A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA

Challenges identifying DLBCL patients with poor outcomes to upfront chemoimmunotherapy and its impact on frontline clinical trials

Diffuse large B cell lymphoma (DLBCL) has a variable course of disease among patients as it consists of subgroups that are clinically, biologically and molecularly heterogeneous. In this review, we will discuss how this heterogeneity has likely hindered the ability of traditional prognostic models to identify DLBCL patients at high risk of having poor outcomes with conventional upfront chemoimmunotherapy. We will highlight the challenges and downsides of using these models for risk stratification in clinical trials. Also, we present some of the novel prognosticators that have shown a prognostic value independently or when incorporated into existing prognostic models. Additionally, since the failure of frontline clinical trials to improve outcomes beyond R-CHOP chemoimmunotherapy may be at least partially explained by the restrictive eligibility criteria, risk stratification methods and the selection bias encountered due to the complexed logistics of clinical trials; we will discuss strategies to refine and modernize clinical trial design.

Integration of PET in DLBCL

F-fluorodeoxyglucose positron emission tomography-computerized tomography (18FDG-PET/CT) is the gold-standard imaging modality for staging and response assessment for most lymphomas. This review focuses on the utility of 18FDG-PET/CT, and its role in staging, prognostication and response assessment in diffuse large B-cell lymphoma (DLBCL), including emerging possibilities for future use.

Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells

BACKGROUND

In our current work, an 18F-FDG PET/CT radiomics-based model was developed to assess the progression-free survival (PFS) and overall survival (OS) of patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received chimeric antigen receptor (CAR)-T cell therapy.

METHODS

A total of 61 DLBCL cases receiving 18F-FDG PET/CT before CAR-T cell infusion were included in the current analysis, and these patients were randomly assigned to a training cohort (n = 42) and a validation cohort (n = 19). Radiomic features from PET and CT images were obtained using LIFEx software, and radiomics signatures (R-signatures) were then constructed by choosing the optimal parameters according to their PFS and OS. Subsequently, the radiomics model and clinical model were constructed and validated.

RESULTS

The radiomics model that integrated R-signatures and clinical risk factors showed superior prognostic performance compared with the clinical models in terms of both PFS (C-index: 0.710 vs. 0.716; AUC: 0.776 vs. 0.712) and OS (C-index: 0.780 vs. 0.762; AUC: 0.828 vs. 0.728). For validation, the C-index of the two approaches was 0.640 vs. 0.619 and 0.676 vs. 0.699 for predicting PFS and OS, respectively. Moreover, the AUC was 0.886 vs. 0.635 and 0.778 vs. 0.705, respectively. The calibration curves indicated good agreement, and the decision curve analysis suggested that the net benefit of radiomics models was higher than that of clinical models.

CONCLUSIONS

PET/CT-derived R-signature could be a potential prognostic biomarker for R/R DLBCL patients undergoing CAR-T cell therapy. Moreover, the risk stratification could be further enhanced when the PET/CT-derived R-signature was combined with clinical factors.

An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma

The objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-year time to progression (TTP). The metabolic tumor volume (MTV), maximum distance between the largest lesion and another lesion (Dmaxbulk), and peak standardized uptake value (SUVpeak) were extracted. The predictive values of the IPI and clinical PET model (MTV, Dmaxbulk, SUVpeak, performance status, and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance, using the positive predictive value (PPV). The IPI yielded an AUC of 0.62. The clinical PET model yielded a significantly higher AUC of 0.71 (P < .001). Patients with high-risk IPI had a 2-year PFS of 61.4% vs 51.9% for those with high-risk clinical PET, with an increase in PPV from 35.5% to 49.1%, respectively. A total of 66.4% of patients with high-risk IPI were free from progression or relapse vs 55.5% of patients with high-risk clinical PET scores, with an increased PPV from 33.7% to 44.6%, respectively. The clinical PET model remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.

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.