Value of CT-Textural Features and Volume-Based PET Parameters in Comparison to Serologic Markers for Response Prediction in Patients with Diffuse Large B-Cell Lymphoma Undergoing CD19-CAR-T Cell Therapy

Reduced Chimeric Antigen Receptor T Cell Expansion Postinfusion Is Associated with Poor Survival in Patients with Large B Cell Lymphoma after Two or More Therapies

CD19-directed chimeric antigen receptor T cell (CAR-T) therapy is now standard of care for relapsed/refractory large B cell non-Hodgkin lymphoma. Despite good overall response rates, many patients still experience disease progression and therefore it is important to predict those at risk of relapse following CAR-T therapy. We performed a prospective study using a flow cytometry assay at a single treatment center to assess early CAR T cell expansion in vivo 6 to 9 days after CAR T cell infusion. Early CAR T cell expansion was used in conjunction with additional clinical risk factors to identify those at greater risk of relapse or treatment failure. Forty-four patients treated with commercial CD19-directed CAR-T therapy were included in the study, with a median follow-up of 306 days. CAR T cell expansion of >30 cells/μL was associated with a lower risk of disease progression or death (hazard ratio, 0.34; P = .048), but did not correlate with the risk of death alone. Patients who had poor early CAR T cell expansion (<30 cells/μL) in addition to high lactate dehydrogenase (LDH) had significantly lower median progression-free survival and overall survival. High LDH level alone was not a statistically significant risk factor for death or disease progression, and thus the interaction between CAR T cell expansion and this clinical risk factor may be important in predicting response. The mean CAR T cell count was higher in patients with grade 2 to 4 cytokine release syndrome (CRS) compared to those with grade 0 to 1 CRS (54.9 cells/μL versus 25.5 cells/μL; P = .01). The methodology of this assay is easily reproducible outside of a clinical trial, allowing for real-life implementation in clinical settings. This study suggests that early assessment of CAR T cell expansion can assist in identifying patients with poor overall survival who may benefit from early intervention or more intensive monitoring.

Biomarkers for prediction of CAR T therapy outcomes: current and future perspectives

Chimeric antigen receptor (CAR) T cell therapy holds enormous potential for the treatment of hematologic malignancies. Despite its benefits, it is still used as a second line of therapy, mainly because of its severe side effects and patient unresponsiveness. Numerous researchers worldwide have attempted to identify effective predictive biomarkers for early prediction of treatment outcomes and adverse effects in CAR T cell therapy, albeit so far only with limited success. This review provides a comprehensive overview of the current state of predictive biomarkers. Although existing predictive metrics correlate to some extent with treatment outcomes, they fail to encapsulate the complexity of the immune system dynamics. The aim of this review is to identify six major groups of predictive biomarkers and propose their use in developing improved and efficient prediction models. These groups include changes in mitochondrial dynamics, endothelial activation, central nervous system impairment, immune system markers, extracellular vesicles, and the inhibitory tumor microenvironment. A comprehensive understanding of the multiple factors that influence therapeutic efficacy has the potential to significantly improve the course of CAR T cell therapy and patient care, thereby making this advanced immunotherapy more appealing and the course of therapy more convenient and favorable for patients.

CT radiomics can predict disease progression within 6 months after chimeric antigen receptor-modified T-cell therapy in relapsed/refractory B-cell non-Hodgkin's lymphoma patients

AIM

To predict progression within 6 months after chimeric antigen receptor-modified (CAR) T-cell therapy for relapsed/refractory (R/R) B-cell non-Hodgkin's lymphoma (B-NHL) patients by radiomic indexes derived from contrast-enhanced computed tomography (CECT) examinations.

MATERIALS AND METHODS

Seventy R/R B-NHL patients who underwent CECT before treatment with CAR T-cells were examined retrospectively. In total, 297 volumes of interest for lesions were segmented from CECT images. Patients without and with disease progression were assigned to groups 1 and 2, respectively. Radiomic and combined predictive models were constructed by three machine-learning algorithms using features from the training set, respectively. Furthermore, predictive models were constructed based on multi-lesion-based and largest-lesion-based radiomic features, respectively.

RESULTS

In the test set, no marked differences were observed between the areas under the curves (AUCs) of the combined and radiomic models for all three machine-learning algorithms (all p>0.05). Differences in machine-learning algorithms did not significantly affect the predictive performances of the models. Radiomics and combined models constructed with multi-lesion-based radiomic features showed better predictive performances than those applying largest-lesion-based radiomic features (all p<0.05 for comparisons between combined models).

CONCLUSION

CECT-based radiomic features may be applied to predict disease progression in R/R B-NHL patients within 6 months after CAR T-cell treatment, and radiomic features from multiple lesions may have better predictive efficacy. Different machine-learning algorithms may not show significant differences in prediction performance.

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed. Transfer learning using a pre-trained neural network model, then retrained for a specific task, was used to predict lesion-level treatment responses from separate dCT, lCT, and FDG-PET images. Patient-level response analysis was performed by applying rule-based reasoning to lesion-level prediction results. Patient-level response prediction was also compared to prediction based on the international prognostic index (IPI) for diffuse large B-cell lymphoma. The average accuracy of lesion-level response prediction based on single whole dCT slice-based input was 0.82+0.05 with sensitivity 0.87+0.07, specificity 0.77+0.12, and AUC 0.91+0.03. Patient-level response prediction from dCT, using the "Majority 60%" rule, had accuracy 0.81, sensitivity 0.75, and specificity 0.88 using 12-month post-treatment patient response as the reference standard and outperformed response prediction based on IPI risk factors (accuracy 0.54, sensitivity 0.38, and specificity 0.61 (p = 0.046)). Prediction of treatment outcome in B-cell lymphomas from pre-treatment medical images using DL-based image analysis and rule-based reasoning is feasible. This approach can potentially provide clinically useful prognostic information for decision-making in advance of initiating CAR T-cell therapy.

Prognostic value of baseline and early response FDG-PET/CT in patients with refractory and relapsed aggressive B-cell lymphoma undergoing CAR-T cell therapy

PURPOSE

Chimeric antigen receptor (CAR)-T cells are a viable treatment option for patients with relapsed or refractory (r/r) aggressive B-cell lymphomas. The prognosis of patients who relapse after CAR-T cell treatment is dismal and factors predicting outcomes need to be identified. Our aim was to assess the value of FDG-PET/CT in terms of predicting patient outcomes.

METHODS

Twenty-two patients with r/r B-cell lymphoma who received CAR-T cell treatment with tisagenlecleucel (n = 17) or axicabtagene ciloleucel (n = 5) underwent quantitative FDG-PET/CT before (PET-0) and 1 month after infusion of CAR-T cells (PET-1). PET-1 was classified as complete metabolic response (CMR, Deauville score 1-3) or non-CMR (Deauville score 4-5).

RESULTS

At the time of PET-1, 12/22 (55%) patients showed CMR, ten (45%) patients non-CMR. 7/12 (58%) CMR patients relapsed after a median of 223 days, three of them (25%) died. 9/10 (90%) non-CMR patients developed relapse or progressive disease after a median of 91 days, eight of them (80%) died. CMR patients demonstrated a significantly lower median total metabolic tumor volume (TMTV) in PET-0 (1 ml) than non-CMR patients (225 ml).

CONCLUSION

Our results confirm the prognostic value of PET-1. 42% of all CMR patients are still in remission 1 year after CAR T-cell treatment. 90% of the non-CMR patients relapsed, indicating the need for early intervention. Higher TMTV before CAR-T cell infusion was associated with lower chances of CMR.

Semi-automated 18F-FDG PET segmentation methods for tumor volume determination in Non-Hodgkin lymphoma patients: a literature review, implementation and multi-threshold evaluation

In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography (¹⁸F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool. A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures. High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc. No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.