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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

We aimed to determine the added value of baseline metabolic tumor volume (MTV) and interim PET (I-PET) to the age-adjusted international prognostic index (aaIPI) to predict 2-y progression-free survival (PFS) in diffuse large B-cell lymphoma. Secondary objectives were to investigate optimal I-PET response criteria (using Deauville score [DS] or quantitative change in SUV(max) [ΔSUV(max)] between baseline and I-PET4 [observational I-PET scans after 4 cycles of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone administered in 2-wk intervals with intensified rituximab in the first 4 cycles [R(R)-CHOP14]). Methods: I-PET4 scans in the HOVON-84 (Hemato-Oncologie voor Volwassenen Nederland [Haemato Oncology Foundation for Adults in the Netherlands]) randomized clinical trial (EudraCT 2006-005174-42) were centrally reviewed using DS (cutoff, 4–5). Additionally, ΔSUV(max) (prespecified cutoff, 70%) and baseline MTV were measured. Multivariable hazard ratio (HR), positive predictive value (PPV), and negative predictive value (NPV) were obtained for 2-y PFS. Results: In total, 513 I-PET4 scans were reviewed according to DS, and ΔSUV(max) and baseline MTV were available for 367 and 296 patients. The NPV of I-PET ranged between 82% and 86% for all PET response criteria. Univariate HR and PPV were better for ΔSUV(max) (4.8% and 53%, respectively) than for DS (3.1% and 38%, respectively). aaIPI and ΔSUV(max) independently predicted 2-y PFS (HR, 3.2 and 5.0, respectively); adding MTV brought about a slight improvement. Low or low-intermediate aaIPI combined with a ΔSUV(max) of more than 70% (37% of patients) yielded an NPV of 93%, and the combination of high-intermediate or high aaIPI and a ΔSUV(max) of 70% or less yielded a PPV of 65%. Conclusion: In this study on diffuse large B-cell lymphoma, I-PET after 4 cycles of R(R)-CHOP14 added predictive value to aaIPI for 2-y PFS, and both were independent response biomarkers in a multivariable Cox model. We externally validated that ΔSUV(max) outperformed DS in 2-y PFS prediction.

PURPOSE: In-vivo quantification of tumor uptake of 89-zirconium (89Zr)-labelled monoclonal antibodies (mAbs) with PET provides a potential tool in strategies to optimize tumor targeting and therapeutic efficacy. A specific challenge for 89Zr-immuno-PET is low tumor contrast. This is expected to result in interobserver variation in tumor delineation. Therefore, the aim of this study was to determine interobserver reproducibility of tumor uptake measures by tumor delineation on 89Zr-immuno-PET scans.METHODS: Data were obtained from previously published clinical studies performed with 89Zr-rituximab, 89Zr-cetuximab and 89Zr-trastuzumab. Tumor lesions on 89Zr-immuno-PET were identified as focal uptake exceeding local background by a nuclear medicine physician. Three observers independently manually delineated volumes of interest (VOI). Maximum, peak and mean standardized uptake values (SUVmax, SUVpeak and SUVmean) were used to quantify tumor uptake. Interobserver variability was expressed as the coefficient of variation (CoV). The performance of semi-automatic VOI delineation using 50% of background-corrected ACpeak was described.RESULTS: In total, 103 VOI were delineated (3-6 days post injection (D3-D6)). Tumor uptake (median, interquartile range) was 9.2 (5.2-12.6), 6.9 (4.0-9.6) and 5.5 (3.3-7.8) for SUVmax, SUVpeak and SUVmean. Interobserver variability was 0% (0-12), 0% (0-2) and 7% (5-14), respectively (n = 103). The success rate of the semi-automatic method was 45%. Inclusion of background was the main reason for failure of semi-automatic VOI.CONCLUSIONS: This study shows that interobserver reproducibility of tumor uptake quantification on 89Zr-immuno-PET was excellent for SUVmax and SUVpeak using a standardized manual procedure for tumor segmentation. Semi-automatic delineation was not robust due to limited tumor contrast.

Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset. Background Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset.Materials and methods The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume.Results The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%).Conclusion When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model. ACKNOWLEDGMENTS The authors thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high-performance computing cluster. FUNDING This work is part of the research program STRaTeGy with project number 14929, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). This study was financed by the Dutch Cancer Society, POINTING project, grant 10034.

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.

Treatment for rheumatoid arthritis (RA) should be started as early as possible to prevent destruction of bone and cartilage in affected joints. A new diagnostic tool for both early diagnosis and therapy monitoring would be valuable to reduce permanent joint damage. Positron emission tomography (PET) imaging of macrophages is a previously demonstrated non-invasive means to visualize (sub)clinical arthritis in RA patients. We developed a kinetic model to quantify uptake of the macrophage tracer [11C]DPA-713 (N,N-diethyl-2-[2-(4-methoxyphenyl)-5,7-dimethylpyrazolo [1,5-a]pyrimidin-3-yl]acetamide) in arthritic joints of RA patients and to assess the performance of several simplified methods. Dynamic [11C]DPA-713 scans of 60 min with both arterial and venous blood sampling were performed in five patients with clinically active disease. [11C]DPA-713 showed enhanced uptake in affected joints of RA patients, with tracer uptake levels corresponding to clinical presence and severity of arthritis. The optimal quantitative model for assessment of [11C]DPA-713 uptake was the irreversible two tissue compartment model (2T3k). Both Ki and standardized uptake value (SUV) correlated with the presence of arthritis in RA patients. Using SUV as an outcome measure allows for a simplified static imaging protocol that can be used in larger cohorts.