicalTrials.gov Identifier: NCT00118209 (CALGB 50303) Disclosures Kahl: Acerta: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; BeiGene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche Laboratories Inc: Consultancy; Pharmacyclics LLC: Consultancy; Genentech: Consultancy; Celgene Corporation: Consultancy; AstraZeneca Pharmaceuticals LP: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy. Friedberg:Astellas: Consultancy; Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Portola Pharmaceuticals: Consultancy; Seattle Genetics: Research Funding; Roche: Other: Travel expenses; Kite Pharmaceuticals: Research Funding; Bayer: Consultancy. Leonard:MEI Pharma: Consultancy; Sutro: Consultancy; Bayer: Consultancy; BMS/Celgene: Consultancy; GenMab: Consultancy; Roche/Genentech: Consultancy; Gilead/Kite: Consultancy; ADC Therapeutics: Consultancy; Regeneron: Consultancy; Epizyme: Consultancy; Miltenyi: Consultancy; AstraZeneca: Consultancy; Karyopharm: Consultancy. Bartlett:Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Millennium: Research Funding; Merck: Research Funding; Kite, a Gilead Company: Research Funding; Immune Design: Research Funding; Janssen: Research Funding; Forty Seven: Research Funding; BMS/Celgene: Research Funding; BTG: Consultancy; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche/Genentech: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Acerta: Consultancy; Affimed Therapeutics: Research Funding; ADC Therapeutics: Consultancy; Autolus: Research Funding.