ArticleFrontiers in oncology2024
Radiomic features of PET/CT imaging of large B cell lymphoma lesions predicts CAR T cell therapy efficacy.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review and meta-analysis of PET-based prognostic metrics in CAR-T treatment of DLBCL.Frontiers in immunology · 2026Pooled it
- Circulating Tumor DNA Assessment of Disease Response in Large B-Cell Lymphoma: Lisocabtagene Maraleucel Versus Autologous Stem Cell Transplantation Standard Therapy.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Trial
- Predictors of response to CD19 chimeric antigen receptor T-cell therapy in large B-cell lymphoma: a consolidated review.Current opinion in oncology · 2025Review
- Article
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Authors and funding
13 authors.
Funding
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.
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