Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients

[18F]FDG PET/CT for prognosis and toxicity prediction of diffuse large B-cell lymphoma patients with chimeric antigen receptor T-cell therapy

PURPOSE

Chimeric antigen receptor (CAR) T-cell therapy has been confirmed to benefit patients with relapsed and/or refractory diffuse large B-cell lymphoma (DLBCL). It is important to provide precise and timely predictions of the efficacy and toxicity of CAR T-cell therapy. In this study, we evaluated the value of [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) combining with clinical indices and laboratory indicators in predicting outcomes and toxicity of anti-CD19 CAR T-cell therapy for DLBCL patients.

METHODS

Thirty-eight DLBCL patients who received CAR T-cell therapy and underwent [18F]FDG PET/CT within 3 months before (pre-infusion) and 1 month after CAR T-cell infusion (M1) were retrospectively reviewed and regularly followed up. Maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), clinical indices, and laboratory indicators were recorded at pre-infusion and M1 time points, and changes in these indices were calculated. Progression-free survival (PFS) and overall survival (OS) were as endpoints. Based on the multivariate Cox regression analysis, two predictive models for PFS and OS were developed and evaluated the efficiency. Pre-infusion indices were subjected to predict the grade of cytokine release syndrome (CRS) resulting from toxic reactions.

RESULTS

For survival analysis at a median follow-up time of 18.2 months, patients with values of international prognostic index (IPI), SUVmax at M1, and TLG at M1 above their optimal thresholds had a shorter PFS (median PFS: 8.1 months [IPI ≥ 2] vs. 26.2 months [IPI < 2], P = 0.025; 3.1 months [SUVmax ≥ 5.69] vs. 26.8 months [SUVmax < 5.69], P < 0.001; and 3.1 months [TLG ≥ 23.79] vs. 26.8 months [TLG < 23.79], P < 0.001). In addition, patients with values of SUVmax at M1 and ∆SUVmax% above their optimal thresholds had a shorter OS (median OS: 12.6 months [SUVmax ≥ 15.93] vs. 'not reached' [SUVmax < 15.93], P < 0.001; 32.5 months [∆SUVmax% ≥ -46.76] vs. 'not reached' [∆SUVmax% < -46.76], P = 0.012). Two novel predictive models for PFS and OS were visualized using nomogram. The calibration analysis and the decision curves demonstrated good performance of the models. Spearman's rank correlation (rs) analysis revealed that the CRS grade correlated strongly with the pre-infusion SUVmax (rs = 0.806, P < 0.001) and moderately with the pre-infusion TLG (rs = 0.534, P < 0.001). Multinomial logistic regression analysis revealed that the pre-infusion value of SUVmax correlated with the risk of developing a higher grade of CRS (P < 0.001).

CONCLUSION

In this group of DLBCL patients who underwent CAR T-cell therapy, SUVmax at M1, TLG at M1, and IPI were independent risk factors for PFS, and SUVmax at M1 and ∆SUVmax% for OS. Based on these indicators, two novel predictive models were established and verified the efficiency for evaluating PFS and OS. Moreover, pre-infusion SUVmax correlated with the severity of any subsequent CRS. We conclude that metabolic parameters measured using [18F]FDG PET/CT can identify DLBCL patients who will benefit most from CAR T-cell therapy, and the value before CAR T-cell infusion may predict its toxicity in advance.

Cerebellum/liver index on baseline 18F-FDG PET/CT to improve prognostication in post-transplant lymphoproliferative disorders: a multicenter retrospective study

BACKGROUND

Besides International Prognostic Index (IPI) score, baseline prognostic factors of post-transplant lymphoproliferative disorders (PTLD) are poorly identified due to the rarity of the disease. New indexes derived from healthy organ uptake in baseline 18F-FDG PET/CT have been studied in immunocompetent lymphoma patients. The aim of this study is to evaluate the performances of the cerebellum-to-liver uptake ratio (denoted as CLIP) as a prognostic factor for PFS and OS. This retrospective multicenter study is based on patients with PTLD included in the K-VIROGREF cohort. The previously published threshold of 3.24 was used for CLIP in these analyses.

RESULTS

A total of 97 patients was included with a majority of monomorphic diffuse large B-cell lymphoma subtype (78.3%). Both IPI score (≥ 3) and CLIP (< 3.24) were significant risk factors of PFS with corresponding hazard ratios of 2.0 (1.0-4.0) and 2.4 (1.3-4.5) respectively. For OS, CLIP was not significant and resulted in a hazard ratio of 2.6 (p = 0.059). Neither IPI score or Total Metabolic Tumor Volume reached significance for OS.

CONCLUSION

CLIP is a promising predictor of PFS and perhaps OS in PTLD. Further prospective studies are needed to confirm these results.

Prognostic value of the combination of volume, massiveness and fragmentation parameters measured on baseline FDG pet in high-burden follicular lymphoma

The prognostic value of radiomic quantitative features measured on pre-treatment 18F-FDG PET/CT was investigated in patients with follicular lymphoma (FL). We conducted a retrospective study of 126 FL patients (grade 1-3a) diagnosed between 2006 and 2020. A dozen of PET/CT-derived features were extracted via a software (Oncometer3D) from baseline 18F-FDG PET/CT images. The receiver operating characteristic (ROC) curve, Kaplan-Meier method and Cox analysis were used to assess the prognostic factors for progression of disease within 24 months (POD24) and progression-free survival at 24 months. Four different clusters were identified among the twelve PET parameters analyzed: activity, tumor burden, fragmentation-massiveness and dispersion. On ROC analyses, TMTV, the total metabolic tumor volume, had the highest AUC (0.734) followed by medPCD, the median distance between the centroid of the tumors and their periphery (AUC: 0.733). Patients with high TMTV (HR = 4.341; p < 0.001), high Tumor Volume Surface Ratio (TVSR) (HR = 3.204; p < 0.003) and high medPCD (HR = 4.507; p < 0.001) had significantly worse prognosis in both Kaplan-Meier and Cox univariate analyses. Furthermore, a synergistic effect was observed in Kaplan-Meier and Cox analyses combining these three PET/CT-derived parameters (HR = 12.562; p < 0.001). Having two or three high parameters among TMTV, TVSR and medPCD was able to predict POD24 status with a specificity of 68% and a sensitivity of 75%. TMTV, TVSR and baseline medPCD are strong prognostic factors in FL and their combination better predicts disease prognosis.

Indicators describing the tumor lesion aggregation and dissemination and their impact on the prognosis of patients with diffuse large B cell lymphoma receiving chimeric antigen receptor T cell therapy

INTRODUCTION

Chimeric antigen receptor (CAR) T cell therapy has markedly improved the prognosis of patients with diffuse large B-cell lymphoma (DLBCL). The relative positioning of tumor lesions in lymphoma varies among patients, manifesting as either aggregation (clumped together) or dissemination (spread throughout the body). Prognostic significance of factors indicating the relative positioning of tumor lesions in CAR T cell therapy remains underexplored. For aggregation, prior research proposed the tumor volume surface ratio (TVSR), linking it to prognosis in chemotherapy. Regarding dissemination, indicators such as disease stage or extranodal involvement, commonly used in clinical practice, have not demonstrated prognostic significance in CAR T cell therapy. This study aims to analyze current indicators of tumor aggregation or dissemination and introduce a novel indicator to assess the prognostic value of tumor lesions' relative positioning in DLBCL patients undergoing CAR T cell therapy.

METHODS

This retrospective study included 42 patients receiving CAR T cell therapy. Lesion image information was obtained from the last PET/CT scan prior to CAR T cell infusion, including total metabolic tumor volume, total tumor surface, diameter of lymphoma masses, and the sites of tumor lesions. We evaluated TVSR and bulky disease as descriptors of tumor aggregation. We refined existing indicators, stage III&IV and >1 site extranodal involvement, to distill a new indicator, termed 'extra stage', to better represent tumor dissemination. The study examined the prognostic significance of tumor aggregation and dissemination.

RESULTS

Our findings indicate that TVSR, while prognostically valuable in chemotherapy, lacks practical prognostic value in CAR T cell therapy. Conversely, bulky disease emerged as an optimal prognostic indicator of tumor aggregation. Both bulky disease and extra stage were associated with poor prognosis and exhibiting synergistic prognostic impact in CAR T cell therapy.

CONCLUSIONS

Overall, the relative positioning of tumor lesions significantly influences the prognosis of patients with DLBCL receiving CAR T cell therapy. The ideal scenario involves tumors with minimal dissemination and no aggregation.

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.

18F-FDG PET/CT-derived total lesion glycolysis predicts abscess formation in patients with surgically confirmed infective endocarditis: Results of a retrospective study at a tertiary center

BACKGROUND

Abnormal activity of 18F-FDG PET/CT is a major Duke criterion in the diagnostic work-up of infective prosthetic valve endocarditis (IE). We hypothesized that quantitative lesion assessment by 18F-FDG PET/CT-derived standard maximum uptake ratio (SURmax), metabolic volume (MV), and total lesion glycolysis (TLG) might be useful in distinct subgroups of IE patients (e.g. IE-related abscess formation).

METHODS

All patients (n = 27) hospitalized in our tertiary IE referral medical center from January 2014 to October 2018 with preoperatively performed 18F-FDG PET/CT and surgically confirmed IE were included into this retrospective analysis.

RESULTS

Patients with surgically confirmed abscess formation (n = 10) had significantly increased MV (by ~ fivefold) and TLG (by ~ sevenfold) as compared to patients without abscess (n = 17). Receiver operation characteristics (ROC) analyses demonstrated that TLG (calculated as MV × SURmean, i.e. TLG (SUR)) had the most favorable area under the ROC curve (0.841 [CI 0.659 to 1.000]) in predicting IE-related abscess formation. This resulted in a sensitivity of 80% and a specificity of 88% at a cut-off value of 14.14 mL for TLG (SUR).

CONCLUSION

We suggest that 18F-FDG PET/CT-derived quantitative assessment of TLG (SUR) may provide a novel diagnostic tool in predicting endocarditis-associated abscess formation.

Durvalumab and PET-Directed Chemoradiation in Locally Advanced Esophageal Adenocarcinoma: A Phase Ib/II Study

OBJECTIVE

To determine the safety and efficacy of adding the anti-PD-L1 antibody durvalumab to induction FOLFOX and preoperative chemotherapy in locally advanced esophageal adenocarcinoma.

BACKGROUND

Neoadjuvant induction FOLFOX followed by positron emission tomography (PET) directed chemoradiation has demonstrated improved survival for esophageal adenocarcinoma. There is clear benefit now for the addition of immune checkpoint inhibitors both in early and advanced stage disease. Given these results we investigated the safety and efficacy of adding durvalumab to induction FOLFOX and preoperative chemoradiotherapy.

METHODS

Patients with locally advanced resectable esophageal/gastroesophageal junction adenocarcinoma received PET-directed chemoradiation with durvalumab before esophagectomy. Patients who had R0 resections received adjuvant durvalumab 1500 mg every 4 weeks for 6 treatments. The primary endpoint of the study was pathologic complete response.

RESULTS

We enrolled 36 patients, 33 of whom completed all preoperative treatment and underwent surgery. Preoperative treatment was well tolerated, with no delays to surgery nor new safety signals. Pathologic complete response was identified in 8 [22% (1-sided 90% lower bound: 13.3%)] patients with major pathologic response in 22 [61% (1-sided 90% lower bound: 50%)] patients. Twelve and 24-month overall survival was 92% and 85%, respectively.

CONCLUSIONS

The addition of durvalumab to induction FOLFOX and PET-directed chemoradiotherapy before surgery is safe, with a high rate of pathologic response, as well as encouraging survival data.

Metabolic bulk volume predicts survival in a homogeneous cohort of stage II/III diffuse large B-cell lymphoma patients undergoing R-CHOP treatment

Purpose

Accurate risk stratification can improve lymphoma management, but current volumetric ¹⁸F-fluorodeoxyglucose (FDG) indicators require time-consuming segmentation of all lesions in the body. Herein, we investigated the prognostic values of readily obtainable metabolic bulk volume (MBV) and bulky lesion glycolysis (BLG) that measure the single largest lesion.

Methods

The study subjects were a homogeneous cohort of 242 newly diagnosed stage II or III diffuse large B-cell lymphoma (DLBCL) patients who underwent first-line R-CHOP treatment. Baseline PET/CT was retrospectively analyzed for maximum transverse diameter (MTD), total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), MBV, and BLG. Volumes were drawn using 30% SUVmax as threshold. Kaplan-Meier survival analysis and the Cox proportional hazards model assessed the ability to predict overall survival (OS) and progression-free survival (PFS).

Results

During a median follow-up period of 5.4 years (maximum of 12.7 years), events occurred in 85 patients, including progression, relapse, and death (65 deaths occurred at a median of 17.6 months). Receiver operating characteristic (ROC) analysis identified an optimal TMTV of 112 cm³, MBV of 88 cm³, TLG of 950, and BLG of 750 for discerning events. Patients with high MBV were more likely to have stage III disease; worse ECOG performance; higher IPI risk score; increased LDH; and high SUVmax, MTD, TMTV, TLG, and BLG. Kaplan-Meier survival analysis showed that high TMTV (p = 0.005 and < 0.001), MBV (both p < 0.001), TLG (p < 0.001 and 0.008), and BLG (p = 0.018 and 0.049) were associated with significantly worse OS and PFS. On Cox multivariate analysis, older age (> 60 years; HR, 2.74; 95% CI, 1.58-4.75; p < 0.001) and high MBV (HR, 2.74; 95% CI, 1.05-6.54; p = 0.023) were independent predictors of worse OS. Older age (hazard ratio [HR], 2.90; 95% CI, 1.74-4.82; p < 0.001) and high MBV (HR, 2.36; 95% CI, 1.15-6.54; p = 0.032) were also independent predictors of worse PFS. Furthermore, among subjects ≤60 years, high MBV remained the only significant independent predictor of worse OS (HR, 4.269; 95% CI, 1.03-17.76; p = 0.046) and PFS (HR, 6.047; 95% CI, 1.73-21.11; p = 0.005). Among subjects with stage III disease, only greater age (HR, 2.540; 95% CI, 1.22-5.30; p = 0.013) and high MBV (HR, 6.476; 95% CI, 1.20-31.9; p = 0.030) were significantly associated with worse OS, while greater age was the only independent predictor of worse PFS (HR, 6.145; 95% CI, 1.10-4.17; p = 0.024).

Conclusions

MBV easily obtained from the single largest lesion may provide a clinically useful FDG volumetric prognostic indicator in stage II/III DLBCL patients treated with R-CHOP.

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.

Evaluation of thresholding methods for the quantification of [68Ga]Ga-PSMA-11 PET molecular tumor volume and their effect on survival prediction in patients with advanced prostate cancer undergoing [177Lu]Lu-PSMA-617 radioligand therapy

PURPOSE

The aim of this study was to systematically evaluate the effect of thresholding algorithms used in computer vision for the quantification of prostate-specific membrane antigen positron emission tomography (PET) derived tumor volume (PSMA-TV) in patients with advanced prostate cancer. The results were validated with respect to the prognostication of overall survival in patients with advanced-stage prostate cancer.

MATERIALS AND METHODS

A total of 78 patients who underwent [177Lu]Lu-PSMA-617 radionuclide therapy from January 2018 to December 2020 were retrospectively included in this study. [68Ga]Ga-PSMA-11 PET images, acquired prior to radionuclide therapy, were used for the analysis of thresholding algorithms. All PET images were first analyzed semi-automatically using a pre-evaluated, proprietary software solution as the baseline method. Subsequently, five histogram-based thresholding methods and two local adaptive thresholding methods that are well established in computer vision were applied to quantify molecular tumor volume. The resulting whole-body molecular tumor volumes were validated with respect to the prognostication of overall patient survival as well as their statistical correlation to the baseline methods and their performance on standardized phantom scans.

RESULTS

The whole-body PSMA-TVs, quantified using different thresholding methods, demonstrate a high positive correlation with the baseline methods. We observed the highest correlation with generalized histogram thresholding (GHT) (Pearson r (r), p value (p): r = 0.977, p < 0.001) and Sauvola thresholding (r = 0.974, p < 0.001) and the lowest correlation with Multiotsu (r = 0.877, p < 0.001) and Yen thresholding methods (r = 0.878, p < 0.001). The median survival time of all patients was 9.87 months (95% CI [9.3 to 10.13]). Stratification by median whole-body PSMA-TV resulted in a median survival time from 11.8 to 13.5 months for the patient group with lower tumor burden and 6.5 to 6.6 months for the patient group with higher tumor burden. The patient group with lower tumor burden had significantly higher probability of survival (p < 0.00625) in eight out of nine thresholding methods (Fig. 2); those methods were SUVmax50 (p = 0.0038), SUV ≥3 (p = 0.0034), Multiotsu (p = 0.0015), Yen (p = 0.0015), Niblack (p = 0.001), Sauvola (p = 0.0001), Otsu (p = 0.0053), and Li thresholding (p = 0.0053).

CONCLUSION

Thresholding methods commonly used in computer vision are promising tools for the semiautomatic quantification of whole-body PSMA-TV in [68Ga]Ga-PSMA-11-PET. The proposed algorithm-driven thresholding strategy is less arbitrary and less prone to biases than thresholding with predefined values, potentially improving the application of whole-body PSMA-TV as an imaging biomarker.

Risk Factors of Progression in Low-tumor Burden Follicular Lymphoma Initially Managed by Watch and Wait in the Era of PET and Rituximab

Patients (pts) with asymptomatic low-burden follicular lymphoma (FL) are usually observed at diagnosis. Time to lymphoma treatment (TLT) initiation can however be very heterogeneous and risk factors of progression are poorly studied. Our study evaluated 201 pts with grade 1-3a low-tumor burden FL diagnosed in four French centers between 2010 and 2020 and managed by a watch and wait strategy in real-life settings. After a median follow-up of 4.8 years, the median TLT was 4.2 years (95% confidence interval, 3.1-5.5). On multivariate analysis, elevated lactate dehydrogenase (hazard ratio [HR] = 2.2; P = 0.02), more than 4 nodal areas involved (HR = 1.7; P = 0.02) and more than 1 extranodal involvement (HR = 2.7; P = 0.01) were identified as independent predictors of TLT. The median TLT was 5.8 years for pts with no risk factor, 2.4 years for 1 risk factor, and 1.3 years for >1 risk factors (P < 0.01). In a subanalysis of 75 pts staged with positron emission tomography-computed tomography (PET-CT), total metabolic tumor volume (TMTV) ≥14 cm³ and standardized Dmax (reflecting tumor dissemination) >0.32 m^-1 were also associated with shorter TLT (HR = 3.4; P = 0.004 and HR = 2.4; P = 0.007, respectively). In multivariate models combining PET-CT parameters and clinical variables, TMTV remained independent predictor of shorter TLT. These simple parameters could help to identify FL patients initially observed at higher risk of early progression. The role of PET-CT (extranodal sites and PET metrics) in low-burden FL appears promising and warrants further assessment in large cohorts.

Total Metabolic Tumor Volume on 18F-FDG PET/CT Is a Useful Prognostic Biomarker for Patients with Extensive Small-Cell Lung Cancer Undergoing First-Line Chemo-Immunotherapy

Background: We aimed to evaluate the prognostic value of imaging biomarkers on 18F-FDG PET/CT in extensive-stage small-cell lung cancer (ES-SCLC) patients undergoing first-line chemo-immunotherapy. Methods: In this multicenter and retrospective study, we considered two cohorts, depending on the type of first-line therapy: chemo-immunotherapy (CIT) versus chemotherapy alone (CT). All patients underwent baseline 18-FDG PET/CT before therapy between June 2016 and September 2021. We evaluated clinical, biological, and PET parameters, and used cutoffs from previously published studies or predictiveness curves to assess the association with progression-free survival (PFS) or overall survival (OS) with Cox prediction models. Results: Sixty-eight patients were included (CIT: CT) (36: 32 patients). The median PFS was 5.9:6.5 months, while the median OS was 12.1:9.8 months. dNLR (the derived neutrophils/(leucocytes-neutrophils) ratio) was an independent predictor of short PFS and OS in the two cohorts (p < 0.05). High total metabolic tumor volume (TMTV^high if > 241 cm³) correlated with outcomes, but only in the CIT cohort (PFS for TMTV^high in multivariable analysis: HR 2.5; 95%CI 1.1-5.9). Conclusion: Baseline 18F-FDG PET/CT using TMTV could help to predict worse outcomes for ES-SCLC patients undergoing first-line CIT. This suggests that baseline TMTV may be used to identify patients that are unlikely to benefit from CIT.

Baseline circulating tumour DNA and total metabolic tumour volume as early outcome predictors in aggressive large B-cell lymphoma. A real-world 112-patient cohort

Approximately 20%-50% of patients with large B-cell lymphoma (LBCL) experience poor outcomes. We aimed to evaluate the combined prognostic value of circulating tumour DNA (ctDNA) and total metabolic tumour volume (TMTV) in LBCL. This observational single-centre study included 112 newly diagnosed LBCL patients, receiving R-CHOP/R-CHOP-like chemotherapies. CtDNA load was calculated following next-generation sequencing of cell-free DNA (cfDNA) using a targeted 40-gene lymphopanel. TMTV was measured using a fully automated artificial intelligence-based method for lymphoma lesion segmentation. CtDNA was detected in cfDNA samples from 95 patients with a median concentration of 3.15 log haploid genome equivalents per mL. TMTV measurements were available for 102 patients. The median TMTV was 501 mL. High ctDNA load (>3.57 log hGE/mL) or high TMTV (>200 mL) were associated with shorter 1-year PFS (44% vs. 83%, p < 0.001 and 64% vs. 97%, p = 0.002, respectively). When combined, three prognostic groups were identified. The shortest PFS was observed when both TMTV and ctDNA load were high (p < 0.001). Even with a short follow up, combining ctDNA load with TMTV improved the risk stratification of patients with aggressive LBCL. In the near future, very high-risk patients could benefit from CAR T-cell therapy or bispecific antibodies as first-line treatments.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Evaluation of survival of the patients with metastatic rectal cancer by staging 18F-FDG PET/CT radiomic and volumetric parameters

OBJECTIVE

The aim of this study is to predict the prognosis in patients with metastatic rectal cancer (mRC) by obtaining a model with machine learning (ML) algorithms through volumetric and radiomic data obtained from baseline 18-Fluorine Fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images.

METHODS

Sixty-two patients with mRC who underwent 18F-FDG PET/CT imaging for staging between January 2015 and January 2021 were evaluated using LIFEx software. The volume of interest (VOI) of the primary tumor was generated and volumetric and textural features were obtained from this VOI. In addition, metabolic tumor volume (tMTV) and total lesion glycolysis (tTLG) values of tumor foci in the whole body. Clinical and radiomic data were evaluated with ML algorithms to create a model that predicts survival. Significant associations between these features and 1-year and 2-year survival were investigated.

RESULTS

Random forest algorithm was the most successful algorithm in predicting 2-year survival (AUC: 0.843, PRC: 0.822, and MCC: 0.583). The model obtained with this algorithm was able to predict 49 patients with 79.03% accuracy. While tMTV and tTLG values were successful in predicting 1-year survival (p: 0.002 and 0.007, respectively), texture characteristics from the primary tumor did not show a significant relationship with 1-year survival.

CONCLUSIONS

In addition to the important role of 18F-FDG PET/CT in staging patients with mRC, this study shows that it is possible to predict survival with ML methods, with parameters obtained using texture analysis from the primary tumor and whole body volumetric parameters.

Determinants of resistance to engineered T cell therapies targeting CD19 in large B cell lymphomas

Most relapsed/refractory large B cell lymphoma (r/rLBCL) patients receiving anti-CD19 chimeric antigen receptor (CAR19) T cells relapse. To characterize determinants of resistance, we profiled over 700 longitudinal specimens from two independent cohorts (n = 65 and n = 73) of r/rLBCL patients treated with axicabtagene ciloleucel. A method for simultaneous profiling of circulating tumor DNA (ctDNA), cell-free CAR19 (cfCAR19) retroviral fragments, and cell-free T cell receptor rearrangements (cfTCR) enabled integration of tumor and both engineered and non-engineered T cell effector-mediated factors for assessing treatment failure and predicting outcomes. Alterations in multiple classes of genes are associated with resistance, including B cell identity (PAX5 and IRF8), immune checkpoints (CD274), and those affecting the microenvironment (TMEM30A). Somatic tumor alterations affect CAR19 therapy at multiple levels, including CAR19 T cell expansion, persistence, and tumor microenvironment. Further, CAR19 T cells play a reciprocal role in shaping tumor genotype and phenotype. We envision these findings will facilitate improved chimeric antigen receptor (CAR) T cells and personalized therapeutic approaches.

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.

[18F]FDG-PET/CT volumetric parameters can predict outcome in untreated mantle cell lymphoma

Several studies have shown a strong predictive value for pretreatment [¹⁸F]FDG-PET/CT metabolic parameters in different lymphoma subtypes. However, few publications exist concerning the role of metabolic parameters in mantle cell lymphoma (MCL). We retrospectively investigated the prognostic value of baseline metabolic tumor volume (MTV) and lesion dissemination in untreated MCL. We compared it to currently used prognostic factors such as stage, mantle cell lymphoma international prognostic index (MIPI) and KI-67. We report that a higher baseline MTV is a risk factor for worse overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) in univariate analysis. In multivariate analysis, MTV was significantly associated with DSS, but not with OS and PFS. We found no correlation between lesion dissemination and outcome. The MIPI score remains the strongest predictor of outcome. These results show that MTV is an important prognostic tool and can improve patient risk stratification at staging of untreated MCL.

Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images

Accurate lymphoma segmentation in PET/CT images is important for evaluating Diffuse Large B-Cell Lymphoma (DLBCL) prognosis. As systemic multiple lymphomas, DLBCL lesions vary in number and size for different patients, which makes DLBCL labeling labor-intensive and time-consuming. To reduce the reliance on accurately labeled datasets, a weakly supervised deep learning method based on multi-scale feature similarity is proposed for automatic lymphoma segmentation. Weak labeling was performed by randomly dawning a small and salient lymphoma volume for the patient without accurate labels. A 3D V-Net is used as the backbone of the segmentation network and image features extracted in different convolutional layers are fused with the Atrous Spatial Pyramid Pooling (ASPP) module to generate multi-scale feature representations of input images. By imposing multi-scale feature consistency constraints on the predicted tumor regions as well as the labeled tumor regions, weakly labeled data can also be effectively used for network training. The cosine similarity, which has strong generalization, is exploited here to measure feature distances. The proposed method is evaluated with a PET/CT dataset of 147 lymphoma patients. Experimental results show that when using data, half of which have accurate labels and the other half have weak labels, the proposed method performed similarly to a fully supervised segmentation network and achieved an average Dice Similarity Coefficient (DSC) of 71.47%. The proposed method is able to reduce the requirement for expert annotations in deep learning-based lymphoma segmentation.

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.

18F-FDG PET/CT prognostic role in predicting response to salvage therapy in relapsed/refractory Hodgkin's lymphoma

PURPOSE

To evaluate the response predictors, both clinical and ¹⁸F-FDG PET/CT parameters, in Hodgkin's lymphoma (HL) patients diagnosed with refractory/relapsed disease who were planned to receive salvage therapy.

METHODS

In this retrospective study, all HL patients referred to our center between March 2015 and July 2021 were reviewed. Patients with refractory/relapsed disease who were candidates for salvage therapy were included. ¹⁸F-FDG PET/CT measurements at the time of diagnosis were extracted as the predictors, and the lesions' response at the end of the salvage therapy was considered the outcomes. The Kaplan-Meier method and multiple Cox regression were utilized to find the significant parameters to predict the time to reach the complete response. The statistical significance level was set at a two-sided p-value <0.05.

RESULTS

A total of 303 tumoral lesions from 64 patients were included. Regarding the factors associated with the response, B symptoms (p-value < 0.01), pathologic subtype (p-value < 0.001), and patient stage (p-value < 0.01) were the significant clinical parameters. In addition, SUVmax (p-value = 0.03), SUVmax/hepatic background SUVmax (p-value = 0.04), SUVmean (in all thresholds; 41% p-value = 0.02, 51% p-value = 0.04, 61% p-value = 0.01), and MTV (in all thresholds; 41% p-value = 0.04, 51% p-value = 0.04, 61% p-value = 0.05) were the significant parameters in the ¹⁸F-FDG PET/CT scans. At the median follow-up of 9 months, we found that pathologic subtype (p-value < 0.01), patient stage (p-value = 0.03), SUVmax (p-value = 0.02), SUVmax/hepatic background SUVmax (p-value = 0.03), SUVmean (in all thresholds; 41% p-value = 0.01, 51% p-value = 0.02, 61% p-value = 0.02), and MTV ≥ 41% (p-value = 0.02) were significant predictive factors. Multiple Cox regression showed the pathologic subtype (p-value = 0.02), SUVmax (p-value = 0.02), and MTV ≥ 41% (p-value = 0.04) were the most significant predictors.

CONCLUSION

Our study demonstrated that by knowing the histopathology of the lesions, the pre-treatment ¹⁸F-FDG PET/CT might be able to predict response after salvage therapy in the relapsed/refractory HL.

A tumor volume and performance status model to predict outcome before treatment in diffuse large B-cell lymphoma

Aggressive large B-cell lymphoma (LBCL) has variable outcomes. Current prognostic tools use factors for risk stratification that inadequately identify patients at high risk of refractory disease or relapse before initial treatment. A model associating 2 risk factors, total metabolic tumor volume (TMTV) >220 cm3 (determined by fluorine-18 fluorodeoxyglucose positron emission tomography coupled with computed tomography) and performance status (PS) ≥2, identified as prognostic in 301 older patients in the REMARC trial (#NCT01122472), was validated in 2174 patients of all ages treated in 2 clinical trials, PETAL (Positron Emission Tomography-Guided Therapy of Aggressive Non-Hodgkin Lymphomas; N = 510) and GOYA (N = 1315), and in real-world clinics (N = 349) across Europe and the United States. Three risk categories, low (no factors), intermediate (1 risk factor), and high (2 risk factors), significantly discriminated outcome in most of the series. Patients with 2 risk factors had worse outcomes than patients with no risk factors in the PETAL, GOYA, and real-world series. Patients with intermediate risk also had significantly worse outcomes than patients with no risk factors. The TMTV/Eastern Cooperative Oncology Group-PS combination outperformed the International Prognostic Index with a positive C-index for progression-free survival and overall survival in most series. The combination of high TMTV > 220 cm3 and ECOG-PS ≥ 2 is a simple clinical model to identify aggressive LBCL risk categories before treatment. This combination addresses the unmet need to better predict before treatment initiation for aggressive LBCL the patients likely to benefit the most or not at all from therapy.

Development of a Hybrid-Imaging-Based Prognostic Index for Metastasized-Melanoma Patients in Whole-Body 18F-FDG PET/CT and PET/MRI Data

Besides tremendous treatment success in advanced melanoma patients, the rapid development of oncologic treatment options comes with increasingly high costs and can cause severe life-threatening side effects. For this purpose, predictive baseline biomarkers are becoming increasingly important for risk stratification and personalized treatment planning. Thus, the aim of this pilot study was the development of a prognostic tool for the risk stratification of the treatment response and mortality based on PET/MRI and PET/CT, including a convolutional neural network (CNN) for metastasized-melanoma patients before systemic-treatment initiation. The evaluation was based on 37 patients (19 f, 62 ± 13 y/o) with unresectable metastasized melanomas who underwent whole-body 18F-FDG PET/MRI and PET/CT scans on the same day before the initiation of therapy with checkpoint inhibitors and/or BRAF/MEK inhibitors. The overall survival (OS), therapy response, metastatically involved organs, number of lesions, total lesion glycolysis, total metabolic tumor volume (TMTV), peak standardized uptake value (SULpeak), diameter (Dmlesion) and mean apparent diffusion coefficient (ADCmean) were assessed. For each marker, a Kaplan−Meier analysis and the statistical significance (Wilcoxon test, paired t-test and Bonferroni correction) were assessed. Patients were divided into high- and low-risk groups depending on the OS and treatment response. The CNN segmentation and prediction utilized multimodality imaging data for a complementary in-depth risk analysis per patient. The following parameters correlated with longer OS: a TMTV < 50 mL; no metastases in the brain, bone, liver, spleen or pleura; ≤4 affected organ regions; no metastases; a Dmlesion > 37 mm or SULpeak < 1.3; a range of the ADCmean < 600 mm2/s. However, none of the parameters correlated significantly with the stratification of the patients into the high- or low-risk groups. For the CNN, the sensitivity, specificity, PPV and accuracy were 92%, 96%, 92% and 95%, respectively. Imaging biomarkers such as the metastatic involvement of specific organs, a high tumor burden, the presence of at least one large lesion or a high range of intermetastatic diffusivity were negative predictors for the OS, but the identification of high-risk patients was not feasible with the handcrafted parameters. In contrast, the proposed CNN supplied risk stratification with high specificity and sensitivity.

High baseline total lesion glycolysis predicts early progression of disease within 24 months in patients with high-tumor-burden follicular lymphoma

Despite the introduction of rituximab-containing regimens, approximately 20% of patients with follicular lymphoma (FL) still experience progression of disease within 24 months (POD24) and have poor overall survival. Therefore, a more accurate risk assessment tool is required. We investigated the predictive value of two new volume-based parameters determined from baseline 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT), baseline total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG), in 45 patients with high-tumor-burden FL who underwent baseline PET/CT. We observed that high TMTV, high TLG, and poor initial treatment response (less than complete [metabolic] response [non-CR/CMR] at the end of induction therapy) independently predicted poor PFS. Notably, POD24-positive patients were more common in the high-TLG group than in the high-TMTV group, which suggests that TLG is a stronger predictor of outcomes than TMTV. Combining baseline TLG and initial treatment response showed that patients with both high TLG and non-CR/CMR experienced significantly poorer outcomes, with a 2 year PFS of 0% (hazard ratio 60.39, P = 0.000002). This combination had 56% sensitivity and 100% specificity for detecting patients who would experience POD24. Baseline TLG and initial treatment response can precisely identify patients at high risk of POD24.

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.

Predictive value of baseline metabolic tumor volume in early-stage favorable Hodgkin Lymphoma - Data from the prospective, multicenter phase III HD16 trial

Background

¹⁸F -fluorodeoxyglucose (FDG) positron emission tomography (PET) plays an important role in the staging and response assessment of lymphoma patients. Our aim was to explore the predictive relevance of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in patients with early stage Hodgkin lymphoma treated within the German Hodgkin Study Group HD16 trial.

Methods

¹⁸F-FDG PET/CT images were available for MTV and TLG analysis in 107 cases from the HD16 trial. We calculated MTV and TLG using three different threshold methods (SUV_4.0, SUV_41% and SUV_140%L), and then performed receiver-operating-characteristic analysis to assess the predictive impact of these parameters in predicting an adequate therapy response with PET negativity after 2 cycles of chemotherapy.

Results

All three threshold methods analyzed for MTV and TLG calculation showed a positive correlation with the PET response after 2 cycles chemotherapy. The largest area under the curve (AUC) was observed using the fixed threshold of SUV_4.0 for MTV- calculation (AUC 0.69 [95% CI 0.55–0.83]) and for TLG-calculation (AUC 0.69 [0.55–0.82]). The calculations for MTV and TLG with a relative threshold showed a lower AUC: using SUV_140%L AUCs of 0.66 [0.53–0.80] for MTV and 0.67 for TLG [0.54–0.81]) were observed, while with SUV_41% an AUC of 0.61 [0.45–0.76] for MTV, and an AUC 0.64 [0.49–0.80]) for TLG were seen.

Conclusions

MTV and TLG do have a predictive value after two cycles ABVD in early stage Hodgkin lymphoma, particularly when using the fixed threshold of SUV_4.0 for MTV and TLG calculation.

Trial registration

ClinicalTrials.gov NCT00736320.

Risk stratification for relapsed/refractory classical Hodgkin lymphoma integrating pretransplant Deauville score and residual metabolic tumor volume

Pretransplant Deauville score (DS) is an imaging biomarker used for risk stratification in relapsed/refractory classical Hodgkin lymphoma (cHL). However, the prognostic value of residual metabolic tumor volume (rMTV) in patients with DS 4-5 has been less well characterized. We retrospectively assessed 106 patients with relapsed/refractory cHL who underwent autologous stem cell transplantation. Pretransplant DS was determined as 1-3 (59%) and 4-5 (41%), with a markedly inferior event-free survival (EFS) in patients with DS 4-5 (hazard ratio [HR], 3.14; p = .002). High rMTV_41% (rMTV_high , ≥4.4 cm³ ) predicted significantly poorer EFS in patients with DS 4-5 (HR, 3.70; p = .014). In a multivariable analysis, we identified two independent factors predicting treatment failure: pretransplant DS combined with rMTV_41% and disease status (primary refractory vs. relapsed). These two factors allow to stratify patients into three groups with divergent 2-year EFS: 89% for low-risk (51%; relapsed disease and either pretransplant DS 1-3 or DS 4-5/rMTV_low ; HR 1), 65% for intermediate-risk (28%; refractory disease and either DS 1-3 or DS 4-5/rMTV_low ; HR 3.26), and 45% for high-risk (21%; DS 4-5/rMTV_high irrespective of disease status; HR 7.61) groups. Pretransplant DS/rMTV_41% combination and disease status predict the risk of post-transplant treatment failure and will guide risk-stratified approaches in relapsed/refractory cHL.

Radiomics signature from [18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma

OBJECTIVES

To investigate the prognostic value of PET radiomics feature in the prognosis of patients with primary gastrointestinal diffuse large B cell lymphoma (PGI-DLBCL) treated with R-CHOP-like regimen.

METHODS

A total of 140 PGI-DLBCL patients who underwent pre-therapy [¹⁸F] FDG PET/CT were enrolled in this retrospective analysis. PET radiomics features obtained from patients in the training cohort were subjected to three machine learning methods and Pearson's correlation test for feature selection. Support vector machine (SVM) was used to build a radiomics signature classifier associated with progression-free survival (PFS) and overall survival (OS). A multivariate Cox proportional hazards regression model was established to predict survival outcomes.

RESULTS

A total of 1421 PET radiomics features were extracted and reduced to 5 features to build a radiomics signature which was significantly associated with PFS and OS (p < 0.05). The combined model incorporating radiomics signatures, metabolic metrics, and clinical risk factors showed high C-indices in both the training (PFS: 0.825, OS: 0.834) and validation sets (PFS: 0.831, OS: 0.877). Decision curve analysis (DCA) demonstrated that the combined models achieved the most net benefit across a wider reasonable range of threshold probabilities for predicting PFS and OS.

CONCLUSION

The newly developed radiomics signatures obtained by the ensemble strategy were independent predictors of PFS and OS for PGI-DLBCL patients. Moreover, the combined model with clinical and metabolic factors was able to predict patient prognosis and may enable personalized treatment decision-making.

KEY POINTS

• Radiomics signatures generated from the optimal radiomics feature set from the [¹⁸F]FDG PET images can predict the survival of PGI-DLBCL patients. • The optimal radiomics feature set is constructed by integrating the feature selection outputs of LASSO, RF, Xgboost, and PC methods. • Combined models incorporating radiomics signatures from¹⁸F-FDG PET images, metabolic parameters, and clinical factors outperformed clinical models, and NCCN-IPI.

Deep learning-based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images

OBJECTIVES

To demonstrate the effectiveness of automatic segmentation of diffuse large B-cell lymphoma (DLBCL) in 3D FDG-PET scans using a deep learning approach and validate its value in prognosis in an external validation cohort.

METHODS

Two PET datasets were retrospectively analysed: 297 patients from a local centre for training and 117 patients from an external centre for validation. A 3D U-Net architecture was trained on patches randomly sampled within the PET images. Segmentation performance was evaluated by six metrics, including the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), sensitivity (Se), positive predictive value (PPV), Hausdorff distance 95 (HD 95), and average symmetric surface distance (ASSD). Finally, the prognostic value of predictive total metabolic tumour volume (pTMTV) was validated in real clinical applications.

RESULTS

The mean DSC, JSC, Se, PPV, HD 95, and ASSD (with standard deviation) for the validation cohort were 0.78 ± 0.25, 0.69 ± 0.26, 0.81 ± 0.27, 0.82 ± 0.25, 24.58 ± 35.18, and 4.46 ± 8.92, respectively. The mean ground truth TMTV (gtTMTV) and pTMTV were 276.6 ± 393.5 cm³ and 301.9 ± 510.5 cm³ in the validation cohort, respectively. Perfect homogeneity in the Bland-Altman analysis and a strong positive correlation in the linear regression analysis (R² linear = 0.874, p < 0.001) were demonstrated between gtTMTV and pTMTV. pTMTV (≥ 201.2 cm³) (PFS: HR = 3.097, p = 0.001; OS: HR = 6.601, p < 0.001) was shown to be an independent factor of PFS and OS.

CONCLUSIONS

The FCN model with a U-Net architecture can accurately segment lymphoma lesions and allow fully automatic assessment of TMTV on PET scans for DLBCL patients. Furthermore, pTMTV is an independent prognostic factor of survival in DLBCL patients.

KEY POINTS

•The segmentation model based on a U-Net architecture shows high performance in the segmentation of DLBCL patients on FDG-PET images. •The proposed method can provide quantitative information as a predictive TMTV for predicting the prognosis of DLBCL patients.

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.

Circulating tumor DNA in primary mediastinal large B-cell lymphoma versus classical Hodgkin lymphoma: a retrospective study

Few data exist concerning circulating tumor DNA (ctDNA) relevance in primary mediastinal B-cell lymphoma (PMBL). To explore this topic, we applied a 9-gene next-generation sequencing pipeline to samples from forty-four PMBL patients (median age 36.5 years). The primary endpoint was a similarity between paired biopsy/plasma mutational profiles. We detected at least one variant in 32 plasma samples (80%). The similarity between the biopsy and ctDNA genetic profiles for the 30 patients with paired mutated biopsy/plasma samples was greater than or equal to 80% in 19 patients (63.3%). We then compared PMBL ctDNA features with those of a cohort of Hodgkin lymphoma patients (n = 60). The top three mutated genes were SOCS1, TNFAIP3, and B2M in both lymphoma types. PMBL displayed more alterations in TNFAIP3 (71.9% vs. 46.3%, p = 0.029) and GNA13 (46.9% vs. 17.1%, p = 0.013) than cHL. Our 9-gene set may delineate tumor genotypes using ctDNA samples from both lymphoma types.

Predictive and Prognostic Role of Pre-Therapy and Interim 68Ga-DOTATOC PET/CT Parameters in Metastatic Advanced Neuroendocrine Tumor Patients Treated with PRRT

Simple Summary

Although a significant improvement has been achieved in the management of metastatic neuroendocrine tumor (NET), disease progression is observed in 20–30% of patients treated with peptide receptor radionuclide therapy (PRRT). Therefore, the early identification of patients who are at high risk of treatment failure is important to avoid futile therapy toxicities. The aim of this study was to identify biomarkers derived from baseline and interim 68Ga-DOTATOC PET/CT in patients undergoing PRRT. In 46 metastatic NET patients with available baseline and interim PET, only baseline total tumor volume (bTV) was able to discriminate responders to PRRT (partial response or stable disease) vs. non-responders. Patients with high bTV had also the worst overall survival. bTV, an imaging biomarker, integrated in the initial workup of NET patients could improve risk stratification and contribute to a tailored therapy approach.

Abstract

Peptide receptor radionuclide therapy (PRRT) is an effective therapeutic option in patients with metastatic neuroendocrine tumor (NET). However, PRRT fails in about 15–30% of cases. Identification of biomarkers predicting the response to PRRT is essential for treatment tailoring. We aimed to evaluate the predictive and prognostic role of semiquantitative and volumetric parameters obtained from the 68Ga-DOTATOC PET/CT before therapy (bPET) and after two cycles of PRRT (iPET). A total of 46 patients were included in this retrospective analysis. The primary tumor was 78% gastroenteropancreatic (GEP), 13% broncho-pulmonary and 9% of unknown origin. 35 patients (76.1%) with stable disease or partial response after PRRT were classified as responders and 11 (23.9%) as non-responders. Logistic regression analysis identified that baseline total volume (bTV) was associated with therapy outcome (OR 1.17; 95%CI 1.02–1.32; p = 0.02). No significant association with PRRT response was observed for other variables. High bTV was confirmed as the only variable independently associated with OS (HR 12.76, 95%CI 1.53–107, p = 0.01). In conclusion, high bTV is a negative predictor for PRRT response and is associated with worse OS rates. Early iPET during PRRT apparently does not provide information useful to change the management of NET patients.

Pre-treatment total metabolic tumour volumes in lymphoma: Does quantity matter?

Positron emission tomography/computed tomography (PET/CT) is used for the staging of lymphomas. Clinical information, such as Ann Arbor stage and number of involved sites, is derived from baseline staging and correlates with tumour volume. With modern imaging software, exact measures of total metabolic tumour volumes (tMTV) can be determined, in a semi- or fully-automated manner. Several technical factors, such as tumour segmentation and PET/CT technology influence tMTV and there is no consensus on a standardized uptake value (SUV) thresholding method, or how to include the volumes in the bone marrow and spleen. In diffuse large B-cell lymphoma, follicular lymphoma, peripheral T-cell lymphoma, and Hodgkin lymphoma, tMTV has been shown to predict progression-free survival and/or overall survival, after adjustments for clinical risk scores. However, most studies have used receiver operating curves to determine the optimal cut-off for tMTV and many studies did not include a training-validation approach, which led to the risk of overestimation of the independent prognostic value of tMTV. The identified cut-off values are heterogeneous, even when the same SUV thresholding method is used. Future studies should focus on testing tMTV in homogeneously-treated cohorts and seek to validate identified cut-off values externally so that a prognostic value can be documented, over and above currently used clinical surrogates for tumour volume.

Integrated clinical and genomic evaluation of guadecitabine (SGI-110) in peripheral T-cell lymphoma

Peripheral T-cell lymphoma (PTCL) is a rare, heterogenous malignancy with dismal outcomes at relapse. Hypomethylating agents (HMA) have an emerging role in PTCL, supported by shared mutations with myelodysplasia (MDS). Response rates to azacitidine in PTCL of follicular helper cell origin are promising. Guadecitabine is a decitabine analogue with efficacy in MDS. In this phase II, single-arm trial, PTCL patients received guadecitabine on days 1-5 of 28-day cycles. Primary end points were overall response rate (ORR) and safety. Translational sub-studies included cell free plasma DNA sequencing and functional genomic screening using an epigenetically-targeted CRISPR/Cas9 library to identify response predictors. Among 20 predominantly relapsed/refractory patients, the ORR was 40% (10% complete responses). Most frequent grade 3-4 adverse events were neutropenia and thrombocytopenia. At 10 months median follow-up, median progression free survival (PFS) and overall survival (OS) were 2.9 and 10.4 months respectively. RHOA^G17V mutations associated with improved PFS (median 5.47 vs. 1.35 months; Wilcoxon p = 0.02, Log-Rank p = 0.06). 4/7 patients with TP53 variants responded. Deletion of the histone methyltransferase SETD2 sensitised to HMA but TET2 deletion did not. Guadecitabine conveyed an acceptable ORR and toxicity profile; decitabine analogues may provide a backbone for future combinatorial regimens co-targeting histone methyltransferases.

The Utility of Metabolic Parameters on Baseline F-18 FDG PET/CT in Predicting Treatment Response and Survival in Paediatric and Adolescent Hodgkin Lymphoma

Lymphoma is the third most common paediatric cancer. Early detection of high-risk patients is necessary to anticipate those who require intensive therapy and follow-up. Current literature shows that residual tumor avidity on PET (Positron Emission Tomography) following chemotherapy corresponds with decreased survival. However, the value of metabolic parameters has not been adequately investigated. In this retrospective study, we aimed to evaluate the prognostic value of metabolic and other parameters in paediatric and adolescent Hodgkin lymphoma. We recorded tMTV (total Metabolic Tumor Volume), TLG (Total Lesion Glycolysis), and SUVmax (maximum Standard Uptake Value) on baseline PET, as well the presence of bone marrow or visceral involvement. HIV (human immunodeficiency virus) status and baseline biochemistry from clinical records were noted. All patients received stage-specific standard of care therapy. Response assessment on end-of-treatment PET was evaluated according to the Deauville criteria. We found that bone marrow involvement (p = 0.028), effusion (p < 0.001), and treatment response (p < 0.001) on baseline PET, as well as HIV status (p = 0.036) and baseline haemoglobin (p = 0.039), were significantly related to progression-free survival (PFS), whereas only effusion (p = 0.017) and treatment response (p = 0.050) were predictive of overall survival (OS). Only baseline tMTV predicted treatment response (p = 0.017). This confirms the value of F-18 FDG PET/CT (Fluoro-deoxy-glucose Positron Emission Tomography/Computed Tomography) in prognostication in paediatric and adolescent Hodgkin lymphoma; however, further studies are required to define the significance of metabolic parameters.

Metabolic Volume Measurements in Multiple Myeloma

Multiple myeloma (MM) accounts for 10-15% of all hematologic malignancies, as well as 20% of deaths related to hematologic malignant tumors, predominantly affecting bone and bone marrow. Positron emission tomography/computed tomography with 18F-fluorodeoxyglucose (FDG-PET/CT) is an important method to assess the tumor burden of these patients. It is often challenging to classify the extent of disease involvement in the PET scans for many of these patients because both focal and diffuse bone lesions may coexist, with varying degrees of FDG uptake. Different metrics involving volumetric parameters and texture features have been proposed to objectively assess these images. Here, we review some metabolic parameters that can be extracted from FDG-PET/CT images of MM patients, including technical aspects and predicting MM outcome impact. Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) are volumetric parameters known to be independent predictors of MM outcome. However, they have not been adopted in clinical practice due to the lack of measuring standards. CT-based segmentation allows automated, and therefore reproducible, calculation of bone metabolic metrics in patients with MM, such as maximum, mean and standard deviation of the standardized uptake values (SUV) for the entire skeleton. Intensity of bone involvement (IBI) is a new parameter that also takes advantage of this approach with promising results. Other indirect parameters obtained from FDG-PET/CT images, such as visceral adipose tissue glucose uptake and subcutaneous adipose tissue radiodensity, may also be useful to evaluate the prognosis of MM patients. Furthermore, the use and quantification of new radiotracers can address different metabolic aspects of MM and may have important prognostic implications.

Prognostic value of baseline metabolic tumour volume in advanced-stage Hodgkin's lymphoma

Our aim was to evaluate the prognostic value of initial total metabolic tumour volume (TMTV) in a population of patients with advanced-stage Hodgkin's lymphoma (HL). We retrospectively included 179 patients with stage IIb-III-IV Hodgkin's disease who received BEACOPP or ABVD as the first-line treatment. The initial TMTV was determined using a semi-automatic method for each patient. We analysed its prognostic value in terms of 5-year progression-free survival (PFS), overall survival, and positron emission tomography (PET) response after two courses of chemotherapy. Considering all the treatments and using a threshold of 217 cm³, TMTV was predictive of 5-year PFS and PET response after two courses of chemotherapy. In multivariable analysis involving TMTV, IPI score, and the first treatment received, TMTV remained a baseline prognostic factor for 5-year PFS. In the subgroup of patients treated with BEACOPP with a threshold of 331 cm³, TMTV was predictive of PET response, but not 5-year PFS (p = 0.087). The combined analysis of TMTV and PET response enabled the individualisation of a subgroup of patients (low TMTV and complete response on PET) with a very low risk of recurrence. Baseline TMTV appears to be a useful independent prognostic factor for predicting relapse in advanced-stage HL in ABVD subgroup, with a tendency of survival curves separation in BEACOPP subgroup.

Whole-body MRI radiomics model to predict relapsed/refractory Hodgkin Lymphoma: A preliminary study

PURPOSE

A strong prognostic score that enables a stratification of newly diagnosed Hodgkin Lymphoma (HL) to identify patients at high risk of refractory/relapsed disease is still needed. Our aim was to investigate the potential value of a radiomics analysis pipeline from whole-body MRI (WB-MRI) exams for clinical outcome prediction in patients with HL.

MATERIALS AND METHODS

Index lesions from baseline WB-MRIs of 40 patients (22 females; mean age 31.7 ± 11.4 years) with newly diagnosed HL treated by ABVD chemotherapy regimen were manually segmented on T1-weighted, STIR, and DWI images for texture analysis feature extraction. A machine learning approach based on the Extra Trees classifier and incorporating clinical variables, ¹⁸F-FDG-PET/CT-derived metabolic tumor volume, and WB-MRI radiomics features was tested using cross-validation to predict refractory/relapsed disease.

RESULTS

Relapsed disease was observed in 10/40 patients (25%), two of whom died due to progression of disease and graft versus host disease, while eight reached the complete remission. In total, 1403 clinical and radiomics features were extracted, of which 11 clinical variables and 171 radiomics parameters from both original and filtered images were selected. The 3 best performing Extra Trees classifier models obtained an equivalent highest mean accuracy of 0.78 and standard deviation of 0.09, with a mean AUC of 0.82 and standard deviation of 0.08.

CONCLUSIONS

Our preliminary results demonstrate that a combined machine learning and texture analysis model to predict refractory/relapsed HL on WB-MRI exams is feasible and may help in the clinical outcome prediction in HL patients.

The Influence of Underlying Disease on Rituximab Pharmacokinetics May be Explained by Target-Mediated Drug Disposition

BACKGROUND AND OBJECTIVES

Rituximab is an anti-CD20 monoclonal antibody approved in several diseases, including chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), rheumatoid arthritis (RA), and anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV). The influence of underlying disease on rituximab pharmacokinetics has never been investigated for several cancer and non-cancer diseases simultaneously. This study aimed at assessing this influence using an integrated semi-mechanistic model accounting for target-mediated elimination of rituximab.

METHODS

Rituximab concentration-time data from five studies previously published in patients with CLL, DLBCL, FL, RA, and AAV were described using a two-compartment model with irreversible binding of rituximab to its target antigen. Both underlying disease and target antigen measurements were assessed as covariates.

RESULTS

Central volume of distribution was [95% confidence interval] 1.7-fold [1.6-1.9] higher in DLBCL than in RA, FL, and CLL, and it was 1.8-fold [1.6-2.1] higher in RA, FL, and CLL than in AAV. First-order elimination rate constants were 1.8-fold [1.7-2.0] and 1.3-fold [1.2-1.5] higher in RA, DLBCL, and FL than in CLL and AAV, respectively. Baseline latent antigen level (L₀) was 54-fold [30-94], 20-fold [11-36], and 29-fold [14-64] higher in CLL, DLBCL, and FL, respectively, than in RA and AAV. In lymphoma, L₀ increased with baseline total metabolic tumor volume (p = 6.10^-7). In CLL, the second-order target-mediated elimination rate constant (k_deg) increased with baseline CD20 count on circulating B cells (CD20_cir, p = 0.0081).

CONCLUSIONS

Our results show for the first time that rituximab pharmacokinetics is strongly influenced by underlying disease and disease activity. Notably, neoplasms are associated with higher antigen amounts that result in decreased exposure to rituximab compared to inflammatory diseases. Our model might be used to estimate unbound target amounts in upcoming studies.

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

Identification of new prognostic factors in relapsed/refractory (rel/ref) diffuse large B-cell lymphoma (DLBCL) is essential for developing risk-adapted approaches. We retrospectively analyzed prognostication based on metabolic tumor volume (MTV) in rel/ref DLBCL (n = 108) before platinum-based salvage chemotherapy. Using 41% SUVmax threshold, patients achieving complete response (CR) exhibited significantly lower baseline values of MTV, compared to those achieving partial response (PR) or with progression of disease (medians MTV 16.26 versus 72.51 versus 98.11 ml, respectively). As a continuous variable, log₂(MTV) was predictive of failure to achieve CR (1-unit increase odds ratio [OR] = 1.58, p < 0.001). Log₂(MTV) significantly predicted progression-free survival (PFS) and overall survival (OS), and one-unit increase in log₂(MTV) was associated with shorter PFS (hazard ratio [HR] = 1.12, p = 0.035) and OS (HR = 1.17, p = 0.007). However, heterogeneity in the selection of post-salvage chemotherapy approaches may have affected survival. These data demonstrate the ability of presalvage MTV to discriminate responders from non-responders to platinum-based chemotherapy and predict survival.

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.

New Approaches in Characterization of Lesions Dissemination in DLBCL Patients on Baseline PET/CT

Dissemination, expressed recently by the largest Euclidian distance between lymphoma sites (SDmax), appeared a promising risk factor in DLBCL patients. We investigated alternative distance metrics to characterize the robustness of the dissemination information. In 290 patients from the REMARC trial (NCT01122472), the Euclidean (Euc), Manhattan (Man), and Tchebychev (Tch) distances between the furthest lesions, firstly based on the centroid of each lesion and then directly from the two most distant tumor voxels and the Travelling Salesman Problem distance (TSP) were calculated. For PFS, the areas under the ROC curves were between 0.63 and 0.64, and between 0.62 and 0.65 for OS. Patients with high SDmax whatever the method of calculation or high SD_TSP had a significantly poorer outcome than patients with low SDmax or SD_TSP (p < 0.001 for both PFS and OS), with significance maintained in Ann Arbor advanced-stage patients. In multivariate analysis with total metabolic tumor volume and ECOG, each distance feature had an independent prognostic value for PFS. For OS, only SDmax_Tch, SDmax_Euc _Vox, and SDmax_Man _Vox reached significance. The spread of DLBCL lesions measured by the largest distance between lymphoma sites is a strong independent prognostic factor and could be measured directly from tumor voxels, allowing its development in the area of the deep learning segmentation methods.

Prognostic Impact of 18F-FDG PET/CT in Patients With Aggressive B-Cell Lymphoma Treated With Anti-CD19 Chimeric Antigen Receptor T Cells

PURPOSE OF THE REPORT

We aimed to evaluate the role of 18F-FDG PET/CT in predicting patient outcome following chimeric antigen receptor T (CAR T) cells infusion in aggressive B-cell lymphoma.

METHODS

18F-FDG PET/CT data before leukapheresis, before CAR T-cell infusion and 1 month (M1) after CAR T-cell infusion, from 72 patients were retrospectively analyzed. SUVmax, total lesion glycolysis (TLG), metabolic tumor volume (MTV), and parameters describing tumor kinetics were calculated for each 18F-FDG PET/CT performed. The aim was to evaluate the prognostic value of 18F-FDG PET/CT metabolic parameters for predicting progression-free survival (PFS) and overall survival (OS) following CAR T-cell therapy.

RESULTS

Regarding PFS, ∆MTVpre-CAR and ∆TLGpre-CAR were found to be more discriminating compared with metabolic parameters at preinfusion. Median PFS in patients with a ∆MTVpre-CAR of less than 300% was 6.8 months (95% confidence interval [CI], 2.8 months to not reached) compared with 2.8 months (95% CI, 0.9-3.0 months) for those with a value of 300% or greater (P = 0.004). Likewise, median PFS in patients with ∆TLGpre-CAR of less than 420% was 6.8 months (95% CI, 2.8 months to not reached) compared with 2.7 months (95% CI, 1.3-3.0 months) for those with a value of 420% or greater (P = 0.0148). Regarding OS, metabolic parameters at M1 were strongly associated with subsequent outcome. SUVmax at M1 with a cutoff value of 14 was the most predictive parameter in multivariate analysis, outweighing other clinicobiological variables (P < 0.0001).

CONCLUSIONS

Disease metabolic volume kinetics before infusion of CAR T cells seems to be superior to initial tumor bulk itself for predicting PFS. For OS, SUVmax at M1 might adequately segregate patients with different prognosis.

Optimizing Workflows for Fast and Reliable Metabolic Tumor Volume Measurements in Diffuse Large B Cell Lymphoma

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.

Value of total lesion glycolysis and cell-of-origin subtypes for prognostic stratification of diffuse large B-cell lymphoma patients

Background

This study aimed to explore the added prognostic value of baseline metabolic volumetric parameters and cell of origin subtypes to the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) in nodal diffuse large B-cell lymphoma (DLBCL) patients.

Methods

A total of 184 consecutive de novo nodal DLBCL patients who underwent baseline positron emission tomography/computed tomography (PET/CT) were included in this study. Kaplan-Meier estimates were generated to evaluate the clinical, biological, and PET/CT parameters' prognostic value. The Cox proportional hazards model was performed to examine the potential independent predictors for progression-free survival (PFS) and overall survival (OS).

Results

With a median follow-up of 35 months, the 3-year PFS and OS were 65.2% and 73.0%, respectively. In univariate analysis, total lesion glycolysis (TLG), cell-of-origin subtypes, and NCCN-IPI were both PFS and OS predictors. High TLG (≥1,852), non-germinal center B (non-GCB), as well as high NCCN-IPI (≥4), were shown to be independently significantly associated with inferior PFS and OS after multivariate analysis. Based on the number of risk factors (high TLG, non-GCB, and high NCCN-IPI), a revised risk model was designed, and the participants were divided into four risk groups with very different outcomes, in which the PFS rates were 89.7%, 66.2%, 51.7%, and 26.7% (χ²=30.179, P<0.001), and OS rates were 93.1%, 73.8%, 56.7%, and 43.3%, respectively (χ²=23.649, P<0.001), respectively. Compared with the NCCN-IPI alone, the revised risk model showed a stronger ability to reveal further discrimination among subgroups, especially for participants with very unfavorable survival outcomes (PFS: χ²=9.963, P=0.002; OS: χ²=4.166, P=0.041, respectively).

Conclusions

The TLG, cell-of-origin subtypes, and NCCN-IPI are independent prognostic survival factors in DLBCL patients. Moreover, the revised risk model composed of the number of risk factors (high TLG, non-GCB, and high NCCN-IPI) can stratify patients better than the NCCN-IPI, especially for patients at high risk, which suggests its potential integration into decision making for personalized medicine.