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PurposeDiffuse large B-cell lymphoma (DLBCL) represents the most common subtype of non-Hodgkin lymphoma. Most relapses occur in the first 2 years after diagnosis. Early response assessment with 18F-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET) may facilitate early change of treatment, thereby preventing ineffective treatment and unnecessary side effects. We aimed to assess the predictive value of visually-assessed interim 18F-FDG PET on progression-free survival (PFS) or event-free survival (EFS) in DLBCL patients treated with first-line immuno-chemotherapy regimens.MethodsFor this systematic review and meta-analysis Pubmed, Embase, and the Cochrane Library were searched until July 11, 2017. Prospective and retrospective studies investigating qualitative interim PET response assessment without treatment adaptation based on the interim PET result were eligible. The primary outcome was two-year PFS or EFS. Prognostic and diagnostic measures were extracted and analysed with pooled hazard ratios and Hierarchical Summary Receiver Operator Characteristic Curves, respectively. Meta-regression was used to study covariate effects.ResultsThe pooled hazard ratio for 18 studies comprising 2,255 patients was 3.13 (95%CI 2.52–3.89) with a 95% prediction interval of 1.68–5.83. In 19 studies with 2,366 patients, the negative predictive value for progression generally exceeded 80% (64–95), but sensitivity (33–87), specificity (49–94), and positive predictive values (20–74) ranged widely.ConclusionsThese findings showed that interim 18F-FDG PET has predictive value in DLBCL patients. However, (subgroup) analyses were limited by lack of information and small sample sizes. Some diagnostic test characteristics were not satisfactory, especially the positive predictive value should be improved, before a successful risk stratified treatment approach can be implemented in clinical practice.

PurposeRecently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET–CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1).MethodsThirteen non-small cell lung cancer (NSCLC) and seventeen lymphoma PET–CT studies were used to derive four image datasets—the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4).ResultsWe found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23–30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed.ConclusionsApplication of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data.

IntroductionAlthough 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.MethodsPET/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.ResultsGenerally, 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.ConclusionSUVmeanliver 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 registrationEudraCT: 2006–005,174-42, 01–08-2008.

Next Generation Sequencing-based subtyping and interim- and end of treatment positron emission tomography (i/eot-PET) monitoring have high potential for upfront and on-treatment risk assessment of diffuse large B-cell lymphoma patients. We performed Dana Farber Cancer Institute (DFCI) and LymphGen genetic subtyping for the HOVON84 (n = 208, EudraCT-2006-005174-42) and PETAL ( n  = 204, EudraCT-2006-001641-33) trials retrospectively combined with DFCI genetic data ( n  = 304). For all R-CHOP treated patients (n = 592), C5/MCD- and C2/A53-subtypes show significantly worse outcome independent of the international prognostic index. For all subtypes, adverse prognostic value of i/eot-PET-positive status is confirmed. Consistent with frequent primary refractory disease, only 67% C2 patients become eot-PET-negative versus 81-88% for other subtypes. Indicative of high relapse rates, outcome of C5 i/eot-PET-negative patients remains significantly worse in HOVON-84, which trend validates in the PETAL and SAKK38-07 trials (NCT00544219). These results show the added value of integrated genetic subtyping and PET monitoring for prognostic stratification and subtype-specific trial design. The prognostic impact of genetic subtypes in diffuse large B-cell lymphoma not otherwise specified (DLBCL-NOS) remains unclear. Here, the authors use data from multiple clinical trials to identify DLBCL-NOS genetic subtypes that are associated with patient outcomes, showing their potential value for prognostic stratification, trial design, and PET response monitoring.

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 18 F-fluorodeoxyglucose ( 18 F-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 18 F-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 and Dmax bulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.