Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques.

Simple Summary: This manuscript aims to address the diagnostic challenges of mediastinal bulky lymphomas with the baseline value of 18F-FDG PET/CT metabolic, volumetric and texture parameters, also relying on machine learning techniques, in patients with grey zone lymphoma, primary diffuse large B-cell lymphoma of the mediastinum and classical Hodgkin lymphoma. Different types of histology demonstrated several baseline 18F-FDG PET/CT radiomics parameters that were significantly different from one another, suggesting the possibility of identifying potential histological heterogeneity and aggressive transformation. Moreover, using radiomics-based imaging biomarkers, machine learning techniques offer a solution for separating not completely disjoint histological types. To date, the gold standard for diagnosis is biopsy, but machine learning methods could be combined with radiomics to build a histological representation of mediastinal bulky masses that is able to successfully identify different types of lymphomas. Finally, this preliminary study supports the potential of metabolic texture analyses as future imaging biomarkers, with a growing role in clinical diagnosis. Background: This study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques. Methods: We reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features. Results: The analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation. Conclusions: Our results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma. [ABSTRACT FROM AUTHOR]