Pre-treatment maximum standardized uptake value predicts outcome after frontline therapy in patients with advanced stage follicular lymphoma

Diagnosis, management and follow-up of follicular lymphoma: a consensus practice statement from the Australasian Lymphoma Alliance

Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma subtype, accounting for 15-20% of all lymphoma diagnoses. Although typically slow-growing and responsive to frontline therapies, advanced-stage FL remains incurable with current treatments and typically follows a chronic relapsing/remitting course with increasingly shorter responses to subsequent lines of therapy. Outcomes are highly variable; some patients experience prolonged first remissions that may approximate a 'functional cure'. By contrast, a significant minority of patients experience disease progression shortly after frontline treatment resulting in high rates of lymphoma-related mortality. Reflecting on the heterogeneous natural history of FL, clinical practice varies widely, particularly in controversial areas, including appropriate disease staging, selection of management strategies and duration of clinical follow-up. This position statement presents an evidence-based synthesis of the literature for application in Australasian practice.

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.

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.

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

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

Duodenal-type follicular lymphoma in 18F-FDG PET/CT imaging: a case report

Follicular lymphoma (FL) is a subtype of non-Hodgkin lymphoma (NHL) that is typically characterized by a slow-growing course. Duodenal-type follicular lymphoma (D-FL) was recently reclassified as a distinct variant. This subtype exhibits unique clinical and biological characteristics, which set it apart from other forms of FL. We report a case of a 36-year-old male patient with multiple, small, gray polypoid lesions in the descending duodenum which were detected by esophagogastroduodenoscopy. The pathological diagnosis was low-grade D-FL. 18F-FDG PET/CT was performed for staging and revealed the pancreas and peripheral lymph nodes were involved by FL, with a clinical IV stage. The patient underwent a bone marrow smear cytology, which revealed no bone marrow abnormalities, and excluded bone marrow involvement. He was treated with six cycles of chemotherapy using the R-CHOP regimen and reached complete remission.

Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: application to chemotherapy response in lymphoma

BACKGROUND

To investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [¹⁸F]FDG PET metabolic uptake rate K_i parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients.

METHODS

We analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP K_i parametric maps. Spearman's rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets.

RESULTS

Via Spearman's rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann-Whitney test).

CONCLUSIONS

Our results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.

Personalised therapy in follicular lymphoma - is the dial turning?

Follicular lymphoma is the most common indolent lymphoma accounting for approximately 20%-25% of all new non-Hodgkin lymphoma diagnoses in western countries. Whilst outcomes are mostly favorable, the spectrum of clinical phenotypes includes high-risk groups with significantly inferior outcomes. This review discusses recent updates in risk stratification and treatment approaches from upfront treatment for limited and advanced stage follicular lymphoma to the growing options for relapsed, refractory disease with perspectives on how to approach this from a personalized lens. Notable gaps remain on how one can precisely and prospectively select optimal treatment for patients based on varying risks, with an anticipation that an increased understanding of the biology of these different phenotypes and increasing refinement of imaging- and biomarker-based tools will, in time, allow these gaps to be closed.

REFLECT: prospective multicenter non-interventional study evaluating the effectiveness and safety of Sandoz rituximab (SDZ-RTX; Rixathon®) in combination with CHOP for the treatment of patients with previously untreated CD20-positive diffuse large B-cell lymphoma

Background

REFLECT is the first prospective study of Sandoz biosimilar rituximab (SDZ-RTX) in patients with diffuse large B-cell lymphoma (DLBCL).

Objective

To evaluate the 2-year effectiveness and safety of SDZ-RTX as first-line treatment for DLBCL.

Design

Real-world, multicenter, open-label, single-arm, non-interventional, post-approval study of SDZ-RTX in combination with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients with treatment-naïve CD20‑positive DLBCL.

Methods

Treatment-naïve, CD20-positive adult patients (⩾18 years) with DLBCL eligible for therapy with R-CHOP were treated with SDZ-RTX-CHOP every 2 or 3 weeks for 6-8 cycles. The effectiveness of SDZ-RTX was measured by the complete response (CR) rate at the end of R-CHOP treatment, as assessed by the treating physician. Progression-free survival (PFS) was assessed at 24 months.

Results

A total of 169 patients [52.1% female, median (range) age 70 (24-94) years] with DLBCL were included in the full analysis set. At baseline, 19.5% and 24.3% of patients had Ann Arbor disease stage III or IV, respectively, and most patients (80.5%) had Eastern Cooperative Oncology Group Performance Status of 0 or 1. A total of 100 (59.2%) patients completed the 24-month observation period. In total, 110 [65.1%; 95% confidence interval (CI): 57.4-72.3] patients achieved CR as best response and 50 (29.6%; 95% CI: 22.8-37.1) patients achieved partial response. Overall best response rate was 94.7% (95% CI: 90.1-97.5). One-year PFS was 84.9% (95% CI: 78.2-89.6), while 2-year PFS was 78.5% (95% CI: 70.9-84.4); median PFS was not reached within the observational period. A total of 143 (84.6%) patients experienced ⩾1 adverse event, 53 (31.4%) of which were suspected to be related to study drug.

Conclusion

This real-world, 2-year study reconfirms that first-line treatment of CD20-positive DLBCL with R-CHOP using SDZ-RTX is effective and well tolerated.

Registration

N/A.

Ki67 proliferation index in follicular lymphoma is associated with favorable outcome in patients treated with R-CHOP

Follicular lymphoma (FL) is a common, indolent small B-cell lymphoma. While the Follicular Lymphoma International Prognostic Index is widely used, reliable prognostic and predictive biomarkers are needed. A recent study suggested that architectural patterns of CD10, BCL6, and Ki67 expression may correlate with progression-free survival (PFS) in FL patients treated with chemotherapy-free regimens. We examined the prognostic and predictive utility of architectural patterns of CD10, BCL6, Ki67, and FOXP1 in 90 patients treated with immunochemotherapy (bendamustine-rituximab [BR] and R-cyclophosphamide, doxorubicin, vincristine, prednisone [CHOP]). We found that high follicular Ki67 (≥30%) was associated with longer PFS in the subgroup of patients treated with R-CHOP but not among those treated with BR. Validation of this biomarker may support routine use of Ki67 as a predictive marker in FL.

Diagnostic and prognostic value of pretreatment PET/CT in extranodal natural killer/T-cell lymphoma: a retrospective multicenter study

PURPOSE

The objective of this research was to assess the utility of positron emission tomography combined with computed tomography (PET/CT) to detect bone marrow invasion (BMI) and the predictive value of PET/CT in extranodal natural killer/T-cell lymphoma (ENKTL) patients.

PATIENTS AND METHODS

This multicentre study enrolled ENKTL patients who underwent pretherapy PET/CT and bone marrow biopsy (BMB). The specificity, sensitivity, negative predictive value (NPV), and positive predictive value (PPV) of PET/CT and BMB for BMI were evaluated. Multivariate analysis was used to identify predictive parameters for constructing a nomogram.

RESULTS

Seven hundred and forty-eight patients were identified from four hospitals, with eighty (10.7%) having focal skeletal lesions on PET/CT and fifty (6.7%) having positive BMB. When BMB is considered as the gold standard, the specificity, sensitivity, PPV, and NPV of PET/CT for diagnosing BMI were found to be 93.8%, 74.0%, 46.3%, and 98.1%, respectively. PET/CT-positive individuals showed significantly worse OS than PET/CT-negative patients in the subgroup of BMB-negative cases. The nomogram model created according to the significant risk factors from multivariate analysis performed well in predicting survival probability.

CONCLUSION

PET/CT offers a superior degree of precision for determining BMI in ENKTL. A nomogram model including the parameters of PET/CT can predict survival probability and may help in applying appropriate personalized therapy.

Investigating and modeling positron emission tomography factors associated with large cell transformation from low-grade lymphomas

Low-grade lymphomas have a 1%-3% annual risk of transformation to a high-grade histology, and prognostic factors remain undefined. We set to investigate the role of positron emission tomography (PET) metrics in identification of transformation in a retrospective case-control series of patients matched by histology and follow-up time. We measured PET parameters including maximum standard uptake value (SUV-max) and total lesion glycolysis (TLG), and developed a PET feature and lactate dehydrogenase (LDH)-based model to identify transformation status within discovery and validation cohorts. For our discovery cohort, we identified 53 patients with transformation and 53 controls with a similar distribution of follicular lymphoma (FL). Time to transformation and control follow-up time was similar. We observed a significant incremental increase in SUV-max and TLG between control, pretransformation and post-transformation groups (P < 0.05). By multivariable analysis, we identified a significant interaction between SUV-max and TLG such that SUV-max had highest significance for low volume cases (P = 0.04). We developed a scoring model incorporating SUV-max, TLG, and serum LDH with improved identification of transformation (area under the curve [AUC] = 0.91). Our model performed similarly for our validation cohort of 23 patients (AUC = 0.90). With external and prospective validation, our scoring model may provide a specific and noninvasive tool for risk stratification for patients with low-grade lymphoma.

Improving the prognostic ability of PET/CT SUVmax to identify follicular lymphoma with early treatment failure

Follicular lymphoma (FL) has a high degree of heterogeneity both clinically and molecularly. Early treatment failure (ETF), progression or relapse within 24 months of frontline immunochemotherapy is associated with a poor prognosis in FL. However, the clinical utility of ETF at diagnosis is limited. The maximum standardized uptake value (SUVmax) is a metabolic parameter for positron emission tomography/computed tomography (PET/CT); nevertheless, the relationship between SUVmax and ETF remains unclear. Thus, identifying early biomarkers that incorporate SUVmax and other clinical correlative variables could be helpful in identifying patients at high risk of ETF. A nomogram consisted of three independent variables, including SUVmax ≥ 12, beta-2 microglobulin > 3 mg/L, and Ki67 > 40%, was established to predict ETF in 127 patients with grade 1, 2, or 3a FL from the First Hospital of Jilin University (training cohort) and was validated using data from the Duke University Medical Center (validation cohort, n=95). The nomogram demonstrated prognostic accuracy in predicting ETF (sensitivity 70.8% and specificity 83.5% in the training cohort; sensitivity 84.2% and specificity 68.4% in the validation cohort). The patients were stratified into three groups: low-, intermediate-, and high-risk. In the training cohort, the corresponding 5-year progression-free survival (PFS) rates were 81.7%, 73.4%, and 34.9%, and the 5-year overall survival (OS) rates were 97.4%, 87.4%, and 62.3%, respectively. In the validation cohort, the 5-year PFS rates were 77.7%, 52.9%, and 34.8%, and the 5-year OS rates were 96.4%, 94.1%, and 73.7%, respectively. This was the first study to use a nomogram with SUVmax to predict ETF in FL to identify a subset of patients who might benefit from individualized targeted therapy.

Baseline [18F]FDG PET/CT may predict the outcome of newly diagnosed follicular lymphoma in patients managed with initial "watch-and-wait" approach

OBJECTIVES

To investigate if baseline [¹⁸F]FDG PET/CT can predict the outcome of follicular lymphoma (FL) in patients managed with an initial "watch-and-wait" approach.

METHODS

Thirty-eight newly diagnosed FL patients who were managed with an initial "watch-and-wait" approach and undergone baseline [¹⁸F]FDG PET/CT were retrospectively enrolled. The standard uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of FL lesions were measured on PET/CT. Patients were followed up for at least 24 months or until initiation of FL therapy. The endpoint was the time to initiation of lymphoma treatment (TLT).

RESULTS

After a median follow-up of 28 months (range 3-94 months), lymphoma treatment was initiated in 21/38 (55.3%) patients (median 15 months, range 3-51 months). Patients with TLT < 24 months showed SUVmax and TLG values significantly higher than those with TLT ≥ 24 months (p < 0.05). Receiver operating characteristic analysis demonstrated cutoff values of SUVmax > 9.5, MTV > 90.62 ml, and TLG > 144.96 SUVbw*ml were optimal for predicting TLT < 24 months. Kaplan-Meier analysis showed SUVmax > 9.5, MTV > 90.62 ml, and TLG > 144.96 SUVbw*ml had statistically significant correlations with shorter TLT (p < 0.01). Lymph node regions ≥ 3 and lymph nodes > 3 cm had almost significance (p < 0.1). In multivariate analysis, SUVmax > 9.5 (HR 3.2 [95% CI 1.1-9.2], p = 0.033) and TLG > 144.96 SUVbw*ml (HR 9.3 [95% CI 1.8-47.7], p = 0.008) were demonstrated to be independent predictive factors for shorter TLT.

CONCLUSIONS

Metabolic indices (SUVmax and TLG) of baseline [¹⁸F]FDG PET/CT could predict the outcome independently in FL patients under an initial "watch-and-wait" approach.

KEY POINTS

• "Watch-and-wait" approach is part of the overall treatment plan in asymptomatic patients with low tumor burden FL. However, the time to initiation of active treatment varies from months to years. • In our retrospective study of 38 patients with FL managed with an initial "watch-and-wait" approach, the SUVmax and TLG were demonstrated to be independent predictive factors for time to initiation of FL treatment. • Baseline [¹⁸F]FDG PET/CT may help to better select patients with FL who are most likely to benefit from "watch-and-wait" management.

Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma

PURPOSE

The aim of this study was to develop a pretherapy PET/CT-based prediction model for treatment response to ibrutinib in lymphoma patients.

PATIENTS AND METHODS

One hundred sixty-nine lymphoma patients with 2441 lesions were studied retrospectively. All eligible lymphomas on pretherapy 18F-FDG PET images were contoured and segmented for radiomic analysis. Lesion- and patient-based responsiveness to ibrutinib was determined retrospectively using the Lugano classification. PET radiomic features were extracted. A radiomic model was built to predict ibrutinib response. The prognostic significance of the radiomic model was evaluated independently in a test cohort and compared with conventional PET metrics: SUVmax, metabolic tumor volume, and total lesion glycolysis.

RESULTS

The radiomic model had an area under the receiver operating characteristic curve (ROC AUC) of 0.860 (sensitivity, 92.9%, specificity, 81.4%; P < 0.001) for predicting response to ibrutinib, outperforming the SUVmax (ROC AUC, 0.519; P = 0.823), metabolic tumor volume (ROC AUC, 0.579; P = 0.412), total lesion glycolysis (ROC AUC, 0.576; P = 0.199), and a composite model built using all 3 (ROC AUC, 0.562; P = 0.046). The radiomic model increased the probability of accurately predicting ibrutinib-responsive lesions from 84.8% (pretest) to 96.5% (posttest). At the patient level, the model's performance (ROC AUC = 0.811; P = 0.007) was superior to that of conventional PET metrics. Furthermore, the radiomic model showed robustness when validated in treatment subgroups: first (ROC AUC, 0.916; P < 0.001) versus second or greater (ROC AUC, 0.842; P < 0.001) line of defense and single treatment (ROC AUC, 0.931; P < 0.001) versus multiple treatments (ROC AUC, 0.824; P < 0.001).

CONCLUSIONS

We developed and validated a pretherapy PET-based radiomic model to predict response to treatment with ibrutinib in a diverse cohort of lymphoma patients.

Efficacy of front-line immunochemotherapy for follicular lymphoma: a network meta-analysis of randomized controlled trials

Front-line treatment for follicular lymphoma has evolved with the introduction of maintenance therapy, bendamustine (Benda), obinutuzumab (G), and lenalidomide (Len). We conducted a random-effects Bayesian network meta-analysis (NMA) of phase 3 randomized controlled trials (RCTs) to identify the regimens with superior efficacy. Progression-free survival (PFS) was compared between 11 modern regimens with different immunochemotherapy and maintenance strategies. G-Benda-G resulted in with the best PFS, with an HR of 0.41 compared to R-Benda, a surface under the cumulative ranking curve (SUCRA) of 0.97, a probability of being the best treatment (PbBT) of 72%, and a posterior ranking distribution (PoRa) of 1 (95% BCI 1-3). This was followed by R-Benda-R4 (HR = 0.49, PbBT = 25%, PoRa = 2) and R-Benda-R (HR = 0.60, PbBT = 3%, PoRa = 3). R-CHOP-R (HR = 0.96) and R-Len-R (HR = 0.97) had similar efficacy to R-Benda. Bendamustine was a better chemotherapy backbone than CHOP either with maintenance (R-Benda-R vs R-CHOP-R, HR = 0.62; G-Benda-G vs G-CHOP-G, HR = 0.55) or without maintenance therapy (R-Benda vs R-CHOP, HR = 0.68). Rituximab maintenance improved PFS following R-CHOP (R-CHOP-R vs R-CHOP, HR = 0.65) or R-Benda (R-Benda-R vs R-Benda, HR = 0.60; R-Benda-R4 vs R-Benda, HR = 0.49). In the absence of multi-arm RCTs that include all common regimens, this NMA provides an important and useful guide to inform treatment decisions.

Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features

BACKGROUND

One of the challenges in the management of patients with follicular lymphoma (FL) is the identification of individuals with histological transformation, most commonly into diffuse large B-cell lymphoma (DLBCL). [¹⁸F]FDG-PET/CT is used for staging of patients with lymphoma, but visual interpretation cannot reliably discern FL from DLBCL. This study evaluated whether radiomic features extracted from clinical baseline [¹⁸F]FDG PET/CT and analyzed by machine learning algorithms may help discriminate FL from DLBCL.

MATERIALS AND METHODS

Patients were selected based on confirmed histopathological diagnosis of primary FL (n=44) or DLBCL (n=76) and available [¹⁸F]FDG PET/CT with EARL reconstruction parameters within 6 months of diagnosis. Radiomic features were extracted from the volume of interest on co-registered [¹⁸F]FDG PET and CT images. Analysis of selected radiomic features was performed with machine learning classifiers based on logistic regression and tree-based ensemble classifiers (AdaBoosting, Gradient Boosting, and XG Boosting). The performance of radiomic features was compared with a SUV_max-based logistic regression model.

RESULTS

From the segmented lesions, 121 FL and 227 DLBCL lesions were included for radiomic feature extraction. In total, 79 radiomic features were extracted from the SUVmap, 51 from CT, and 6 shape features. Machine learning classifier Gradient Boosting achieved the best discrimination performance using 136 radiomic features (AUC of 0.86 and accuracy of 80%). SUV_max-based logistic regression model achieved an AUC of 0.79 and an accuracy of 70%. Gradient Boosting classifier had a significantly greater AUC and accuracy compared to the SUV_max-based logistic regression (p≤0.01).

CONCLUSION

Machine learning analysis of radiomic features may be of diagnostic value for discriminating FL from DLBCL tumor lesions, beyond that of the SUV_max alone.

Follicular lymphoma and macrophages: impact of approved and novel therapies

The survival and proliferation of follicular lymphoma (FL) cells are strongly dependent on macrophages, because their presence is necessary for the propagation of FL cells in vitro. To this regard, as also shown for the majority of solid tumors, a high tissue content of tumor-associated macrophages (TAMs), particularly if showing a protumoral phenotype (also called M2), is strongly associated with a poor outcome among patients with FL treated with chemotherapy. The introduction of rituximab, an anti-CD20 antibody that can be used by TAMs to facilitate antibody-dependent cellular cytotoxicity and antibody-dependent cellular phagocytosis, has challenged this paradigm. In the rituximab era, clinical studies have yielded conflicting results in FL, showing variable outcomes based on the type of regimen used. This highlighted, for the first time, that the impact of TAMs on the prognosis of patients with FL may depend on the administered treatment, emphasizing the need to better understand how currently available therapies affect macrophage function in FL. We summarize the impact of approved and novel therapies for FL, including radiation therapy, chemotherapy, anti-CD20 monoclonal antibodies, lenalidomide, and targeted agents, on the biology of TAMs and describe their effects on macrophage phagocytosis, polarization, and function. Although novel agents targeting the CD47/SIRPα axis are being developed and show promising activity in FL, a deeper understanding of macrophage biology and their complex pathways will help to develop novel and safer therapeutic strategies for patients with this type of lymphoma.

Follicular Lymphoma: Refining Prognostic Models and Impact of Pod-24 in Clinical Outcomes

Follicular lymphoma (FL) is the most common indolent lymphoma, accounting for 20%-25% of all non-Hodgkin's lymphomas (NHLs). It is a malignancy with variable biologic presentation and heterogeneous clinical outcomes. Several models incorporating clinical laboratory variables and molecular biomarkers are able to predict its prognosis, allowing to stratify patients into different risk groups. However, these prognostic scores should not be used to indicate first-line treatment or risk-adapted therapeutic recommendations. Over the past 5 years, progression of disease within 24 months (POD-24) of first-line chemo-immunotherapy has emerged as a robust adverse prognostic factor, capable of assessing overall survival and identifying high-risk patients with indication for more aggressive therapeutic approaches, such as consolidation based in autologous stem cell transplantation. It should be reinforced that POD-24 is not a baseline measurement, it is based on a post-treatment strategy, and is usually applied to patients with a high tumor burden. The identification of newly diagnosed patients at high risk for disease progression, particularly those with low tumor volume is still a challenge in the context of FL. Therefore, the primary purpose of this review is to provide an overview of the main prognostic models validated to date for FL. Moreover, using these scores, which incorporate clinical and genetic variables, we aim to identify individuals with newly diagnosed FL, advanced disease, and low tumor burden with a high probability of progression or relapse within 24 months of first treatment. Thus, a decision regarding risk-adapted induction therapy could be better stablished for these subset of patients.

Is it time for PET-guided therapy in follicular lymphoma

18F-fluorodeoxyglucose (FDG) Positron Emission Tomography-Computerised Tomography (PET) is now established as the gold-standard imaging modality for both staging and response assessment of follicular lymphoma (FL). In this Perspective, we propose where PET can, and cannot, guide clinicians in their therapeutic approach. PET at diagnosis/pre-treatment is important for staging, with greater sensitivity compared to standard CT and consequent improved outcomes in truly limited stage FL. Small datasets suggesting a high baseline SUVmax identifies de-novo histologic transformation (HT) are not corroborated by data from GALLIUM, the largest prospective study using modern therapies for FL. Nonetheless, the role of baseline quantitative PET measures requires further clarification. The median survival of patients with newly diagnosed FL is now potentially beyond 20 years. Treatment of symptomatic FL aims to achieve remission and optimise quality of life for as long as possible, with many patients achieving a "functional cure" at the cost of unwanted treatment effects. Several studies have identified that end-of-induction (EOI) PET after initial chemoimmunotherapy for patients with high tumour burden is strongly predictive of both progression-free and overall survival, and EOI PET is being evaluated as a platform for response-adapted treatment. There remain unmet needs: improving the inferior survival for patients remaining PET-positive; and quantifying the PFS and time to next treatment advantage, and additional toxicity of anti-CD20 maintenance in patients achieving complete metabolic remission. In the absence of an overall survival advantage for frontline maintenance, the question of using PET to guide our therapeutic approach is more important than ever in the context of the COVID-19 pandemic.

Intratumoral T cells have a differential impact on FDG-PET parameters in follicular lymphoma

Data on the prognostic impact of pretherapy 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in follicular lymphoma (FL) is conflicting. The predictive utility of pretherapy total metabolic tumor volume (TMTV) and maximum standardized uptake value (SUVmax) on outcome appears to vary between regimens. Chemoimmunotherapies vary in the extent of T-cell depletion they induce. The role of intratumoral T cells on pretherapy FDG-PET parameters is undefined. We assessed pretherapy FDG-PET parameters and quantified intratumoral T cells by multiple methodologies. Low intratumoral T cells associated with approximately sixfold higher TMTV, and FL nodes from patients with high TMTV showed increased malignant B-cell infiltration and fewer clonally expanded intratumoral CD8+ and CD4+ T-follicular helper cells than those with low TMTV. However, fluorescently labeled glucose uptake was higher in CD4+ and CD8+ T cells than intratumoral B cells. In patients with FDG-PET performed prior to excisional biopsy, SUVmax within the subsequently excised node associated with T cells but not B cells. In summary, TMTV best reflects the malignant B-cell burden in FL, whereas intratumoral T cells influence SUVmax. This may contribute to the contradictory results between the prognostic role of different FDG-PET parameters, particularly between short- and long-term T-cell-depleting chemoimmunotherapeutic regimens. The impact of glucose uptake in intratumoral T cells should be considered when interpreting pretherapy FDG-PET in FL.

High Basal Maximal Standardized Uptake Value (SUVmax) in Follicular Lymphoma Identifies Patients with a Low Risk of Long-Term Relapse

BACKGROUND

Despite that the unfavorable prognostic role of a high Total Metabolic Tumor Volume (TMTV) in Follicular Lymphoma has been demonstrated, the role of SUV_max alone at baseline PET/CT could have a different prognostic role.

PATIENTS AND METHODS

We performed a retrospective observational monocentric cohort study. All patients affected by FL who underwent a basal PET/CT were included. Two subgroups were identified and compared in terms of PFS and OS: (A) Basal SUV_max ≤ 6; and (B) Basal SUV_max > 6.

RESULTS

Ninety-four patients were included, 34 in group A (36.2%) and 60 in group B (63.8%). The PFS at two years was comparable in the two groups (97%). The five-year PFS was 73.5% for group A and 95% for group B (p 0.005). The five-year PFS in the whole cohort was 87.5%. A clear advantage was confirmed in group A in the absence of other risk factors. Patients with SUV_max ≤ 6 and no risk factors showed a 5-year PFS of 73% against 83% for patients with SUV_max > 6 and at least two risk factors.

CONCLUSION

A high FDG uptake favorably correlated with PFS. A low basal SUV_max reflected a higher rate of late relapse requiring a prolonged follow-up. The basal SUV_max is an approachable parameter with prognostic implications.

Baseline SUVmax is related to tumor cell proliferation and patient outcome in follicular lymphoma

Follicular lymphoma (FL) is the most common indolent lymphoma. Despite the clear benefit of CD20-based therapy, a subset of FL patients still progress to aggressive lymphoma. Thus, identifying early biomarkers that incorporate PET metrics could be helpful to identify patients with a high risk of treatment failure with Rituximab. We retrospectively included a total of 132 untreated FL patients separated into training and validation cohorts. Optimal threshold of baseline SUVmax was first determined in the training cohort (n=48) to predict progression-free survival (PFS). The PET results were investigated along with the tumor and immune microenvironment, which were determined by immunochemistry and transcriptome studies involving gene set enrichment analyses and immune cell deconvolution, together with the tumor mutation profile. We report that baseline SUVmax >14.5 was associated with poorer PFS than baseline SUVmax ≤14.5 (HR=0.28; p=0.00046). Neither immune T-cell infiltration nor immune checkpoint expression were associated with baseline PET metrics. By contrast, FL samples with Ki-67 staining ≥10% showed enrichment of cell cycle/DNA genes (p=0.013) and significantly higher SUVmax values (p=0.007). Despite similar oncogenic pathway alterations in both SUVmax groups of FL samples, 4 out of 5 cases harboring the infrequent FOXO1 transcription factor mutation were seen in FL patients with SUVmax >14.5. Thus, high baseline SUVmax reflects FL tumor proliferation and, together with Ki-67 proliferative index, can be used to identify patients at risk of early relapse with R-chemotherapy.

Precision medicine in follicular lymphoma: Focus on predictive biomarkers

Current care for patients with follicular lymphoma (FL) offers most of them long-term survival. Improving it further will require careful patient selection. This review focuses on predictive biomarkers (ie, those whose outcome correlations depend on the treatment strategy) in FL, because awareness of what patient subsets benefit most or least from each therapy will help in this task. The first part of this review aims to summarize what biomarkers are predictive in FL, the magnitude of the effect and the quality of the evidence. We find predictive biomarkers in the setting of (a) indication of active treatment, (b) front-line induction (use of anthracyline-based regimens, CHOP vs bendamustine, addition of rituximab), (c) post-(front-line)induction (rituximab maintenance, radioimmunotherapy), and (d) relapse (hematopoietic stem cell transplant) and targeted agents. The second part of this review discusses the challenges of precision medicine in FL, including (a) cost, (b) clinical relevance considerations, and (c) difficulties over the broad implementation of biomarkers. We then provide our view on what biomarkers may become used in the next few years. We conclude by underscoring the importance of assessing the potential predictiveness of available biomarkers to improve patient care but also that there is a long road ahead before reaching their broad implementation due to remaining scientific, technological, and economic hurdles.