Purpose: Mediastinal bulky involvement is common in lymphomas, particularly in classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL). Despite advanced methods of assessment, traditional imaging appears insufficient in differentiating histology. This study tested the diagnostic value of 18F-FDG PET/CT volumetric and texture parameters in the histological differentiation of mediastinal bulky due to GZL, PMBCL and cHL, also using a machine-learning approach.
Methods: We retrospectively reviewed patients with mediastinal bulky disease with a histopathological diagnosis of cHL, PMBCL or GZL who underwent pre-treatment 18F-FDG PET/CT. Lesions were delineated using a fully automated preselection of 18F-FDG avid structures defined by a threshold SUV ≥ 2.5. Volumetric and radiomic parameters were measured using LIFEx software both for bulky lesion (BL) and for all lesions (AL) on 18F-FDG PET/CT. Analysis of selected radiomic features was performed with Machine Learning classifiers based on Logistic Regression.
Results: We reviewed 117 patients (29 PMBCL, 80 cHL, 8 GZL). The analysis showed significant differences between the 3 lymphoma groups regarding SUVmax, SUVmean, BL/AL-MTV and BL/AL-TLG. Several PET textural features both of first order and of second order grey-level showed significant differences between the 3 groups. Finally, machine-learning classifier provided good accuracy in the discrimination between groups. Logistic Regression showed good performance, confirming true positive rate (TPR) and true negative rate (TNR) greater than 80% in the characterization of PMBCL and cHL. The multiclass classifier showed TPR greater than 70% and TNR lower 5% in the identification of PMBCL and cHL and TPR of 44% in GZL.
Conclusion: Mediastinal bulky involvement from different histologies showed diverse 18F-FDG-PET radiomic features and the machine-learning approach successfully identified the different subtypes. Our results support the potential of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.