Radiomic features of PET/CT imaging of large B cell lymphoma lesions predicts CAR T cell therapy efficacy

Abstract

Background: Relapsed and refractory Diffuse large B cell lymphoma (DLBCL) can be successfully treated with axicabtagene ciloleucel (axi-cel), a CD19-directed autologous chimeric antigen receptor T cell (CAR-T) therapy. Diagnostic image-based features could help identify the patients who would clinically respond to this advanced immunotherapy.

Purpose: The aim of this study was to establish a radiomic image feature-based signature derived from positron emission tomography/computed tomography (PET/CT), including metabolic tumor burden, which can predict a durable response to CAR-T therapy in refractory/relapsed DLBCL.

Methods: We conducted a retrospective review of 155 patients with relapsed/refractory DLBCL treated with axi-cel CAR-T therapy. The patients' disease involvement was evaluated based on nodal or extranodal sites. A sub-cohort of these patients with at least one nodal lesion (n=124) was assessed, while an overlapping sub-cohort (n=94) had at least one extranodal lesion. The lesion regions were characterized using 306 quantitative imaging metrics for PET images and CT images independently. Principal component (PC) analysis was performed to reduce the dimensionality in feature-based functional categories: size (n=38), shape (n=9), and texture (n=259). The selected features were used to build prediction models for survival at 1 year and tested for prognosis to overall/progression-free survival (OS/PFS) using a Kaplan-Meier (KM) plot.

Results: The Shape-based PC features of the largest extranodal lesion on PET were predictive of 1-year survival (AUC 0.68 [0.43,0.94]) and prognostic of OS/PFS (p<0.018). Metabolic tumor volume (MTV) was an independent predictor with an area under the curve (AUC) of 0.74 [0.58, 0.87]. Combining these features improved the predictor performance (AUC of 0.78 [0.7, 0.87]). Additionally, the Shape-based PC features were unrelated to total MTV (Spearman's ρ of 0.359, p≤ 0.001).

Conclusion: Our study found that shape-based radiomic features on PET imaging were predictive of treatment outcome (1-year survival) and prognostic of overall survival. We also found non-size-based radiomic predictors that had comparable performance to MTV and provided complementary information to improve the predictability of treatment outcomes.

Keywords: MTV (metabolic tumor volume); PET/CT scan; biomarkers in CAR T cell therapy; imaging biomarkers in lymphoma; radiomics in immunotherapy.

Figure 1

Patient scans showing representative slices of lesions in different image modalities (CT/PET and fused) with an arrow. (A) Lesions associated with the lymphatic system in the pelvis and (B) extranodal lesions in the abdomen.

Figure 2

Receiver operating characteristic curves to predict 1-year overall survival using a logistic model based on shape features of the largest lesion in the extranodal regions of PET (SUV) images (see Table 2 ). The curves include MTV (average AUC 0.74), the Shape-based radiomic features (Principal components 1 to 3) (average AUC 0.68), and Shape-based radiomic features (Principal components 1 to 3) with MTV (average AUC 0.79).

Figure 3

Kaplan-Meier (KM) plots obtained using patients grouped with a cut-off point obtained from a logistic model for the Shape-based radiomic features (Principal components 1 to 3) extracted from the extranodal regions of PET (SUV) images (details in Table 2 ); (A) Overall survival, (B) Progression-free survival.

Figure 4

Patients selected based on shape-based predictors (principal component-based) using the PET radiomic features of their largest extranodal lesion. Representative PET/Fused (PET-CT) image slices were selected: (A) The panel on left side indicated high Shape PC metric, and (B) the panel on the right corresponds to patients with a smaller value for the Shape PC metric for their largest extranodal lesion.