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.

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 SUV_peak and the maximal distance between the largest lesion and any other lesion (Dmax_bulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUV_peak and Dmax_bulk) 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.

Purpose: Praluzatamab ravtansine (CX-2009) is a conditionally activated Probody drug conjugate (PDC) comprising an anti-CD166 mAb conjugated to DM4, with a protease-cleavable linker and a peptide mask that limits target engagement in normal tissue and circulation. The tumor microenvironment is enriched for proteases capable of cleaving the linker, thereby releasing the mask, allowing for localized binding of CX-2009 to CD166. CX-2009 was evaluated in a phase I/II clinical trial for patients with advanced solid tumors. Patients and Methods: Eligible patients had metastatic cancer receiving ≥2 prior treatments. CX-2009 was administered at escalating doses every 3 weeks (0.25–10 mg/kg) or every 2 weeks (4–6 mg/kg). Primary objective was to determine the safety profile and recommended phase II dose (RP2D). Results: Of 99 patients enrolled, the most prevalent subtype was breast cancer (n = 45). Median number of prior therapies was 5 (range, 1–19). Dose-limiting toxicities were observed at 8 mg/kg every 3 weeks and 6 mg/kg every 2 weeks. On the basis of tolerability, the RP2D was 7 mg/kg every 3 weeks. Tumor regressions were observed at doses ≥4 mg/kg. In the hormone receptor–positive/HER2-nonamplified breast cancer subset (n = 22), 2 patients (9%) had confirmed partial responses, and 10 patients (45%) had stable disease. Imaging with zirconium-labeled CX-2009 confirmed uptake in tumor lesions and shielding of major organs. Activated, unmasked CX-2009 was measurable in 18 of 22 posttreatment biopsies. Conclusions: CD166 is a novel, ubiquitously expressed target. CX-2009 is the first conditionally activated antibody–drug conjugate to CD166 to demonstrate both translational and clinical activity in a variety of tumor types.

Introduction: 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 six segmentation methods: two fixed thresholds on SUV4.0 and SUV2.5, two relative methods of 41% of SUV_max (41max), and a contrast-corrected 50% of SUVpeak (A50P) and two combination ‘majority vote’ methods (MV2, MV3). Segmentation quality was assessed by two reviewers based on pre-defined 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 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: There is a high correlation 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 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-avid lesions should be addressed in a larger cohort of cHL patients.

The objective of this study was to externally validate the clinicalPET model developed in the HOVON-84 trial and to compare the model performance of our clinicalPET model to the international prognostic index (IPI). In total, 1195 Diffuse large B-cell lymphoma patients were included. 887 patients from 6 studies were used as external validation datasets. Primary outcomes were 2-year progression free survival (PFS) and 2-year time to progression (TTP). Metabolic tumor volume (MTV), the maximum distance between the largest lesion and another lesion (Dmaxbulk) and the peak standardized uptake value (SUVpeak) were extracted. The predictive value of the IPI and the HOVON-84 clinicalPET model (MTV, Dmaxbulk, SUVpeak, performance status and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance with the positive predictive value (PPV). Using 2-year PFS as outcome, the IPI yielded an AUC of 0.62 (range:0.51-0.65). The clinicalPET model yielded a significantly higher AUC of 0.71 (range:0.59-0.75, p<0.001). Comparable results were found using 2-year TTP. High-risk IPI patients had a 2-year PFS of 61.4%, versus 51.9% for the high-risk clinicalPET patients, with an increase in PPV from 35.5% to 49.1%, respectively. 66.4% of high-risk IPI patients were free from progression or relapse versus 55.5% for high-risk clinicalPET patients, with an increased PPV from 33.7% to 44.6%, respectively. The clinicalPET model that was developed in the HOVON-84 dataset remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.

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.