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

International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation

Total metabolic tumor volume (TMTV) is prognostic in lymphoma. However, cutoff values for risk stratification vary markedly, according to the tumor delineation method used. We aimed to create a standardized TMTV benchmark dataset allowing TMTV to be tested and applied as a reproducible biomarker. Methods: Sixty baseline 18F-FDG PET/CT scans were identified with a range of disease distributions (20 follicular, 20 Hodgkin, and 20 diffuse large B-cell lymphoma). TMTV was measured by 12 nuclear medicine experts, each analyzing 20 cases split across subtypes, with each case processed by 3-4 readers. LIFEx or ACCURATE software was chosen according to reader preference. Analysis was performed stepwise: TMTV1 with automated preselection of lesions using an SUV of at least 4 and a volume of at least 3 cm3 with single-click removal of physiologic uptake; TMTV2 with additional removal of reactive bone marrow and spleen with single clicks; TMTV3 with manual editing to remove other physiologic uptake, if required; and TMTV4 with optional addition of lesions using mouse clicks with an SUV of at least 4 (no volume threshold). Results: The final TMTV (TMTV4) ranged from 8 to 2,288 cm3, showing excellent agreement among all readers in 87% of cases (52/60) with a difference of less than 10% or less than 10 cm3 In 70% of the cases, TMTV4 equaled TMTV1, requiring no additional reader interaction. Differences in the TMTV4 were exclusively related to reader interpretation of lesion inclusion or physiologic high-uptake region removal, not to the choice of software. For 5 cases, large TMTV differences (>25%) were due to disagreement about inclusion of diffuse splenic uptake. Conclusion: The proposed segmentation method enabled highly reproducible TMTV measurements, with minimal reader interaction in 70% of the patients. The inclusion or exclusion of diffuse splenic uptake requires definition of specific criteria according to lymphoma subtype. The publicly available proposed benchmark allows comparison of study results and could serve as a reference to test improvements using other segmentation approaches.

The prognostic utility of 18F-FDG PET parameters in lymphoma patients under CAR-T-cell therapy: a systematic review and meta-analysis

BACKGROUND

Chimeric antigen receptor (CAR) T-cell therapy has attracted considerable attention since its recent endorsement by the Food and Drug Administration, as it has emerged as a promising immunotherapeutic modality within the landscape of oncology. This study explores the prognostic utility of [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG PET) in lymphoma patients undergoing CAR T-cell therapy. Through meta-analysis, pooled hazard ratio (HR) values were calculated for specific PET metrics in this context.

METHODS

PubMed, Scopus, and Ovid databases were explored to search for relevant topics. Dataset retrieval from inception until March 12, 2024, was carried out. The primary endpoints were impact of specific PET metrics on overall survival (OS) and progression-free survival (PFS) before and after treatment. Data from the studies were extracted for a meta-analysis using Stata 17.0.

RESULTS

Out of 27 studies identified for systematic review, 15 met the criteria for meta-analysis. Baseline OS analysis showed that total metabolic tumor volume (TMTV) had the highest HR of 2.66 (95% CI: 1.52-4.66), followed by Total-body total lesion glycolysis (TTLG) at 2.45 (95% CI: 0.98-6.08), and maximum standardized uptake values (SUVmax) at 1.30 (95% CI: 0.77-2.19). TMTV and TTLG were statistically significant (p < 0.0001), whereas SUVmax was not (p = 0.33). For PFS, TMTV again showed the highest HR at 2.65 (95% CI: 1.63-4.30), with TTLG at 2.35 (95% CI: 1.40-3.93), and SUVmax at 1.48 (95% CI: 1.08-2.04), all statistically significant (p ≤ 0.01). The ΔSUVmax was a significant predictor for PFS with an HR of 2.05 (95% CI: 1.13-3.69, p = 0.015).

CONCLUSION

[18F]FDG PET parameters are valuable prognostic tools for predicting outcome of lymphoma patients undergoing CAR T-cell therapy.

Technologist-Based Implementation of Total Metabolic Tumor Volume into Clinical Practice

Metabolic tumor volume (MTV) is defined as the total metabolically active tumor volume seen on ¹⁸F-FDG PET/CT examinations. Calculating MTV is often time-consuming, requiring a high degree of manual input. In this study, the MTV calculations of a board-certified nuclear radiologist were compared with those of 2 nuclear medicine technologists. As part of the technologists' educational program, after their classroom time they were trained by the radiologist for 30 min. The technologists calculated MTV within 7.5% of the radiologist's calculations in a set of patients who had diffuse large B-cell lymphoma and were undergoing initial staging ¹⁸F-FDG PET/CT. These findings suggest that nuclear medicine technologists may help accelerate implementation of MTV into clinical practice with favorable accuracy, possibly as an initial step followed by validation by the interpreting physician. The aim of this study was to explore whether efficiency is improved by integrating nuclear medicine technologists into a semiautomated workflow to calculate total MTV.

Evaluation of a convolution neural network for baseline total tumor metabolic volume on [18F]FDG PET in diffuse large B cell lymphoma

OBJECTIVES

New PET data-processing tools allow for automatic lesion selection and segmentation by a convolution neural network using artificial intelligence (AI) to obtain total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) routinely at the clinical workstation. Our objective was to evaluate an AI implemented in a new version of commercial software to verify reproducibility of results and time savings in a daily workflow.

METHODS

Using the software to obtain TMTV and TLG, two nuclear physicians applied five methods to retrospectively analyze data for 51 patients. Methods 1 and 2 were fully automated with exclusion of lesions ≤ 0.5 mL and ≤ 0.1 mL, respectively. Methods 3 and 4 were fully automated with physician review. Method 5 was semi-automated and used as reference. Time and number of clicks to complete the measurement were recorded for each method. Inter-instrument and inter-observer variation was assessed by the intra-class coefficient (ICC) and Bland-Altman plots.

RESULTS

Between methods 3 and 5, for the main user, the ICC was 0.99 for TMTV and 1.0 for TLG. Between the two users applying method 3, ICC was 0.97 for TMTV and 0.99 for TLG. Mean processing time (± standard deviation) was 20 s ± 9.0 for method 1, 178 s ± 125.7 for method 3, and 326 s ± 188.6 for method 5 (p < 0.05).

CONCLUSION

AI-enabled lesion detection software offers an automated, fast, reliable, and consistently performing tool for obtaining TMTV and TLG in a daily workflow.

KEY POINTS

• Our study shows that artificial intelligence lesion detection software is an automated, fast, reliable, and consistently performing tool for obtaining total metabolic tumor volume and total lesion glycolysis in a daily workflow.

Metabolic Imaging in B-Cell Lymphomas during CAR-T Cell Therapy

Simple Summary

Chimeric antigen receptor–engineered T cells are an innovative therapy in hematologic malignancies, especially in patients with refractory/relapsed B-cell lymphomas. Few studies have analyzed the role of [¹⁸F]FDG PET/CT in this field; this review aims to illustrate the literature data and the major findings related to [¹⁸F]FDG PET/CT use during CAR-T cell therapy in B-cell lymphomas, focusing on the prognostic value of metabolic parameters, as well as on response assessment. Furthermore, this work shows in detail the specific adverse events during CAR-T cell therapy and the role of [¹⁸F]FDG PET/CT imaging in their occurrence.

Abstract

Chimeric antigen receptor–engineered (CAR) T cells are emerging powerful therapies for patients with refractory/relapsed B-cell lymphomas. [¹⁸F]FDG PET/CT plays a key role during staging and response assessment in patients with lymphoma; however, the evidence about its utility in CAR-T therapies for lymphomas is limited. This review article aims to provide an overview of the role of PET/CT during CAR-T cell therapy in B-cell lymphomas, focusing on the prognostic value of metabolic parameters, as well as on response assessment. Data from the literature report on the use of [¹⁸F]FDG PET/CT at the baseline with two scans performed before treatment started focused on the time of decision (TD) PET/CT and time of transfusion (TT) PET/CT. Metabolic tumor burden is the most studied parameter associated with disease progression and overall survival, making us able to predict the occurrence of adverse effects. Instead, for post-therapy evaluation, 1 month (M1) PET/CT seems the preferable time slot for response assessment and in this setting, the Deauville 5-point scale (DS), volumetric analyses, SUVmax, and its variation between different time points (∆SUVmax) have been evaluated, confirming the usefulness of M1 PET/CT, especially in the case of pseudoprogression. Additionally, an emerging role of PET/CT brain scans is reported for the evaluation of neurotoxicity related to CAR-T therapies. Overall, PET/CT results to be an accurate method in all phases of CAR-T treatment, with particular interest in assessing treatment response. Moreover, PET parameters have been reported to be reliable predictors of outcome and severe toxicity.

Integration of Baseline Metabolic Parameters and Mutational Profiles Predicts Long-Term Response to First-Line Therapy in DLBCL Patients: A Post Hoc Analysis of the SAKK38/07 Study

Accurate estimation of the progression risk after first-line therapy represents an unmet clinical need in diffuse large B-cell lymphoma (DLBCL). Baseline (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) parameters, together with genetic analysis of lymphoma cells, could refine the prediction of treatment failure. We evaluated the combined impact of mutation profiling and baseline PET/CT functional parameters on the outcome of DLBCL patients treated with the R-CHOP14 regimen in the SAKK38/07 clinical trial (NCT00544219). The concomitant presence of mutated SOCS1 with wild-type CREBBP and EP300 defined a group of patients with a favorable prognosis and 2-year progression-free survival (PFS) of 100%. Using an unsupervised recursive partitioning approach, we generated a classification-tree algorithm that predicts treatment outcomes. Patients with elevated metabolic tumor volume (MTV) and high metabolic heterogeneity (MH) (15%) had the highest risk of relapse. Patients with low MTV and favorable mutational profile (9%) had the lowest risk, while the remaining patients constituted the intermediate-risk group (76%). The resulting model stratified patients among three groups with 2-year PFS of 100%, 82%, and 42%, respectively (p < 0.001).

Deep Learning Approach to Automatize TMTV Calculations Regardless of Segmentation Methodology for Major FDG-Avid Lymphomas

The total metabolic tumor volume (TMTV) is a new prognostic factor in lymphomas that could benefit from automation with deep learning convolutional neural networks (CNN). Manual TMTV segmentations of 1218 baseline 18FDG-PET/CT have been used for training. A 3D V-NET model has been trained to generate segmentations with soft dice loss. Ground truth segmentation has been generated using a combination of different thresholds (TMTVprob), applied to the manual region of interest (Otsu, relative 41% and SUV 2.5 and 4 cutoffs). In total, 407 and 405 PET/CT were used for test and validation datasets, respectively. The training was completed in 93 h. In comparison with the TMTVprob, mean dice reached 0.84 in the training set, 0.84 in the validation set and 0.76 in the test set. The median dice scores for each TMTV methodology were 0.77, 0.70 and 0.90 for 41%, 2.5 and 4 cutoff, respectively. Differences in the median TMTV between manual and predicted TMTV were 32, 147 and 5 mL. Spearman’s correlations between manual and predicted TMTV were 0.92, 0.95 and 0.98. This generic deep learning model to compute TMTV in lymphomas can drastically reduce computation time of TMTV.

Computed tomography-based skeletal segmentation for quantitative PET metrics of bone involvement in multiple myeloma

Purpose

Quantifications in nuclear medicine are occasionally limited by the lack of standardization for defining volumes of interest (VOIs) on functional images. In the present article, we propose the use of computed tomography (CT)–based skeletal segmentation to determine anatomically the VOI in order to calculate quantitative parameters of fluorine 18 fluorodeoxyglucose (¹⁸F-FDG) PET/CT images from patients with multiple myeloma.

Methods

We evaluated 101 whole-body ¹⁸F-FDG PET/CTs of 58 patients with multiple myeloma. An initial subjective visual analysis of the PET images was used to classify the bone involvement as negative/mild, moderate, or marked. Then, a fully automated CT–based segmentation of the skeleton was performed on PET images. The maximum, mean, and SD of the standardized uptake values (SUV_max, SUV_mean, and SD_SUV) were calculated for bone tissue and compared with the visual analysis.

Results

Forty-five (44.5%), 32 (31.7%), and 24 (23.8%) PET images were, respectively, classified as negative/mild, moderate, or marked bone involvement. All quantitative parameters were significantly related to the visual assessment of bone involvement. This association was stronger for the SUV_mean [odds ratio (OR): 10.52 (95% confidence interval (CI), 5.68–19.48); P < 0.0001] and for the SD_SUV [OR: 5.58 (95% CI, 3.31–9.42); P < 0.001) than for the SUV_max [OR: 1.01 (95% CI, 1.003–1.022); P = 0.003].

Conclusion

CT–based skeletal segmentation allows for automated and therefore reproducible calculation of PET quantitative parameters of bone involvement in patients with multiple myeloma. Using this method, the SUV_mean and its respective SD correlated better with the visual analysis of ¹⁸F-FDG PET images than SUVmax. Its value in staging and evaluating therapy response needs to be evaluated.

Prognostic models integrating quantitative parameters from baseline and interim positron emission computed tomography in patients with diffuse large B-cell lymphoma: post-hoc analysis from the SAKK38/07 clinical trial

Positron emission computed tomography (PET/CT) in patients with diffuse large B-cell lymphoma (DLBCL) enrolled in a prospective clinical trial were reviewed to test the impact of quantitative parameters from interim PET/CT scans on overall (OS) and progression-free (PFS) survival. We centrally reviewed baseline and interim PET/CT scans of 138 patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone given every 14 days (R-CHOP14) in the SAKK38/07 trial (ClinicalTrial.gov identifier: NCT00544219). Cutoff values for maximum standardized uptake value (SUV_max ), metabolic tumor volume (MTV), total lesion glycolysis (TLG) and metabolic heterogeneity (MH) were defined by receiver operating characteristic analysis. Responses were scored using the Deauville scale (DS). Patients with DS 5 at interim PET/CT (defined by uptake >2 times higher than in normal liver) had worse PFS (P = 0.014) and OS (P < 0.0001). A SUV_max reduction (Δ) greater than 66% was associated with longer PFS (P = 0.0027) and OS (P < 0.0001). Elevated SUV_max , MTV, TLG, and MH at interim PET/CT also identified patients with poorer outcome. At multivariable analysis, ΔSUV_max and baseline MTV appeared independent outcome predictors. A prognostic model integrating ΔSUV_max and baseline MTV discriminated three risk groups with significantly (log-rank test for trend, P < 0.0001) different PFS and OS. Moreover, the integration of MH and clinical prognostic indices could further refine the prediction of OS. PET metrics-derived prognostic models perform better than the international indices alone. Integration of baseline and interim PET metrics identified poor-risk DLBCL patients who might benefit from alternative treatments.

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.

Deep-Learning 18F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma

Total metabolic tumor volume (TMTV), calculated from ¹⁸F-FDG PET/CT baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) whose measurement requires the segmentation of all malignant foci throughout the body. No consensus currently exists regarding the most accurate approach for such segmentation. Further, all methods still require extensive manual input from an experienced reader. We examined whether an artificial intelligence–based method could estimate TMTV with a comparable prognostic value to TMTV measured by experts. Methods: Baseline ¹⁸F-FDG PET/CT scans of 301 DLBCL patients from the REMARC trial (NCT01122472) were retrospectively analyzed using a prototype software (PET Assisted Reporting System [PARS]). An automated whole-body high-uptake segmentation algorithm identified all 3-dimensional regions of interest (ROIs) with increased tracer uptake. The resulting ROIs were processed using a convolutional neural network trained on an independent cohort and classified as nonsuspicious or suspicious uptake. The PARS-based TMTV (TMTV_PARS) was estimated as the sum of the volumes of ROIs classified as suspicious uptake. The reference TMTV (TMTV_REF) was measured by 2 experienced readers using independent semiautomatic software. The TMTV_PARS was compared with the TMTV_REF in terms of prognostic value for progression-free survival (PFS) and overall survival (OS). Results: TMTV_PARS was significantly correlated with the TMTV_REF (ρ = 0.76; P < 0.001). Using PARS, an average of 24 regions per subject with increased tracer uptake was identified, and an average of 20 regions per subject was correctly identified as nonsuspicious or suspicious, yielding 85% classification accuracy, 80% sensitivity, and 88% specificity, compared with the TMTV_REF region. Both TMTV results were predictive of PFS (hazard ratio, 2.3 and 2.6 for TMTV_PARS and TMTV_REF, respectively; P < 0.001) and OS (hazard ratio, 2.8 and 3.7 for TMTV_PARS and TMTV_REF, respectively; P < 0.001). Conclusion: TMTV_PARS was consistent with that obtained by experts and displayed a significant prognostic value for PFS and OS in DLBCL patients. Classification of high-uptake regions using deep learning for rapidly discarding physiologic uptake may considerably simplify TMTV estimation, reduce observer variability, and facilitate the use of TMTV as a predictive factor in DLBCL patients.

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model

Several functional parameters from baseline (18)F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography have been proposed as promising biomarkers of treatment efficacy in diffuse large B-cell lymphoma (DLBCL). We tested their ability to predict outcome in 2 cohorts of DLBCL patients receiving conventional immunochemotherapy (rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, and prednisone [R-CHOP] regimen), either every 14 (R-CHOP14) or 21 days (R-CHOP21). Baseline PET analysis was performed in 141 patients with DLBCL treated with R-CHOP14 in the prospective SAKK38/07 study (NCT00544219) of the Swiss Group for Clinical Cancer Research (testing set). Reproducibility was examined in a validation set of 113 patients treated with R-CHOP21. In the SAKK38/07 cohort, progression-free survival (PFS) at 5 years was 83% for patients with low metabolic tumor volume (MTV) and 59% for those with high MTV (hazard ratio [HR], 3.4; 95% confidence interval [CI], 1.6-7.0; P = .0005), whereas overall survival (OS) was 91% and 64%, respectively (HR, 4.4; 95% CI, 1.9-10; P = .0001). MTV was the most powerful predictor of outcome also in the validation set. Elevated metabolic heterogeneity (MH) significantly predicted poorer outcomes in the subgroups of patients with elevated MTV. A model integrating MTV and MH identified high-risk patients with shorter PFS (testing set: HR, 5.6; 95% CI, 1.8-17; P < .0001; validation set: HR, 5.6; 95% CI, 1.7-18; P = .0002) and shorter OS (testing set: HR, 9.5; 95% CI, 1.7-52; P < .0001; validation set: HR, 7.6; 95% CI, 2.0-28; P = .0003). This finding was confirmed by an unsupervised regression tree analysis indicating that prognostic models based on MTV and MH may allow early identification of refractory patients who might benefit from treatment intensification. This trial was registered at www.clinicaltrials.gov as #NCT00544219.

Baseline PET as prognostic index in diffuse large B-cell lymphoma and grade IIIb follicular lymphoma: a retrospective study of a single-center experience

BACKGROUND

International guidelines support performing baseline positron emission tomography (PET) in lymphoma. Metabolic tumor volume (MTV) measurement has been proposed as a good measurement of disease burden. We investigated if MTV at baseline PET can be predictive of complete response (CR) to first line standard chemotherapy in diffuse large B-cell lymphoma (DLBCL) and in follicular lymphoma (FL) grade IIIb.

METHODS

We retrospectively analyzed data on 54 consecutive patients with DLBCL and FL grade IIIb treated in our institution. Dedicated software automatically estimated the SUV<inf>max</inf> of the most active lesion and the MTV of the entire lesion burden using an isocontour threshold method set at 42% (MTV42) and 28% (MTV28) of the SUV<inf>max</inf>. In addition, the ratio value (MTV28/MTV42) was calculated. Every group of lesions was evaluated separately. All patients were treated with R-CHOP-21. We performed a univariate and a multivariate logistic regression analysis to explore any possible association between PET parameters and CR.

RESULTS

At the univariate logistic regression analysis, patients with a MTV28 lower than the median value (173.1) had an odds ratio (OR) of 4 (95% CI: 0.94-16.9) of obtaining a CR in comparison to patients with a MTV 28 higher than the median value; patients with a MTV42 lower than the median value (i.e. 85.6) had an OR of 3.63 (95% CI: 0.85-15.34) of obtaining a CR in comparison to patients with a MTV 42 equal or higher than the median value. Using MTV28/MTV42 value with median as cut-off instead of MTV28, patients with a MTV28/MTV42 lower than the median value (i.e. 1.81) had an OR of 4.26 (95% CI: 0.72-25.07) and of 7.54 (95% CI: 0.70-80.91) of obtaining a CR in comparison to patients with a MTV28/MTV42 equal or higher than the median value in the two models, respectively.

CONCLUSIONS

The results of our study suggest that MTV could be a useful tool to predict response to R-CHOP in patients affected with DLBCL and FL grade IIIb and that a multi-parameters evaluation should be considered.

Prognostic role of baseline 18F-FDG PET/CT metabolic parameters in Burkitt lymphoma

PURPOSE

Burkitt's lymphoma (BL) is an aggressive lymphoma subtype with high 18F-FDG avidity at 18F-FDG-PET/CT, but no validated criteria for PET/CT in treatment evaluation or prediction of outcome in BL are available. The aim of our study was to investigate whether the metabolic baseline PET/CT parameters can predict treatment response and prognosis in BL.

MATERIALS AND METHODS

We retrospectively enrolled 65 patients who underwent baseline 18F-FDG-PET/CT, interim and end of treatment PET/CT. The PET images were analyzed visually and semi-quantitatively by measuring the maximum standardized uptake value body weight (SUVbw), the maximum standardized uptake value lean body mass (SUVlbm), the maximum standardized uptake value body surface area (SUVbsa), lesion to liver SUVmax ratio (L-L SUV R), lesion to blood-pool SUVmax ratio (L-BP SUV R), total metabolic tumor volume (tMTV) and total lesion glycolysis (TLG). Survival curves were plotted according to the Kaplan-Meier method.

RESULTS

At a median follow-up of 40 months, the median PFS and OS were 34 and 39 months. MTV and TLG were significantly higher in patients with partial response compared to complete response group at end of treatment, while no significant differences were found at interim. Other metabolic PET/CT parameters were not related to treatment response. MTV and TLG were demonstrated to be independent prognostic factors for both PFS and OS; instead SUVbw, SUVlbm, SUVbsa, L-L SUV R and L-BP SUV R were not related to outcome survival.

CONCLUSIONS

Metabolic tumour features (MTV and TLG) were significantly correlated with response to treatment and long-term outcome.