Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma

• Published in: EJNMMI research Vol. 13; no. 1; p. 92
• Main Authors: Jing, Fenglian, Liu, Yunuan, Zhao, Xinming, Wang, Na, Dai, Meng, Chen, Xiaolin, Zhang, Zhaoqi, Zhang, Jingmian, Wang, Jianfang, Wang, Yingchen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 26.10.2023; Springer Nature B.V; SpringerOpen
• Subjects: Cardiac Imaging; Diffuse large B-cell lymphoma; FDG PET/CT; Fluorine isotopes; Health services; Heterogeneity; Imaging; Lymphoma; Machine learning; Mathematical models; Medical prognosis; Medicine; Medicine & Public Health; Metabolism; Nuclear Medicine; Oncology; Original Research; Orthopedics; Parameters; Positron emission; Prognosis; Radiology; Radiomics; Risk stratification; Signatures; Survival; Prognosis; FDG PET/CT; Risk stratification; Machine learning; Diffuse large B-cell lymphoma; Radiomics
• ISSN: 2191-219X; 2191-219X
• DOI: 10.1186/s13550-023-01047-5

Background Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18 F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL. Results A total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell’s concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P  = 0.017) and OS (AUC: 0.767 vs. 0.667, P  = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P  = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P  = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P  = 0.163) or OS (AUC: 0.772 vs. 0.767, P  = 0.403). Conclusions Radiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.