Is there an optimal method for measuring baseline metabolic tumor volume in diffuse large B cell lymphoma?

Measuring Total Metabolic Tumor Volume from 18F-FDG PET: A Reality Check

Measuring total metabolic tumor volume (TMTV) on 18F-FDG PET/CT images in clinical practice requires a fast, reliable, and easy-to-perform multilesional segmentation workflow. We conducted a field test to derive total metabolic volumes using 5 representative baseline 18F-FDG PET/CT scans from patients with diffuse large B-cell lymphoma. The scans were transferred to 10 different sites or readers who used different commercially available software platforms to derive TMTV after a recently proposed benchmark workflow. Observed TMTVs were compared with reference values, and overall analysis times were reported. Our results show that TMTVs can be obtained with reasonable accuracy across readers and platforms (within 10% compared with reference benchmark values for most TMTVs) but that processing times can vary considerably depending on reader experience and the software platform. Our study showed that there is an urgent need to improve TMTV segmentation workflows in clinical practice, requiring closer collaboration between users and software vendors.

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.

Metabolic tumor volume from baseline [18 F]FDG PET/CT at diagnosis improves the IPI stratification in patients with diffuse large B-cell lymphoma

PURPOSE

Although several different parameters of PET/CT were reported to be predictive of survival in DLBCL, the best parameter remains to be elucidated and whether it could improve the risk stratification of IPI in patients with DLBCL.

PROCEDURES

262 DLBCL patients including in the training and validation cohort were retrospectively analyzed in this study.

RESULTS

Among different parameters, MTV was identified as the optimal prognostic parameter with a maximum area under the curve (AUC) of 0.652 ± 0.112 than TLG and SDmax (0.645 ± 0.113 and 0.600 ± 0.117, respectively). Patients with high MTV were associated with inferior PFS (p < 0.001 and p = 0.021, respectively) and OS (p < 0.001 and p < 0.001, respectively) in both the training and validation cohort. The multivariate analysis revealed that high MTV was an unfavorable factor for PFS (relative ratio [RR], 2.295; 95% confidence interval [CI], 1.457-3.615; p < 0.01) and OS (RR, 2.929; 95% CI 1.679-5.109; p < 0.01) independent of IPI.

CONCLUSIONS

Further analysis showed MTV could improve the risk stratification of IPI for both PFS and OS (p < 0.01 and p < 0.01, respectively). In conclusion, our study suggests that MTV was an optimal prognostic parameter of PET/CT for survival and it could improve the risk stratification of IPI in DLBCL, which may help to guide treatment in clinical trial.

Metabolic tumor volume predicts outcome in patients with advanced stage follicular lymphoma from the RELEVANCE trial

BACKGROUND

We investigated the prognostic value of baseline positron emission tomography (PET) parameters for patients with treatment-naïve follicular lymphoma (FL) in the phase III RELEVANCE trial, comparing the immunomodulatory combination of lenalidomide and rituximab (R2) versus R-chemotherapy (R-chemo), with both regimens followed by R maintenance therapy.

PATIENTS AND METHODS

Baseline characteristics of the entire PET-evaluable population (n = 406/1032) were well balanced between treatment arms. The maximal standard uptake value (SUVmax) and the standardized maximal distance between tow lesions (SDmax) were extracted, the standardized distance between two lesions the furthest apart, were extracted. The total metabolic tumor volume (TMTV) was computed using the 41% SUVmax method.

RESULTS

With a median follow-up of 6.5 years, the 6-year progression-free survival (PFS) was 57.8%, the median TMTV was 284 cm3, SUVmax was 11.3 and SDmax was 0.32 m-1, with no significant difference between arms. High TMTV (>510 cm3) and FLIPI were associated with an inferior PFS (P = 0.013 and P = 0.006, respectively), whereas SUVmax and SDmax were not (P = 0.08 and P = 0.12, respectively). In multivariable analysis, follicular lymphoma international prognostic index (FLIPI) and TMTV remained significantly associated with PFS (P = 0.0119 and P = 0.0379, respectively). These two adverse factors combined stratified the overall population into three risk groups: patients with no risk factors (40%), with one factor (44%), or with both (16%), with a 6-year PFS of 67.7%, 54.5%, and 41.0%, respectively. No significant interaction between treatment arms and TMTV or FLIPI (P = 0.31 or P = 0.59, respectively) was observed. The high-risk group (high TMTV and FLIPI 3-5) had a similar PFS in both arms (P = 0.45) with a median PFS of 68.4% in the R-chemo arm versus 71.4% in the R2 arm.

CONCLUSIONS

Baseline TMTV is predictive of PFS, independently of FLIPI, in patients with advanced FL even in the context of antibody maintenance.

Prediction nomogram based on 18F-FDG PET/CT and clinical parameters for patients with diffuse large B-cell lymphoma

The objective of this study was to develop a nomogram including parameters assessed by 18F-FDG PET/CT and clinical parameters for patients with diffuse large B-cell lymphoma (DLBCL) to predict progression-free survival (PFS). A total of 181 patients with pathologically diagnosed DLBCL at Sichuan Cancer Hospital and Institute from March 2015 to December 2020 were enrolled in this retrospective study. The area under the receiver operating characteristic (ROC) curve (AUC) was used to calculate the optimal cutoff values of the semiquantitative parameters (SUVmax, TLG, MTV, and Dmax) for PFS. A nomogram was constructed according to multivariate Cox proportional hazards regression. The predictive and discriminatory capacities of the nomogram were then measured using the concordance index (C-index), calibration plots, and Kaplan-Meier curves. The predictive and discriminatory capacities of the nomogram and the International Prognostic Index of the National Comprehensive Cancer Network (NCCN-IPI) were compared via the C-index and AUC. Multivariate analysis demonstrated that male gender and pretreatment Ann Arbor stage III-IV, non-GCB, elevated lactate dehydrogenase (LDH), number of extranodal organ involvement (Neo)>1, MTV≥152.8 cm3, and Dmax ≥53.9 cm were associated with unfavorable PFS (all p<0.05). The nomogram, including gender, Ann Arbor stage, pathology type, Neo, LDH levels, MTV, and Dmax, showed good prediction accuracy, with a C-index of 0.760 (95% CI: 0.727-0.793), which was higher than that of NCCN-IPI (0.710; 95% CI: 0.669-751). The calibration plots for 2-year demonstrated good consistency between the predicted and observed probabilities for survival time. We established a nomogram including MTV, Dmax, and several clinical parameters to predict the PFS of patients with DLBCL, and the nomogram showed better predictability and higher accuracy than NCCN-IPI.

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.

Predicting time to treatment in follicular lymphoma on watchful waiting using baseline metabolic tumour burden

PURPOSE

Asymptomatic patients with follicular lymphoma (FL) and a low tumour burden can be followed without initial therapy, a strategy called watchful waiting (WW). Prediction of the time to treatment (TTT) is still a challenge. We investigated the prognostic value of baseline total metabolic tumour volume (TMTV) and whole-body total lesion glycolysis (WB-TLG) to predict TTT in patients with FL on WW.

METHODS

We conducted a retrospective study of 54 patients with FL (grade 1-3a) diagnosed between June 2013 and December 2019, staged with FDG PET/CT, and managed on WW. Median age was 62 years (range 34-85), stage was advanced (III-IV) in 57%, and FLIPI score was intermediate to high (≥ 2) in 52% of the patients.

RESULTS

The median TMTV and WB-TLG were 7.1 and 43.3, respectively. With a median follow-up of 59 months, 41% of patients started immuno-chemotherapy. The optimal cut-points to identify patients with TTT within 24 months were 14 for TMTV (AUC 0.70; 95% CI 51-88) and 64 for WB-TLG (AUC 0.71; 95% CI 52-89) (p < 0.005). The probability of not having started treatment within 24 months was 87% for TMTV < 14 and 53% for TMTV ≥ 14 (p < 0.005). TMTV was independent of the FLIPI score for TTT prediction. Patients with both FLIPI ≥ 2 and TMTV ≥ 14 had only an 18% probability of not having started treatment at 36 months, while this probability was 75% in patients with TMTV < 14.

CONCLUSION

Metabolic tumour volume parameters may add information to clinical scores to better predict TTT and better stratify patients for interventional studies.

Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma

Simple Summary

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma. Even with the improvements in the treatment of DLBCL, around a quarter of patients will experience recurrence. The aim of this single centre retrospective study was to predict which patients would have recurrence within 2 years of their treatment using machine learning techniques based on radiomics extracted from the staging PET/CT images. Our study demonstrated that in our dataset of 229 patients (training data = 183, test data = 46) that a combined radiomic and clinical based model performed better than a simple model based on metabolic tumour volume, and that it had a good predictive ability which was maintained when tested on an unseen test set.

Abstract

Background: Approximately 30% of patients with diffuse large B-cell lymphoma (DLBCL) will have recurrence. The aim of this study was to develop a radiomic based model derived from baseline PET/CT to predict 2-year event free survival (2-EFS). Methods: Patients with DLBCL treated with R-CHOP chemotherapy undergoing pre-treatment PET/CT between January 2008 and January 2018 were included. The dataset was split into training and internal unseen test sets (ratio 80:20). A logistic regression model using metabolic tumour volume (MTV) and six different machine learning classifiers created from clinical and radiomic features derived from the baseline PET/CT were trained and tuned using four-fold cross validation. The model with the highest mean validation receiver operator characteristic (ROC) curve area under the curve (AUC) was tested on the unseen test set. Results: 229 DLBCL patients met the inclusion criteria with 62 (27%) having 2-EFS events. The training cohort had 183 patients with 46 patients in the unseen test cohort. The model with the highest mean validation AUC combined clinical and radiomic features in a ridge regression model with a mean validation AUC of 0.75 ± 0.06 and a test AUC of 0.73. Conclusions: Radiomics based models demonstrate promise in predicting outcomes in DLBCL patients.

Value of Primary Rectal Tumor PET/MRI in the Prediction of Synchronic Metastatic Disease

PURPOSE

To analyze the associations between positron emission tomography (PET)/magnetic resonance imaging (MRI) features for primary rectal tumors and metastases.

PROCEDURES

Between November 2016 and April 2018, 101 patients with rectal adenocarcinoma were included in this prospective study (NCT02537340) for whole-body PET/MRI for baseline staging. Two readers analyzed the PET/MRI; they assessed the semiquantitative PET features of the primary tumor and the N- and M-stages. Another reader analyzed the MRI features for locoregional staging. The reference standard for confirming metastatic disease was biopsy or imaging follow-up. Non-parametric tests were used to compare the PET/MRI features of the participants with or without metastatic disease. Binary logistic regression was used to evaluate the associations between the primary tumor PET/MRI features and metastatic disease.

RESULTS

A total of 101 consecutive participants (median age 62 years; range: 33-87 years) were included. Metastases were detected in 35.6% (36 of 101) of the participants. Among the PET/MRI features, higher tumor lesion glycolysis (352.95 vs 242.70; P = .46) and metabolic tumor volume (36.15 vs 26.20; P = .03) were more frequent in patients with than in those without metastases. Additionally, patients with metastases had a higher incidence of PET-positive (64% vs 32%; P = .009) and MRI-positive (56% vs 32%; P = .03) mesorectal lymph nodes, extramural vascular invasion (86% vs 49%; P > .001), and involvement of mesorectal fascia (64% vs 42%; P = .04); there were also differences between the mrT stages of these two groups (P = .008). No differences in the maximum standardized uptake values for the primary tumors in patients with and without metastases were observed (18.9 vs 19.1; P = .56). Multivariable logistic regression showed that extramural vascular invasion on MRI was the only significant predictor (adjusted odds ratio, 3.8 [95% CI: 1.1, 13.9]; P = .001).

CONCLUSION

PET/MRI facilitated the identification of participants with a high risk of metastatic disease, though these findings were based mainly on MRI features.

Total tumor burden in lymphoma - an evolving strong prognostic parameter

Total metabolic tumor volume (TMTV), a new parameter extracted from baseline FDG-PET/CT, has been recently proposed by several groups as a prognosticator in lymphomas before first-line treatment. TMTV, the sum of the metabolic volume of each lesion, is an index of the metabolically most active part of the tumor and highly correlates with the total tumor burden. TMTV measurement is obtained from PET images processed with different software and techniques, many being now freely available. In the various lymphoma subtypes where it has been measured, such as diffuse large B-cell lymphoma, Hodgkin lymphoma, Follicular Lymphoma, and Peripheral T-cell lymphoma, TMTV has been reported as a strong predictor of outcome (progression-free survival and overall survival) often outperforming the clinical scores, molecular predictors, and results of interim PET. Combined with these scores, TMTV improves the stratification of the populations into risk groups with different outcomes. TMTV cut-off separating the high-risk from the low-risk population impacts the outcome whatever the technique used for its measurement and an international harmonization is ongoing. TMTV is a unique and easy tool that could replace the surrogate of tumor burden included in the prognostic indexes used in lymphoma and help tailor therapy. Other parameters extracted from the baseline PET may give an information on the dissemination of this total tumor volume such as the maximum distance between the lesions. Trials based on TMTV would probably demonstrate its predictive value.

Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review

Purpose

To systematically review the literature evaluating clinical utility of imaging metrics derived from baseline fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) for prediction of progression-free (PFS) and overall survival (OS) in patients with classical Hodgkin lymphoma (HL) and diffuse large B cell lymphoma (DLBCL).

Methods

A search of MEDLINE/PubMed, Web of Science, Cochrane, Scopus and clinicaltrials.gov databases was undertaken for articles evaluating PET/CT imaging metrics as outcome predictors in HL and DLBCL. PRISMA guidelines were followed. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool.

Results

Forty-one articles were included (31 DLBCL, 10 HL). Significant predictive ability was reported in 5/20 DLBCL studies assessing SUVmax (PFS: HR 0.13–7.35, OS: HR 0.83–11.23), 17/19 assessing metabolic tumour volume (MTV) (PFS: HR 2.09–11.20, OS: HR 2.40–10.32) and 10/13 assessing total lesion glycolysis (TLG) (PFS: HR 1.078–11.21, OS: HR 2.40–4.82). Significant predictive ability was reported in 1/4 HL studies assessing SUVmax (HR not reported), 6/8 assessing MTV (PFS: HR 1.2–10.71, OS: HR 1.00–13.20) and 2/3 assessing TLG (HR not reported). There are 7/41 studies assessing the use of radiomics (4 DLBCL, 2 HL); 5/41 studies had internal validation and 2/41 included external validation. All studies had overall moderate or high risk of bias.

Conclusion

Most studies are retrospective, underpowered, heterogenous in their methodology and lack external validation of described models. Further work in protocol harmonisation, automated segmentation techniques and optimum performance cut-off is required to develop robust methodologies amenable for clinical utility.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05233-2.

Advanced-stage Hodgkin lymphoma: have effective therapy and modern imaging changed the significance of bulky disease?

The prognostic significance of bulky disease in advanced-stage Hodgkin lymphoma is an area of controversy. Early studies suggested that the presence of bulk was associated with an increased risk of disease relapse. The effect of bulk is less clear in more recent studies. The shift to response-adapted treatment regimens may obscure the prognostic significance of initially bulky disease, as patients with such disease have lower rates of complete metabolic response on early interim scans and thus are more likely to receive intensified chemotherapy. Various definitions of bulk have been used, further complicating interpretation of the available data. Advances in diagnostic imaging enable quantification of the three-dimensional lymphoma volume, which may ultimately become a new routine measure of bulky disease. This review aims to summarize the prognostic significance of bulky disease in advanced-stage HL, the influence of bulk on the choice of therapy, and the changing definition of bulk with advances in diagnostic imaging.

Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network

PURPOSE

Lymphoma lesion detection and segmentation on whole-body FDG-PET/CT are a challenging task because of the diversity of involved nodes, organs or physiological uptakes. We sought to investigate the performances of a three-dimensional (3D) convolutional neural network (CNN) to automatically segment total metabolic tumour volume (TMTV) in large datasets of patients with diffuse large B cell lymphoma (DLBCL).

METHODS

The dataset contained pre-therapy FDG-PET/CT from 733 DLBCL patients of 2 prospective LYmphoma Study Association (LYSA) trials. The first cohort (n = 639) was used for training using a 5-fold cross validation scheme. The second cohort (n = 94) was used for external validation of TMTV predictions. Ground truth masks were manually obtained after a 41% SUVmax adaptive thresholding of lymphoma lesions. A 3D U-net architecture with 2 input channels for PET and CT was trained on patches randomly sampled within PET/CTs with a summed cross entropy and Dice similarity coefficient (DSC) loss. Segmentation performance was assessed by the DSC and Jaccard coefficients. Finally, TMTV predictions were validated on the second independent cohort.

RESULTS

Mean DSC and Jaccard coefficients (± standard deviation) in the validations set were 0.73 ± 0.20 and 0.68 ± 0.21, respectively. An underestimation of mean TMTV by - 12 mL (2.8%) ± 263 was found in the validation sets of the first cohort (P = 0.27). In the second cohort, an underestimation of mean TMTV by - 116 mL (20.8%) ± 425 was statistically significant (P = 0.01).

CONCLUSION

Our CNN is a promising tool for automatic detection and segmentation of lymphoma lesions, despite slight underestimation of TMTV. The fully automatic and open-source features of this CNN will allow to increase both dissemination in routine practice and reproducibility of TMTV assessment in lymphoma patients.

Evaluation of the prognostic value of different methods of calculating the tumour metabolic volume with 18F-FDG PET/CT, in patients with diffuse large cell B-cell lymphoma

INTRODUCTION AND OBJECTIVES

Metabolic tumor volume (MTV) is a promising indicator of prognosis in diffuse large B-cell lymphoma (DLBCL). The aim of the present study is to evaluate the different methods for the calculation of the basal metabolic tumor volume with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) in the patients with DLBCL, relating each one of the volumes measured with progression-free survival (PFS) and overall survival (OS).

METHODOLOGY

This is a retrospective analytical cohort study, in which 34 patients underwent to ¹⁸F-FDG PET/CT baseline prior to treatment. We compared three SUV thresholds 2.5, SUV 40% of the maximum SUV and SUV mean hepatic uptake (PERCIST) for the calculation of MTV and total lesion glycolysis (TLG) biomarkers, relating them to the PFS and OS. The best predictive model was selected based on the Akaike's information criterion (AIC) after performing a Cox proportional hazards regression.

RESULTS

In relation to the PFS, they show statistically significant differences: MTV 2.5, TLG 2.5, MTV 40, TLG 40, MTV and TLG calculated with the PERCIST threshold. Among these, the one that has a lower AIC is MTV 2.5, so it is considered the best parameter to predict the PFS. With respect to OS, it shows statistically significant differences: MTV 2.5, VMT and TLG calculated with the PERCIST threshold. Among these three, the one with the lowest AIC is MTV 2.5, which is why it is considered the best parameter to predict OS. In addition, a higher value of MTV and total tumor glycolysis (TLG), is associated with worse PFS and OS CONCLUSION: The MTV calculated with the threshold SUV 2.5 seems to be the best parameter to predict PFS and OS in patients diagnosed with DLBCL with ¹⁸F-FDG PET/CT.