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The objective of this study was to externally validate the clinicalPET model developed in the HOVON-84 trial and to compare the model performance of our clinicalPET model to the international prognostic index (IPI). In total, 1195 Diffuse large B-cell lymphoma patients were included. 887 patients from 6 studies were used as external validation datasets. Primary outcomes were 2-year progression free survival (PFS) and 2-year time to progression (TTP). Metabolic tumor volume (MTV), the maximum distance between the largest lesion and another lesion (Dmaxbulk) and the peak standardized uptake value (SUVpeak) were extracted. The predictive value of the IPI and the HOVON-84 clinicalPET model (MTV, Dmaxbulk, SUVpeak, performance status and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance with the positive predictive value (PPV). Using 2-year PFS as outcome, the IPI yielded an AUC of 0.62 (range:0.51-0.65). The clinicalPET model yielded a significantly higher AUC of 0.71 (range:0.59-0.75, p

Interim 18F-fluorodeoxyglucose positron emission tomography (Interim- 18F-FDG-PET,hereafter I-PET) has the potential to guide treatment of patients with diffuse large B-celllymphoma (DLBCL) if the prognostic value is known. The aim of this study was to determinethe optimal timing and response criteria for evaluating prognosis with I-PET in DLBCL.Individual patient data from 1692 patients with de novo DLBCL were combined and scans were harmonized. I-PET was performed at various time points during treatment with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. Scans were interpreted using the Deauville score (DS) and change in maximum standardized uptake value (DSUVmax ). Multilevel Cox proportional hazards models corrected for International Prognostic Index (IPI) score were used to study the effects oftiming and response criteria on 2-year progression-free survival (PFS). I-PET after 2 cycles (I-PET2) and I-PET4 significantly discriminated good responders from poor responders, with the highest hazard ratios (HRs) for I-PET4. Multivariable HRs for a PET-positive result at I-PET2 and I-PET4 were 1.71 and 2.95 using DS4-5, 4.91 and 6.20 using DS5, and 2.93 and 4.65 using DSUVmax , respectively. DSUVmax identified a larger proportion of poor respondersthan DS5 did. For all criteria, the negative predictive value was >80%, and positivepredictive values ranged from 30% to 70% at I-PET2 and I-PET4. Unlike I-PET1, I-PET3discriminated good responders from poor responders using DS4-5 and DS5 thresholds (HRs,2.94 and 4.67, respectively). I-PET2 and I-PET4 predict good response equally during R-CHOPtherapy in DLBCL. Optimal timing and response criteria depend on the clinical context. Goodresponse at I-PET2 is suggested for de-escalation trials, and poor response using DSUVmax atI-PET4 is suggested for randomized trials that are evaluating new therapies

Purpose This pilot study aimed to determine interobserver reliability and ease of use of three workflows for measuring metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in diffuse large B cell lymphoma (DLBCL). Procedures Twelve baseline [18F]FDG PET/CT scans from DLBCL patients with wide variation in number and size of involved organs and lymph nodes were selected from the international PETRA consortium database. Three observers analyzed scans using three workflows. Workflow A: user-defined selection of individual lesions followed by four automated segmentations (41%SUVmax, A50%SUVpeak, SUV≥2.5, SUV≥4.0). For each lesion, observers indicated their “preferred segmentation.” Individually selected lesions were summed to yield total MTV and TLG. Workflow B: fully automated preselection of [18F]FDG-avid structures (SUV≥4.0 and volume≥3ml), followed by removing non-tumor regions with single mouse clicks. Workflow C: preselected volumes based on Workflow B modified by manually adding lesions or removing physiological uptake, subsequently checked by experienced nuclear medicine physicians. Workflow C was performed 3 months later to avoid recall bias from the initial Workflow B analysis. Interobserver reliability was expressed as intraclass correlation coefficients (ICC). Results Highest interobserver reliability in Workflow A was found for SUV≥2.5 and SUV≥4.0 methods (ICCs for MTV 0.96 and 0.94, respectively). SUV≥4.0 and A50%Peak were most and SUV≥2.5 was the least preferred segmentation method. Workflow B had an excellent interobserver reliability (ICC = 1.00) for MTV and TLG. Workflow C reduced the ICC for MTV and TLG to 0.92 and 0.97, respectively. Mean workflow analysis time per scan was 29, 7, and 22 min for A, B, and C, respectively. Conclusions Improved interobserver reliability and ease of use occurred using fully automated preselection (using SUV≥4.0 and volume≥3ml, Workflow B) compared with individual lesion selection by observers (Workflow A). Subsequent manual modification was necessary for some patients but reduced interobserver reliability which may need to be balanced against potential improvement on prognostic accuracy.

Introduction Diffuse large B-Cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL). Up to one third of patients relapse or fail to achieve complete remission, with poor prognosis. and low response rates to salvage treatments. Early identification of patients unlikely to be cured with R-CHOP might improve their chance of cure and quality of life. Interim [18F]FDG PET (I-PET) measures the pattern of response during treatment, which might add important prognostic value. These measures could be used for I-PET guided treatment for both patients with a good response and patients with a poor response. At this moment it is unclear what the influence of timing and PET positivity criteria is on the prognostic value of I-PET. Therefore, the aim of this individual patient data (IPD) meta-analysis was to determine the optimal timing and optimal PET positivity criteria for I-PET to predict response in DLBCL patients. Methods Individual patient data from 1977 de novo DLBCL patients were obtained from the PETRA database (www.petralymphoma.org). These patients had been enrolled in 9 clinical studies and treated with R-CHOP14, R-CHOP21 or DA-EPOCH-R. All patients had an I-PET following one to four cycles of therapy. Progression free survival (PFS) was the primary endpoint. We used the Deauville score (DS) as a visual assessment of the I-PET with two different cut-offs for PET-positivity: DS 4-5 positive and DS 5 positive. Relative reductions (Δ) of Standardized Uptake Values (SUV) were used for semi-quantitative scoring of I-PET. A cut-off of 66% reduction was used for I-PET scans after 1,2 or 3 cycles and a cut-off of 70% was used for I-PET after 4 cycles. DS were available for 1828 patients and ΔSUV data for 1632 patients. Multilevel Cox proportional hazards models were used to study the effects of timing and PET positivity criteria on 2-year PFS, with I-PET after 2 cycles as reference. Results After correction for Ann Arbor stage and age, there were no significant differences in PFS between the 9 studies. I-PET1 was not able to significantly discriminate between responders and non-responders. I-PET2 and I-PET4 were able to significantly discriminate responders and non-responders, with higher hazard ratios (HR) for I-PET4. HR were 1.65 and 2.36 for DS4-5 positive, 3.69 and 5.28 for DS5 positive and 2.36 and 3.67 for ΔSUV criteria for I-PET2 and I-PET4 respectively. I-PET3 was able to significantly discriminate responders and non-responders only for DS5 positive criteria (HR: 7.92) (Table 1). However, there were few patients with I-PET1 and I-PET3 scans. Regarding I-PET negative patients, there were no significant differences in PFS using any of the response criteria at the 4 timepoints assessed. Regarding I-PET positive patients, there was a significantly lower PFS at I-PET4 than at I-PET2 , using the DS4-5 positive criteria (p = 0.009, HR = 1.52 (95% BI 1.11 - 2.09)) and ΔSUV criteria (p = 0.05, HR = 1.68 (95%BI 1.00-2.82)) but no other significant differences using other criteria or timepoints (Table 2). Conclusions I-PET is able to significantly discriminate between responding and non-responding patients after 2, 3 or 4 cycles of chemotherapy. The optimal timing to identify responders is after 2 cycles, as there is no significant increase in survival at later times, regardless of PET criteria. As the PFS of I-PET4 positive patients is significantly lower than that of I-PET2 patients, I-PET4 might be the optimal timing to identify non-responders. We suggest to perform an I-PET4 scan to identify poor-responding patients. The worst prognostic subgroup is best identified using the DS 5 positive or ΔSUV criteria. Based on these data, I-PET could be used to design response adapted trials for patients with good and poor responses respectively. Disclosures Barrington: Roche: Honoraria, Research Funding, Speakers Bureau; Celgene: Research Funding; Amgen: Research Funding; Bristol Myers Squibb: Research Funding; Takeda: Honoraria, Speakers Bureau. Dührsen:Alexion: Honoraria; Roche: Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Janssen: Honoraria; Teva: Honoraria; CPT: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Celgene: Research Funding. Hüttmann:University Hospital Essen: Employment; Takeda: Honoraria; Gilead: Honoraria. Zucca:Janssen: Research Funding; AstraZenaca: Research Funding; Kite, A Gilead Company: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celltrion Helathcare: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Research Funding; Abbvie: Other: Travel Grant. Higley:FNIH: Research Funding; CCS associates: Employment; NCIS/Leidos: Research Funding. Hutchings:Genmab: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses, Research Funding. Lugtenburg:Celgene: Consultancy, Honoraria; Genmab: Consultancy, Honoraria; Janssen Cilag: Honoraria; Servier: Consultancy, Honoraria, Research Funding; BMS: Consultancy; Roche: Consultancy, Honoraria, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Research Funding. Zijlstra:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria.