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Introduction Although visual and quantitative assessments of [18F]FDG PET/CT studies typically rely on liver uptake value as a reference or normalisation factor, consensus or consistency in measuring [18F]FDG uptake is lacking. Therefore, we evaluate the variation of several liver standardised uptake value (SUV) measurements in lymphoma [18F]FDG PET/CT studies using different uptake metrics. Methods PET/CT scans from 34 lymphoma patients were used to calculate SUVmaxliver, SUVpeakliver and SUVmeanliver as a function of (1) volume-of-interest (VOI) size, (2) location, (3) imaging time point and (4) as a function of total metabolic tumour volume (MTV). The impact of reconstruction protocol on liver uptake is studied on 15 baseline lymphoma patient scans. The effect of noise on liver SUV was assessed using full and 25% count images of 15 lymphoma scans. Results Generally, SUVmaxliver and SUVpeakliver were 38% and 16% higher compared to SUVmeanliver. SUVmaxliver and SUVpeakliver increased up to 31% and 15% with VOI size while SUVmeanliver remained unchanged with the lowest variability for the largest VOI size. Liver uptake metrics were not affected by VOI location. Compared to baseline, liver uptake metrics were 15–18% and 9–18% higher at interim and EoT PET, respectively. SUVliver decreased with larger total MTVs. SUVmaxliver and SUVpeakliver were affected by reconstruction protocol up to 62%. SUVmax and SUVpeak moved 22% and 11% upward between full and 25% count images. Conclusion SUVmeanliver was most robust against VOI size, location, reconstruction protocol and image noise level, and is thus the most reproducible metric for liver uptake. The commonly recommended 3 cm diameter spherical VOI-based SUVmeanliver values were only slightly more variable than those seen with larger VOI sizes and are sufficient for SUVmeanliver measurements in future studies. Trial registration EudraCT: 2006–005,174-42, 01–08-2008.

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume (MTV) and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). A moderate association of CNN probabilities with MTV (r = 0.57) and Dmaxbulk (r = 0.52) was observed in the external dataset. Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (

Purpose Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment. Methods Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values. Results The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUVpeak and Dmaxbulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively). Conclusion Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance. Trial registration number and date EudraCT: 2006–005,174-42, 01–08-2008.

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

Although visual and quantitative assessments of [18F]FDG PET/CT studies typically rely on liver uptake value as a reference or normalisation factor, consensus or consistency in measuring [18F]FDG uptake is lacking. Therefore, we evaluate the variation of several liver standardised uptake value (SUV) measurements in lymphoma [18F]FDG PET/CT studies using different uptake metrics.PET/CT scans from 34 lymphoma patients were used to calculate SUVmaxGenerally, SUVmaxSUVmeanEudraCT: 2006-005,174-42, 01-08-2008.

Radiomics features may predict outcome in diffuse large B-cell lymphoma (DLBCL). Currently, multiple segmentation methods are used to calculate metabolic tumor volume (MTV). We assessed the influence of segmentation method on the discriminative power of radiomics features in DLBCL at the patient level and for the largest lesion. Methods: Fifty baseline (18)F-FDG PET/CT scans of DLBCL patients with progression or relapse within 2 years after diagnosis were matched on uptake time and reconstruction method with 50 baseline PET/CT scans of DLBCL patients without progression. Scans were analyzed using 6 semiautomatic segmentation methods (SUV threshold of 4.0 [SUV4.0], SUV threshold of 2.5, 41% of SUV(max), 50% of SUV(peak), a majority vote segmenting voxels detected by ≥2 methods, and a majority vote segmenting voxels detected by ≥3 methods). On the basis of these segmentations, 490 radiomics features were extracted at the patient level, and 486 features were extracted for the largest lesion. To quantify the agreement between features extracted from different segmentation methods, the intraclass correlation (ICC) agreement was calculated for each method compared with SUV4.0. The feature space was reduced by deleting features that had high Pearson correlations (≥0.7) with the previously established predictors MTV or SUV(peak). Model performance was assessed using stratified repeated cross validation with 5 folds and 2,000 repeats, yielding the mean receiver-operating-characteristics curve integral for all segmentation methods using logistic regression with backward feature selection. Results: The percentage of features yielding an ICC of at least 0.75, compared with the SUV4.0 segmentation, was lowest for 50% of SUV(peak) both at the patient level and for the largest lesion, with 77.3% and 66.7% of the features yielding an ICC of at least 0.75, respectively. Features did not correlate strongly with MTV, with at least 435 features at the patient level and 409 features for the largest lesion for all segmentation methods having a correlation coefficient of less than 0.7. Features correlated strongly with SUV(peak) (at least 190 at patient level and 134 for the largest lesion were uncorrelated to SUV(peak), respectively). Receiver-operating-characteristics curve integrals ranged between 0.69 ± 0.11 and 0.84 ± 0.09 at the patient level and between 0.69 ± 0.11 and 0.73 ± 0.10 at the lesion level. Conclusion: Even though there are differences in the actual radiomics feature values derived and selected features among segmentation methods, there is no substantial difference in the discriminative power of radiomics features among segmentation methods.

Purpose MYC gene rearrangements in diffuse large B-cell lymphoma (DLBCL) patients are associated with poor prognosis. Our aim was to compare patterns of 2[18F]fluoro-2-deoxy-D-glucose positron emission tomography computed tomography (PET/CT) response in MYC + and MYC- DLBCL patients. Methods Interim PET/CT (I-PET) and end of treatment PET/CT (EoT-PET) scans of 81 MYC + and 129 MYC- DLBCL patients from 2 HOVON trials were reviewed using the Deauville 5-point scale (DS). DS1-3 was regarded as negative and DS4-5 as positive. Standardized uptake values (SUV) and metabolic tumor volume (MTV) were quantified at baseline, I-PET, and EoT-PET. Negative (NPV) and positive predictive values (PPV) were calculated using 2-year overall survival. Results MYC + DLBCL patients had significantly more positive EoT-PET scans than MYC- patients (32.5 vs 15.7%, p = 0.004). I-PET positivity rates were comparable (28.8 vs 23.8%). In MYC + patients 23.2% of the I-PET negative patients converted to positive at EoT-PET, vs only 2% for the MYC- patients (p = 0.002). Nine (34.6%) MYC + DLBCL showed initially uninvolved localizations at EoT-PET, compared to one (5.3%) MYC- patient. A total of 80.8% of EoT-PET positive MYC + patients showed both increased lesional SUV and MTV compared to I-PET. In MYC- patients, 31.6% showed increased SUV and 42.1% showed increased MTV. NPV of I-PET and EoT-PET was high for both MYC subgroups (81.8–94.1%). PPV was highest at EoT-PET for MYC + patients (61.5%). Conclusion MYC + DLBCL patients demonstrate aberrant PET response patterns compared to MYC- patients with more frequent progression during treatment after I-PET negative assessment and new lesions at sites that were not initially involved. Trial registration number and date of registration HOVON-84: EudraCT: 2006–005,174-42, retrospectively registered 01–08-2008. HOVON-130: EudraCT: 2014–002,654-39, registered 26–01-2015

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 Biomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [18F]-FDG PET/CT scans have shown promising results. This study aims to investigate which lesion- and feature-selection approaches/methods resulted in the best prediction of progression after 2 years. Methods A total of 296 patients were included. 485 radiomics features (n = 5 conventional PET, n = 22 morphology, n = 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOI. 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined. Results Models with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.72–0.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs. Conclusion Regardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value. Trial registration number and date: EudraCT: 2006–005174-42, 01–08-2008.

PURPOSE Baseline metabolic tumor volume (MTV) is a promising biomarker in diffuse large B-cell lymphoma (DLBCL). Our aims were to determine the best statistical relationship between MTV and survival and to compare MTV with the International Prognostic Index (IPI) and its individual components to derive the best prognostic model. METHODS PET scans and clinical data were included from five published studies in newly diagnosed diffuse large B-cell lymphoma. Transformations of MTV were compared with the primary end points of 3-year progression-free survival (PFS) and overall survival (OS) to derive the best relationship for further analyses. MTV was compared with IPI categories and individual components to derive the best model. Patients were grouped into three groups for survival analysis using Kaplan-Meier analysis; 10% at highest risk, 30% intermediate risk, and 60% lowest risk, corresponding with expected clinical outcome. Validation of the best model was performed using four studies as a test set and the fifth study for validation and repeated five times. RESULTS The best relationship for MTV and survival was a linear spline model with one knot located at the median MTV value of 307.9 cm3. MTV was a better predictor than IPI for PFS and OS. The best model combined MTV with age as continuous variables and individual stage as I-IV. The MTV-age-stage model performed better than IPI and was also better at defining a high-risk group (3-year PFS 46.3% v 58.0% and 3-year OS 51.5% v 66.4% for the new model and IPI, respectively). A regression formula was derived to estimate individual patient survival probabilities. CONCLUSION A new prognostic index is proposed using MTV, age, and stage, which outperforms IPI and enables individualized estimates of patient outcome.

Aim/Introduction Up to one third of diffuse large B-cell lymphoma (DLBCL) patients experience relapse or fail to achieve complete remission during first-line treatment. Identification of poor prognosis patients might be further improved by radiomics. Radiomics analysis of imaging data provides quantitative features of tumor characteristics such as intensity, shape, volume, texture and intra- and inter-lesion heterogeneity. The objective of this study was to assess the added value of baseline quantitative radiomics features in DLBCL patients compared to currently used clinical characteristics such as the IPI score. Materials and Methods 317 newly diagnosed DLBCL patients with baseline 18F-FDG PET/CT scans from the HOVON84 trial (Lugtenburg et al, JCO 2020) were included. Lesions were delineated using a fully automated preselection of 18F-FDG avid structures defined by a SUV ≥ 4.0 and volume >3mL. Missed lesions were added and non-tumour regions were removed (accurate tool, https://petralymphoma.org). Next, 490 radiomics features were extracted from the volume of interest using RaCat software (Pfaehler et al, 2019). To reduce feature space dimensions, we made a preselection of clinically most relevant radiomics features based on literature (SUVmax, SUVmean, SUVpeak, total lesion glycolysis, metabolic tumor volume (MTV), dissemination features and sphericity) and used logistic regression with backward feature selection to predict 2-year time to progression (TTP), defined as time from baseline PET/CT to progression. Patients who died without progression were censored at date of death. Furthermore, we tested the predictive value of known clinical predictors (age (cut off: >60 years), WHO performance status (cut-off: ≥ 1 and ≥2), Ann Arbor stage, extranodal involvement (cut-off: ≥ 1 and >1), lactate dehydrogenase (LDH) level and bulky disease (≥ 10 cm)) and of a model that combined radiomics and clinical parameters. Model performance was assessed using repeated cross-validation (5 folds, 2000 repeats) yielding the mean receiver-operator-characteristics curve integral (AUC). High- and low-risk groups were defined based on prevalence of events, diagnostic performance was assessed using positive- and negative predictive values. Patients censored before 2 years of follow-up were excluded for the prediction models and diagnostic performance. Results The categorical IPI score yielded in a cross-validated AUC (CV-AUC) of 0.68 (Figure 1). The highest performance for the radiomics model was observed for the natural logarithms of MTV and SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk), which yielded in a cross-validated AUC (CV-AUC) of 0.75±0.07, which was significantly higher than the discriminative power of the MTV model (CV-AUC: 0.66±0.08, p=0.01). LDH/upper limit of normal, WHO performance status ≥1 and extranodal involvement ≥1 showed the highest performance for the clinical prediction model with a CV-AUC of 0.71±0.08. When combining radiomics features with clinical predictors, the highest performance was observed for the natural logarithms of MTV and SUVpeak, Dmaxbulk, WHO performance status ≥ 1 and age, with a CV-AUC of 0.77±0.07, which was significantly higher than the IPI model (p=0.003). Adding radiomics features to clinical predictors increased the positive predictive value with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP: 28.3% vs 43.8%, respectively). Conclusion Combining quantitative radiomics features extracted from baseline 18F-FDG PET/CT scans with components of the IPI score significantly improved identification of patients at risk of relapse at baseline. Adding radiomics features can significantly increase the efficiency of clinical trials in poor prognosis patients. Figure Disclosures Lugtenburg: Incyte: Honoraria; Celgene: Honoraria; Genmab: Honoraria; Genentech: Honoraria; Roche: Research Funding; Servier: Honoraria, Research Funding; Takeda: Honoraria, Research Funding. Zijlstra:Roche: Research Funding.

Metabolic tumor volume (MTV) on interim PET (I-PET) is a potential prognostic biomarker for diffuse large B-cell lymphoma (DLBCL). Implementation of MTV on I-PET requires a consensus on which semiautomated segmentation method delineates lesions most successfully with least user interaction. Methods used for baseline PET are not necessarily optimal for I-PET because of lower lesional SUVs at I-PET. Therefore, we aimed to evaluate which method provides the best delineation quality for Deauville score (DS) 4–5 DLBCL lesions on I-PET at the best interobserver agreement on delineation quality and, second, to assess the effect of lesional SUV(max) on delineation quality and performance agreement. Methods: DS 4–5 lesions from 45 I-PET scans were delineated using 6 semiautomated methods: a fixed SUV threshold of 2.5 g/cm(3), a fixed SUV threshold of 4.0 g/cm(3), an adaptive threshold corrected for source-to-local background activity contrast at 50% of the SUV(peak), 41% of SUV(max) per lesion, a majority vote including voxels detected by at least 2 methods, and a majority vote including voxels detected by at least 3 methods (MV3). Delineation quality per MTV was rated by 3 independent observers as acceptable or nonacceptable. For each method, observer scores on delineation quality, specific agreement, and MTV were assessed for all lesions and per category of lesional SUV(max) (10). Results: In 60 DS 4–5 lesions on I-PET, MV3 performed best, with acceptable delineation in 90% of lesions and a positive agreement of 93%. Delineation quality scores and agreement per method strongly depended on lesional SUV: the best delineation quality scores were obtained using MV3 in lesions with an SUV(max) of less than 10 and using SUV4.0 in more (18)F-FDG–avid lesions. Consequently, overall delineation quality and positive agreement improved by applying the most preferred method per SUV category instead of using MV3 as the single best method. The MV3- and SUV4.0-derived MTVs of lesions with an SUV(max) of more than 10 were comparable after exclusion of visually failed MV3 contouring. For lesions with an SUV(max) of less than 10, MTVs using different methods correlated poorly. Conclusion: On I-PET, MV3 performed best and provided the highest interobserver agreement regarding acceptable delineations of DS 4–5 DLBCL lesions. However, delineation-method preference strongly depended on lesional SUV. Therefore, we suggest exploration of an approach that identifies the optimal delineation method per lesion as a function of tumor (18)F-FDG uptake characteristics, that is, SUV(max).

Introduction Genetic abnormalities, such as MYC oncogene rearrangements, contribute to the outcome heterogeneity in diffuse large B-cell lymphoma (DLBCL) patients. These rearrangements occur in 10-15% of DLBCL patients and have been associated with a poor prognosis. Recently, radiomics features extracted from PET/CT scans have shown to be predictive of outcome. The aim of this study was to investigate if the ability to predict outcome in DLBCL can be improved by combining different clinical, radiomics and genetic features. Methods 323 DLBCL patients from the HOVON-84, HOVON-130, and PETAL trials with a baseline PET/CT scan and a minimum follow-up of two years were included. MYC status was assessed using Fluorescence in situ hybridization (FISH). 245 patients were MYC negative, whereas 25 patients had a MYC rearrangement and 57 patients had MYC and BCL2 and/or BCL6 rearrangements. Lesions were delineated using a semi-automated preselection of 18F-FDG avid structures defined by a SUV4.0 threshold using the ACCURATE tool. Next, 5 conventional PET features (maximum standardized uptake value (SUV max), SUV peak, SUV mean, metabolic tumor volume (MTV) and total lesion glycolysis and 18 dissemination features were extracted. Dissemination features were pertaining to distance between lesions, differences in uptake between lesions and differences in volume between lesions. Logistic regression with backward feature selection was used to predict 2-year time to progression, defined as time from baseline PET/CT to progression. We tested the predictive value of 4 models. 1) a clinical model using individual components of the international prognostic index (IPI): Ann Arbor stage (categorical), WHO performance status (categorical), lactate dehydrogenase (LDH) levels (dichotomous) and age (continuous), 2) a model that included clinical and genetic predictors: MYC status (categorical) and IPI components, 3) a model that included radiomics features: 5 conventional PET and 18 dissemination features and 4) a model that combined clinical and genetic predictors with radiomics features. Model performance was assessed using repeated cross-validation (5-fold, 1000 repeats) yielding the cross-validated area under the curve of the receiver-operator-characteristics curve (CV-AUC). To match prevalence of MYC-positive patients with real-world prevalence (Rosenwald et al, JCO 2019) all 245 MYC-negative patients were used for each repeat, and 10 MYC-FISH_positive DLBCL patients and 20 patients with MYC and BCL2 and/or BCL6 rearrangements were selected using random stratified sampling. Regression coefficients were averaged over all folds to calculate the probability of progression for all patients. High- and low-risk groups were defined based on prevalence of events and the diagnostic performance was assessed using positive- and negative predictive values. Results The highest model performance for the clinical model was observed when combining Ann Arbor stage, LDH and extranodal involvement and yielded in a CV-AUC of 0.69 (95% confidence interval (CI): 0.52-0.83). MYC status combined with WHO performance status, LDH and extranodal involvement yielded an improved CV-AUC of 0.71 (95% CI: 0.52-0.86). The highest model performance for the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and any other lesions (Dmax bulk), the maximum difference in SUV peak between two lesions (DSUVpeak patient) and the maximum difference in volume between two lesions (DVol patient) yielding a CV-AUC of 0.77 (95% CI: 0.62-0.90). The optimal combined model included MYC status, WHO performance status, LDH, MTV, Dmax patient, DSUVpeak patient and DVol patient after backward feature selection and yielded a CV-AUC of 0.77 (95% CI: 0.60 - 0.90). The positive predictive value was highest for the combined model (53.0%) and increased by 20% compared to the optimal clinical model (33.1%). Negative predictive values were comparable between models and ranged between 85.8-88.1%. Conclusions Prediction models using 18F-FDG PET/CT radiomics features at baseline aids in identifying DLBCL patients at high risk for relapse. The positive predictive value increased when radiomics features were added to the clinical and genetic parameters. Therefore, radiomics features can increase the efficiency of clinical trials by only selecting poor prognosis patients. Figure 1 Figure 1. Disclosures Chamuleau: Gilead: Research Funding; Genmab: Research Funding; Celgene: Research Funding. Lugtenburg: Incyte: Honoraria; Regeneron: Honoraria; Genmab: Honoraria; Servier: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Celgene: Honoraria, Other: Non-financial support; Travel support; Roche: Honoraria, Research Funding. Dührsen: CPT Cellex Patient Treatment: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Hüttmann: Celgene: Honoraria; Gilead: Honoraria; Lead Discovery Center GmbH: Consultancy; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Richter: HTG Molecular Diagnostics, Inc.: Current Employment, Research Funding. Klapper: Regeneron: Consultancy, Research Funding; Amgen: Research Funding; Roche: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Zijlstra: Takeda: Research Funding.

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.