Meta-Analysis of the Test-Retest Repeatability of [18F]-Fluorodeoxyglucose Standardized Uptake Values: Implications for Assessment of Tumor Response.

PURPOSE: Currently, guidelines for PET with 18F-fluorodeoxyglucose (FDG-PET) interpretation for assessment of therapy response in oncology primarily involve visual evaluation of FDG-PET/CT scans. However, quantitative measurements of the metabolic activity in tumors may be even more useful in evaluating response to treatment. Guidelines based on such measurements, including the European Organization for Research and Treatment of Cancer Criteria and PET Response Criteria in Solid Tumors, have been proposed. However, more rigorous analysis of response criteria based on FDG-PET measurements is needed to adopt regular use in practice. EXPERIMENTAL DESIGN: Well-defined boundaries of repeatability and reproducibility of quantitative measurements to discriminate noise from true signal changes are a needed initial step. An extension of the meta-analysis from de Langen and colleagues (2012) of the test-retest repeatability of quantitative FDG-PET measurements, including mean, maximum, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak, respectively), was performed. Data from 11 studies in the literature were used to estimate the relationship between the variance in test-retest measurements with uptake level and various study-level, patient-level, and lesion-level characteristics. RESULTS: Test-retest repeatability of percentage fluctuations for all three types of SUV measurement (max, mean, and peak) improved with higher FDG uptake levels. Repeatability in all three SUV measurements varied for different lesion locations. Worse repeatability in SUVmean was also associated with higher tumor volumes. CONCLUSIONS: On the basis of these results, recommendations regarding SUV measurements for assessing minimal detectable changes based on repeatability and reproducibility are proposed. These should be applied to differentiate between response categories for a future set of FDG-PET-based criteria that assess clinically significant changes in tumor response.

[89Zr]Zr-DFO-girentuximab and [18F]FDG PET/CT to Predict Watchful Waiting Duration in Patients with Metastatic Clear-cell Renal Cell Carcinoma.

PURPOSE: Watchful waiting (WW) can be considered for patients with metastatic clear-cell renal cell carcinoma (mccRCC) with good or intermediate prognosis, especially those with <2 International Metastatic RCC Database Consortium criteria and ≤2 metastatic sites [referred to as watch and wait ("W&W") criteria]. The IMaging PAtients for Cancer drug SelecTion-Renal Cell Carcinoma study objective was to assess the predictive value of [18F]FDG PET/CT and [89Zr]Zr-DFO-girentuximab PET/CT for WW duration in patients with mccRCC. EXPERIMENTAL DESIGN: Between February 2015 and March 2018, 48 patients were enrolled, including 40 evaluable patients with good (n = 14) and intermediate (n = 26) prognosis. Baseline contrast-enhanced CT, [18F]FDG and [89Zr]Zr-DFO-girentuximab PET/CT were performed. Primary endpoint was the time to disease progression warranting systemic treatment. Maximum standardized uptake values (SUVmax) were measured using lesions on CT images coregistered to PET/CT. High and low uptake groups were defined on the basis of median geometric mean SUVmax of RECIST-measurable lesions across patients. RESULTS: The median WW time was 16.1 months [95% confidence interval (CI): 9.0-31.7]. The median WW period was shorter in patients with high [18F]FDG tumor uptake than those with low uptake (9.0 vs. 36.2 months; HR, 5.6; 95% CI: 2.4-14.7; P < 0.001). Patients with high [89Zr]Zr-DFO-girentuximab tumor uptake had a median WW period of 9.3 versus 21.3 months with low uptake (HR, 1.7; 95% CI: 0.9-3.3; P = 0.13). Patients with "W&W criteria" had a longer median WW period of 21.3 compared with patients without: 9.3 months (HR, 1.9; 95% CI: 0.9-3.9; Pone-sided = 0.034). Adding [18F]FDG uptake to the "W&W criteria" improved the prediction of WW duration (P < 0.001); whereas [89Zr]Zr-DFO-girentuximab did not (P = 0.53). CONCLUSIONS: In patients with good- or intermediate-risk mccRCC, low [18F]FDG uptake is associated with prolonged WW. This study shows the predictive value of the "W&W criteria" for WW duration and shows the potential of [18F]FDG-PET/CT to further improve this.

Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma.

We investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography-computed tomography of 323 patients, which included maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUVpeak between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients.

Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features.

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.

Convolutional neural networks for automatic image quality control and EARL compliance of PET images.

BACKGROUND: Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. MATERIALS AND METHODS: 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using fivefold cross-validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations. RESULTS: In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. CONCLUSION: The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by, e.g., adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

Dual time point imaging in locally advanced head and neck cancer to assess residual nodal disease after chemoradiotherapy.

BACKGROUND: FDG-PET/CT has a high negative predictive value to detect residual nodal disease in patients with locally advanced squamous cell head and neck cancer after completing concurrent chemoradiotherapy (CCRT). However, the positive predictive value remains suboptimal due to inflammation after radiotherapy, generating unnecessary further investigations and possibly even surgery. We report the results of a preplanned secondary end point of the ECLYPS study regarding the potential advantages of dual time point FDG-PET/CT imaging (DTPI) in this setting. Standardized dedicated head and neck FDG-PET/CT images were obtained 12 weeks after CCRT at 60 and 120 min after tracer administration. We performed a semiquantitative assessment of lymph nodes, and the retention index (RI) was explored to optimize diagnostic performance. The reference standard was histology, negative FDG-PET/CT at 1 year, or > 2 years of clinical follow-up. The time-dependent area under the receiver operator characteristics (AUROC) curves was calculated. RESULTS: In total, 102 subjects were eligible for analysis. SUV values increased in malignant nodes (median SUV1 = 2.6 vs. SUV2 = 2.7; P = 0.04) but not in benign nodes (median SUV1 = 1.8 vs. SUV2 = 1.7; P = 0.28). In benign nodes, RI was negative although highly variable (median RI = - 2.6; IQR 21.2), while in malignant nodes RI was positive (median RI = 12.3; IQR 37.2) and significantly higher (P = 0.018) compared to benign nodes. A combined threshold (SUV1 ≥ 2.2 + RI ≥ 3%) significantly reduced the amount of false-positive cases by 53% (P = 0.02) resulting in an increased specificity (90.8% vs. 80.5%) and PPV (52.9% vs. 37.0%), while sensitivity (60.0% vs. 66.7%) and NPV remained comparably high (92.9% vs. 93.3%). However, AUROC, as overall measure of benefit in diagnostic accuracy, did not significantly improve (P = 0.62). In HPV-related disease (n = 32), there was no significant difference between SUV1, SUV2, and RI in malignant and benign nodes, yet this subgroup was small. CONCLUSIONS: DTPI did not improve the overall diagnostic accuracy of FDG-PET/CT to detect residual disease 12 weeks after chemoradiation. Due to differences in tracer kinetics between malignant and benign nodes, DTPI improved the specificity, but at the expense of a loss in sensitivity, albeit minimal. Since false negatives at the 12 weeks PET/CT are mainly due to minimal residual disease, DTPI is not able to significantly improve sensitivity, but repeat scanning at a later time (e.g. after 12 months) could possibly solve this problem. Further study is required in HPV-associated disease.

Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index.

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.

3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans.

Acquisition time and injected activity of 18F-fluorodeoxyglucose (18F-FDG) PET should ideally be reduced. However, this decreases the signal-to-noise ratio (SNR), which impairs the diagnostic value of these PET scans. In addition, 89Zr-antibody PET is known to have a low SNR. To improve the diagnostic value of these scans, a Convolutional Neural Network (CNN) denoising method is proposed. The aim of this study was therefore to develop CNNs to increase SNR for low-count 18F-FDG and 89Zr-antibody PET. Super-low-count, low-count and full-count 18F-FDG PET scans from 60 primary lung cancer patients and full-count 89Zr-rituximab PET scans from five patients with non-Hodgkin lymphoma were acquired. CNNs were built to capture the features and to denoise the PET scans. Additionally, Gaussian smoothing (GS) and Bilateral filtering (BF) were evaluated. The performance of the denoising approaches was assessed based on the tumour recovery coefficient (TRC), coefficient of variance (COV; level of noise), and a qualitative assessment by two nuclear medicine physicians. The CNNs had a higher TRC and comparable or lower COV to GS and BF and was also the preferred method of the two observers for both 18F-FDG and 89Zr-rituximab PET. The CNNs improved the SNR of low-count 18F-FDG and 89Zr-rituximab PET, with almost similar or better clinical performance than the full-count PET, respectively. Additionally, the CNNs showed better performance than GS and BF.

The Impact of Semiautomatic Segmentation Methods on Metabolic Tumor Volume, Intensity, and Dissemination Radiomics in 18F-FDG PET Scans of Patients with Classical Hodgkin Lymphoma.

Consensus about a standard segmentation method to derive metabolic tumor volume (MTV) in classical Hodgkin lymphoma (cHL) is lacking, and it is unknown how different segmentation methods influence quantitative PET features. Therefore, we aimed to evaluate the delineation and completeness of lesion selection and the need for manual adaptation with different segmentation methods, and to assess the influence of segmentation methods on the prognostic value of MTV, intensity, and dissemination radiomics features in cHL patients. Methods: We analyzed a total of 105 18F-FDG PET/CT scans from patients with newly diagnosed (n = 35) and relapsed/refractory (n = 70) cHL with 6 segmentation methods: 2 fixed thresholds on SUV4.0 and SUV2.5, 2 relative methods of 41% of SUVmax (41max) and a contrast-corrected 50% of SUVpeak (A50P), and 2 combination majority vote (MV) methods (MV2, MV3). Segmentation quality was assessed by 2 reviewers on the basis of predefined quality criteria: completeness of selection, the need for manual adaptation, and delineation of lesion borders. Correlations and prognostic performance of resulting radiomics features were compared among the methods. Results: SUV4.0 required the least manual adaptation but tended to underestimate MTV and often missed small lesions with low 18F-FDG uptake. SUV2.5 most frequently included all lesions but required minor manual adaptations and generally overestimated MTV. In contrast, few lesions were missed when using 41max, A50P, MV2, and MV3, but these segmentation methods required extensive manual adaptation and overestimated MTV in most cases. MTV and dissemination features significantly differed among the methods. However, correlations among methods were high for MTV and most intensity and dissemination features. There were no significant differences in prognostic performance for all features among the methods. Conclusion: A high correlation existed between MTV, intensity, and most dissemination features derived with the different segmentation methods, and the prognostic performance is similar. Despite frequently missing small lesions with low 18F-FDG avidity, segmentation with a fixed threshold of SUV4.0 required the least manual adaptation, which is critical for future research and implementation in clinical practice. However, the importance of small, low 18F-FDG-avidity lesions should be addressed in a larger cohort of cHL patients.

Quantitative Radiomics Features in Diffuse Large B-Cell Lymphoma: Does Segmentation Method Matter?

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 18F-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 SUVmax, 50% of SUVpeak, 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 SUVpeak 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 SUVpeak 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 SUVpeak (at least 190 at patient level and 134 for the largest lesion were uncorrelated to SUVpeak, 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.

Study of 89Zr-Pembrolizumab PET/CT in Patients With Advanced-Stage Non-Small Cell Lung Cancer.

The tumor programmed death ligand 1 (PD-L1) proportion score is the current method for selecting non-small cell lung cancer (NSCLC) patients for single-agent treatment with pembrolizumab, a programmed cell death 1 (PD-1) monoclonal antibody. However, not all patients respond to therapy. Better understanding of in vivo drug behavior may help in the selection of patients who will benefit the most. Methods: NSCLC patients eligible for pembrolizumab monotherapy as first- or later-line therapy were enrolled. Patients received 2 injections of 89Zr-pembrolizumab, 1 without a preceding dose of pembrolizumab and 1 with a preceding dose of 200 mg of pembrolizumab, directly before tracer injection. Up to 4 PET/CT scans were obtained after tracer injection. After imaging acquisition, patients were treated with 200 mg of pembrolizumab every 3 wk. Tumor uptake and tracer biodistribution were visually assessed and quantified as the SUV. Tumor tracer uptake was correlated with PD-1 and PD-L1 expression and response to pembrolizumab treatment. Results: Twelve NSCLC patients were included. One patient experienced grade 3 myalgia after tracer injection. 89Zr-pembrolizumab was observed in the blood pool, liver, and spleen. Tracer uptake was visualized in 47.2% of 72 tumor lesions measuring ΒΧΡ20 mm in the long-axis diameter, and substantial uptake heterogeneity was observed within and between patients. Uptake was higher in patients with a response to pembrolizumab treatment (n = 3) than in patients without a response (n = 9), although this finding was not statistically significant (median SUVpeak, 11.4 vs. 5.7; P = 0.066). No significant correlations were found with PD-L1 or PD-1 immunohistochemistry. Conclusion:89Zr-pembrolizumab injection was safe, with only 1 grade 3 adverse event-possibly immune-related-in 12 patients. 89Zr-pembrolizumab tumor uptake was higher in patients with a response to pembrolizumab treatment but did not correlate with PD-L1 or PD-1 immunohistochemistry.

18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma.

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.

PD-L1 PET/CT Imaging with Radiolabeled Durvalumab in Patients with Advanced-Stage Non-Small Cell Lung Cancer.

Better biomarkers are needed to predict treatment outcome in non-small cell lung cancer (NSCLC) patients treated with anti-programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) checkpoint inhibitors. PD-L1 immunohistochemistry has limited predictive value, possibly because of tumor heterogeneity of PD-L1 expression. Noninvasive PD-L1 imaging using 89Zr-durvalumab might better reflect tumor PD-L1 expression. Methods: NSCLC patients eligible for second-line immunotherapy were enrolled. Patients received 2 injections of 89Zr-durvalumab: one without a preceding dose of unlabeled durvalumab (tracer dose only) and one with a preceding dose of 750 mg of durvalumab, directly before tracer injection. Up to 4 PET/CT scans were obtained after tracer injection. After imaging acquisition, patients were treated with 750 mg of durvalumab every 2 wk. Tracer biodistribution and tumor uptake were visually assessed and quantified as SUV, and both imaging acquisitions were compared. Tumor tracer uptake was correlated with PD-L1 expression and clinical outcome, defined as response to durvalumab treatment. Results: Thirteen patients were included, and 10 completed all scheduled PET scans. No tracer-related adverse events were observed, and all patients started durvalumab treatment. Biodistribution analysis showed 89Zr-durvalumab accumulation in the blood pool, liver, and spleen. Serial imaging showed that image acquisition 120 h after injection delivered the best tumor-to-blood pool ratio. Most tumor lesions were visualized with the tracer dose only versus the coinjection imaging acquisition (25% vs. 13.5% of all lesions). Uptake heterogeneity was observed within (SUVpeak range, 0.2-15.1) and between patients. Tumor uptake was higher in patients with treatment response or stable disease than in patients with disease progression according to RECIST 1.1. However, this difference was not statistically significant (median SUVpeak, 4.9 vs. 2.4; P = 0.06). SUVpeak correlated better with the combined tumor and immune cell PD-L1 score than with PD-L1 expression on tumor cells, although neither was statistically significant (P = 0.06 and P = 0.93, respectively). Conclusion:89Zr-durvalumab was safe, without any tracer-related adverse events, and more tumor lesions were visualized using the tracer dose-only imaging acquisition. 89Zr-durvalumab tumor uptake was higher in patients with a response to durvalumab treatment but did not correlate with tumor PD-L1 immunohistochemistry.

18F-FDG PET Improves Baseline Clinical Predictors of Response in Diffuse Large B-Cell Lymphoma: The HOVON-84 Study.

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 SUVmax [ΔSUVmax] 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, ΔSUVmax (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 ΔSUVmax 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 ΔSUVmax (4.8% and 53%, respectively) than for DS (3.1% and 38%, respectively). aaIPI and ΔSUVmax 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 ΔSUVmax of more than 70% (37% of patients) yielded an NPV of 93%, and the combination of high-intermediate or high aaIPI and a ΔSUVmax 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 ΔSUVmax outperformed DS in 2-y PFS prediction.

Diagnostic Performance of [18F]FDG PET in Staging Grade 1-2, Estrogen Receptor Positive Breast Cancer.

Positron emission tomography using [18F]fluorodeoxyglucose (FDG PET) potentially underperforms for staging of patients with grade 1-2 estrogen receptor positive (ER+) breast cancer. The aim of this study was to retrospectively investigate the diagnostic accuracy of FDG PET in this patient population. Suspect tumor lesions detected on conventional imaging and FDG PET were confirmed with pathology or follow up. PET-positive lesions were (semi)quantified with standardized uptake values (SUV) and these were correlated with various pathological features, including the histological subtype. Pre-operative imaging detected 155 pathologically verified lesions (in 74 patients). A total of 115/155 (74.2%) lesions identified on FDG PET were classified as true positive, i.e., malignant (in 67 patients) and 17/155 (10.8%) lesions as false positive, i.e., benign (in 9 patients); 7/155 (4.5%) as false negative (in 7 patients) and 16/155 (10.3%) as true negative (in 14 patients). FDG PET incorrectly staged 16/70 (22.9%) patients. The FDG uptake correlated with histological subtype, showing higher uptake in ductal carcinoma, compared to lobular carcinoma (p < 0.05). Conclusion: Within this study, FDG PET inadequately staged 22.9% of grade 1-2, ER + BC cases. Incorrect staging can lead to inappropriate treatment choices, potentially affecting survival and quality of life. Prospective studies investigating novel radiotracers are urgently needed.

Potential and pitfalls of 89Zr-immuno-PET to assess target status: 89Zr-trastuzumab as an example.

BACKGROUND: 89Zirconium-immuno-positron emission tomography (89Zr-immuno-PET) is used for assessment of target status to guide antibody-based therapy. We aim to determine the relation between antibody tumor uptake and target concentration to improve future study design and interpretation. METHODS: The relation between tumor uptake and target concentration was predicted by mathematical modeling of 89Zr-labeled antibody disposition in the tumor. Literature values for trastuzumab kinetics were used to provide an example. RESULTS: 89Zr-trastuzumab uptake initially increases with increasing target concentration, until it levels off to a constant value. This is determined by the total administered mass dose of trastuzumab. For a commonly used imaging dose of 50 mg 89Zr-trastuzumab, uptake can discriminate between immunohistochemistry score (IHC) 0 versus 1-2-3. CONCLUSION: The example for 89Zr-trastuzumab illustrates the potential to assess target expression. The pitfall of false-positive findings depends on the cut-off to define clinical target positivity (i.e., IHC 3) and the administered mass dose.

Spatial concordance of DNA methylation classification in diffuse glioma.

BACKGROUND: Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence. METHODS: We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites. RESULTS: Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account. CONCLUSION: Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists.

Interobserver Agreement on Automated Metabolic Tumor Volume Measurements of Deauville Score 4 and 5 Lesions at Interim 18F-FDG PET in Diffuse Large B-Cell Lymphoma.

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 SUVmax 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/cm3, a fixed SUV threshold of 4.0 g/cm3, an adaptive threshold corrected for source-to-local background activity contrast at 50% of the SUVpeak, 41% of SUVmax 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 SUVmax (<5, 5-10, >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 SUVmax of less than 10 and using SUV4.0 in more 18F-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 SUVmax of more than 10 were comparable after exclusion of visually failed MV3 contouring. For lesions with an SUVmax 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 18F-FDG uptake characteristics, that is, SUVmax.

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET.

BACKGROUND: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV-including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. METHODS: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test-retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. RESULTS: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68). CONCLUSION: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.