Prognostic value of presalvage metabolic tumor volume in patients with relapsed/refractory diffuse large B-cell lymphoma

Understandings 18 FDG PET radiomics and its application to lymphoma

The early identification of lymphoma patients who fail front-line treatment is crucial for optimizing disease management. Positron emission tomography, a well-established tool for staging and response evaluation in lymphoma, is typically assessed visually or semiquantitatively, leaving much of its latent information unexploited. Radiomic analysis, which employs mathematical descriptors, can enable the extraction of quantitative features from baseline images that correlate with the disease's biological characteristics. Emerging radiomic features such as metabolic tumour volume, total lesion glycolysis and markers of disease dissemination and metabolic heterogeneity are proving to be powerful prognostic biomarkers in lymphoma. Texture analysis, the most advanced area of radiomics, offers highly complex features that require further standardization and validation before being adopted as reliable biomarkers. Combining radiomic features with clinical risk factors and genomic data holds promising potential for improving clinical risk prediction. This review explores the current state of radiomic analysis, progress towards its standardization and its incorporation into clinical practice and trial designs. The integration of radiomic markers with circulating tumour DNA may provide a comprehensive approach to developing baseline and dynamic risk scores, facilitating the testing of novel treatments and advancing personalized treatment of aggressive lymphomas.

Axicabtagene ciloleucel vs standard of care in second-line large B-cell lymphoma: outcomes by metabolic tumor volume

ABSTRACT

Metabolic tumor volume (MTV) assessed using 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography, a measure of tumor burden, is a promising prognostic indicator in large B-cell lymphoma (LBCL). This exploratory analysis evaluated relationships between baseline MTV (categorized as low [median or less] vs high [greater than median]) and clinical outcomes in the phase 3 ZUMA-7 study (NCT03391466). Patients with LBCL relapsed within 12 months of or refractory to first-line chemoimmunotherapy were randomized 1:1 to axicabtagene ciloleucel (axi-cel; autologous anti-CD19 chimeric antigen receptor T-cell therapy) or standard care (2-3 cycles of chemoimmunotherapy followed by high-dose chemotherapy with autologous stem cell transplantation in patients who had a response). All P values are descriptive. Within high- and low-MTV subgroups, event-free survival (EFS) and progression-free survival (PFS) were superior with axi-cel vs standard care. EFS in patients with high MTV (vs low MTV) was numerically shorter with axi-cel and was significantly shorter with standard care. PFS was shorter in patients with high MTV vs low MTV in both the axi-cel and standard-care arms, and median MTV was lower in patients in ongoing response at data cutoff vs others. Median MTV was higher in patients treated with axi-cel who experienced grade ≥3 neurologic events or cytokine release syndrome (CRS) than in patients with grade 1/2 or no neurologic events or CRS, respectively. Baseline MTV less than or equal to median was associated with better clinical outcomes in patients receiving axi-cel or standard care for second-line LBCL. The trial was registered at www.clinicaltrials.gov as #NCT03391466.

TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis

PURPOSE

Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach.

METHODS

Our study included 1418 2-[18F]FDG PET/CT scans from four different centers. The dataset was divided into 900 scans for development/validation/testing phases and 518 for multi-center external testing. The former consisted of 450 lymphoma, lung cancer, and melanoma scans, along with 450 negative scans, while the latter consisted of lymphoma patients from different centers with diffuse large B cell, primary mediastinal large B cell, and classic Hodgkin lymphoma cases. Our approach involves resampling PET/CT images into different voxel sizes in the first step, followed by training multi-resolution 3D U-Nets on each resampled dataset using a fivefold cross-validation scheme. The models trained on different data splits were ensemble. After applying soft voting to the predicted masks, in the second step, we input the probability-averaged predictions, along with the input imaging data, into another 3D U-Net. Models were trained with semi-supervised loss. We additionally considered the effectiveness of using test time augmentation (TTA) to improve the segmentation performance after training. In addition to quantitative analysis including Dice score (DSC) and TMTV comparisons, the qualitative evaluation was also conducted by nuclear medicine physicians.

RESULTS

Our cascaded soft-voting guided approach resulted in performance with an average DSC of 0.68 ± 0.12 for the internal test data from developmental dataset, and an average DSC of 0.66 ± 0.18 on the multi-site external data (n = 518), significantly outperforming (p < 0.001) state-of-the-art (SOTA) approaches including nnU-Net and SWIN UNETR. While TTA yielded enhanced performance gains for some of the comparator methods, its impact on our cascaded approach was found to be negligible (DSC: 0.66 ± 0.16). Our approach reliably quantified TMTV, with a correlation of 0.89 with the ground truth (p < 0.001). Furthermore, in terms of visual assessment, concordance between quantitative evaluations and clinician feedback was observed in the majority of cases. The average relative error (ARE) and the absolute error (AE) in TMTV prediction on external multi-centric dataset were ARE = 0.43 ± 0.54 and AE = 157.32 ± 378.12 (mL) for all the external test data (n = 518), and ARE = 0.30 ± 0.22 and AE = 82.05 ± 99.78 (mL) when the 10% outliers (n = 53) were excluded.

CONCLUSION

TMTV-Net demonstrates strong performance and generalizability in TMTV segmentation across multi-site external datasets, encompassing various lymphoma subtypes. A negligible reduction of 2% in overall performance during testing on external data highlights robust model generalizability across different centers and cancer types, likely attributable to its training with resampled inputs. Our model is publicly available, allowing easy multi-site evaluation and generalizability analysis on datasets from different institutions.

A Systematic Literature Review of Health-Related Quality of Life Outcomes and Associated Utility Values in Relapsed and/or Refractory Large B Cell Lymphoma

BACKGROUND

In this ever-expanding treatment landscape, there is a lack of consolidated health-related quality of life (HRQOL) outcomes and utility reports in relapsed or refractory (R/R) large B cell lymphoma (LBCL) to inform health care policy and decision-maker assessments for both old and new products. These assessments can have a direct effect on what treatment options are available to patients and physicians.

OBJECTIVE

A systematic literature review (SLR) was performed to understand the HRQOL evidence for treatments in R/R LBCL and identify associated health utility values.

METHODS

The SLR searched and screened literature published from 1 January 2003 to 2 May 2022. Studies were screened based on Population, Intervention, Comparator, Outcome, Study design criteria established a priori and were assessed by two independent reviewers; quality assessments of the evidence were performed in accordance with health technology assessment recommendations from the National Institute for Health and Care Excellence. Several types of therapies were included, such as chimeric antigen receptor (CAR) T cell products (lisocabtagene maraleucel, axicabtagene ciloleucel, tisagenlecleucel), novel therapies (selinexor, nivolumab, polatuzumab vedotin, and bendamustine), salvage therapies, and rituximab.

RESULTS

The review identified 33 unique studies reporting HRQOL, including 15 economic studies that reported health state utility values, 9 clinical trials, 7 health technology assessment reports, and 1 each of a vignette-based study and a point-in-time survey. Improvements in general and/or lymphoma-specific HRQOL measures were observed with CAR T cell therapy in both the second-line and third-line or later settings. On-treatment utility values for CAR T cell therapies ranged from 0.50 to 0.74. Values for remission/progression-free survival (0.70-0.90) and for disease progression (0.39-0.59) were similar across studies. For novel therapies, utility values were 0.83 for progression-free survival and ranged from 0.39 to 0.71 for disease progression. On-treatment utility values for salvage chemotherapy ranged from 0.63 to 0.67.

CONCLUSIONS

Overall, the evidence synthesized in this SLR provides a comprehensive understanding of the HRQOL evidence in R/R LBCL. This article identified several sources for utility values in the published literature showing variation in the HRQOL outcomes for patients across a variety of therapeutics. Treatment of R/R LBCL with CAR T cell therapies was associated with improvement in health utility values. Mixed results were found for novel therapies and salvage therapies. More data are needed as new therapies are used in this patient population to inform treatment decision-making.

PET/CT Biomarkers Enable Risk Stratification of Patients with Relapsed/Refractory Diffuse Large B-cell Lymphoma Enrolled in the LOTIS-2 Clinical Trial

PURPOSE

Significant progress has occurred in developing quantitative PET/CT biomarkers in diffuse large B-cell lymphoma (DLBCL). Total metabolic tumor volume (MTV) is the most extensively studied, enabling assessment of FDG-avid tumor burden associated with outcomes. However, prior studies evaluated the outcome of cytotoxic chemotherapy or chimeric antigen receptor T-cell therapy without data on recently approved FDA agents. Therefore, we aimed to assess the prognosis of PET/CT biomarkers in patients treated with loncastuximab tesirine.

EXPERIMENTAL DESIGN

We centrally reviewed screening PET/CT scans of patients with relapsed/refractory DLBCL enrolled in the LOTIS-2 (NCT03589469) study. MTV was obtained by computing individual volumes using the SUV ≥4.0 threshold. Other PET/CT metrics, clinical factors, and the International Metabolic Prognostic Index (IMPI) were evaluated. Logistic regression was used to assess the association between biomarkers and treatment response. Cox regression was used to determine the effect of biomarkers on time-to-event outcomes. We estimated biomarker prediction as continuous and binary variables defined by cutoff points.

RESULTS

Across 138 patients included in this study, MTV with a cutoff point of 96 mL was the biomarker associated with the highest predictive performance in univariable and multivariable models to predict failure to achieve complete metabolic response (OR, 5.42; P = 0.002), progression-free survival (HR, 2.68; P = 0.002), and overall survival (HR, 3.09; P < 0.0001). IMPI demonstrated an appropriate performance, however, not better than MTV alone.

CONCLUSIONS

Pretreatment MTV demonstrated robust risk stratification, with those patients demonstrating high MTV achieving lower responses and survival to loncastuximab tesirine in relapsed/refractory DLBCL.

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

Advances in positron emission tomography and radiomics

Positron emission tomography is established for staging and response evaluation in lymphoma using visual evaluation and semi-quantitative analysis. Radiomic analysis involving quantitative imaging features at baseline, such as metabolic tumor volume and markers of disease dissemination and changes in the standardized uptake value during treatment are emerging as powerful biomarkers. The combination of radiomic features with clinical risk factors and genomic analysis offers the potential to improve clinical risk prediction. This review discusses the state of current knowledge, progress toward standardization of tumor delineation for radiomic analysis and argues that radiomic features, molecular markers and circulating tumor DNA should be included in clinical trial designs to enable the development of baseline and dynamic risk scores that could further advance the field to facilitate testing of novel treatments and personalized therapy in aggressive lymphomas.

Are dynamic or fixed FDG-PET measures of disease of greater prognostic value in patients with relapsed/refractory diffuse large B-cell lymphoma undergoing autologous haematopoietic stem cell transplantation?

Positron emission tomography (PET) response assessment using the Deauville score has prognostic utility in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) undergoing autologous stem-cell transplantation (ASCT). Improved predictive methods are required to identify patients with poor outcomes who may be better considered for other salvage options. We investigated the prognostic value of mean tumour volume (MTV) and maximum standardised uptake value (SUVmax) at pre-salvage and pre-ASCT time-points, and the quantitative changes between scans (∆MTV and ∆SUVmax). One hundred and twenty-five patients with R/R DLBCL underwent salvage immunochemotherapy and ASCT: 80 patients had pre-salvage PET and 90 had pre-ASCT PET available. With a median follow-up of 5.6 years, 5-year progression-free survival (PFS) and overall survival (OS) were 52% and 65%, respectively. For patients with PET-positive residual disease after salvage therapy, pre-ASCT MTV was a significant negative prognosticator for PFS (HR 1.19 per 100 ml, p < 0.001) and OS (HR 1.78 per 100 ml, p < 0.001). Similarly, pre-ASCT SUVmax was negatively associated with PFS (HR 1.08, p < 0.001) and OS (HR 1.08, p < 0.001). Notably, pre-salvage MTV and SUVmax and ∆MTV and ∆SUVmax were not associated with PFS or OS. In conclusion, pre-ASCT MTV and SUVmax appear to be of greater predictive value than the degree of response. Potential application may exist for PET-directed management of R/R DLBCL patients.

A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma

Metabolic tumor volume (MTV) is a robust prognostic biomarker in diffuse large B-cell lymphoma (DLBCL). The available semiautomatic software for calculating MTV requires manual input limiting its routine application in clinical research. Our objective was to develop a fully automated method (AM) for calculating MTV and to validate the method by comparing its results with those from two nuclear medicine (NM) readers. The automated method designed for this study employed a deep convolutional neural network to segment normal physiologic structures from the computed tomography (CT) scans that demonstrate intense avidity on positron emission tomography (PET) scans. The study cohort consisted of 100 patients with newly diagnosed DLBCL who were randomly selected from the Alliance/CALGB 50,303 (NCT00118209) trial. We observed high concordance in MTV calculations between the AM and readers with Pearson's correlation coefficients and interclass correlations comparing reader 1 to AM of 0.9814 (p < 0.0001) and 0.98 (p < 0.001; 95%CI = 0.96 to 0.99), respectively; and comparing reader 2 to AM of 0.9818 (p < 0.0001) and 0.98 (p < 0.0001; 95%CI = 0.96 to 0.99), respectively. The Bland-Altman plots showed only relatively small systematic errors between the proposed method and readers for both MTV and maximum standardized uptake value (SUVmax). This approach may possess the potential to integrate PET-based biomarkers in clinical trials.

A PET in a time of need: toward early PET-adapted therapy in DLBCL in first relapse

Salvage chemotherapy and autologous stem cell transplant remain a standard of care in the management of diffuse large B cell lymphoma (DLBCL) at first relapse. However, this paradigm is increasingly being challenged by novel immunotherapies, such as chimeric antigen receptor T-cells (CART-cells). Traditional positron emission tomography-based (PET) prognostication takes place after salvage and before autologous stem cell transplant (ASCT), and while useful, for many patients this information comes too late and at the expense of unnecessary toxicity. In this edition of Leukemia & Lymphoma, two groups present their findings on the use of early quantitative PET markers and the correlation with outcomes in patients embarking on second line salvage chemotherapy. These approaches have the potential to better identify patients who are destined for treatment failure and help guide appropriate sequencing of alternative therapies or the development of PET-adapted clinical trials.