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

Abstract

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.

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

“…Recently, 18 F-FDG PET/CT radiomics-based ML analysis has been applied to overcome these issues [ 75 ]. Previous studies have revealed that 18 F-FDG PET/CT radiomics-based ML analysis is useful in not only classifying tumors based on histological subtypes but also differentiating malignant lymphoma from other diseases [ 76 – 80 ].…”

“…Abenavoli et al [ 76 ] showed that the ML model with the RF algorithm using PET-radiomics had a better performance in differentiating diffuse large B-cell lymphoma (DLBCL) from Hodgkin’s lymphoma (HD) based on SUVmax (AUC: 0.87 vs. 0.78). de Jesus et al [ 77 ] reported that the ML model with the gradient boosting algorithm using PET/CT radiomics had a significantly higher AUC in distinguishing DLBCL and follicular lymphoma according to SUVmax (AUC:0.86 vs. 0.79, p < 0.01).…”

Texture analysis of 18F‐FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement

“…Such intratumor heterogeneity correlates not only with molecular subtypes, but also with biological factors that can be associated with different tumor aggressiveness and prognosis. [16][17][18] Recent research demonstrated the promising role of textural analysis from both CECT and PET imaging in predicting prognosis in HL patients. [19][20][21][22] However, no radiomic studies focused specifically on HL patients affected by bulky lymphomas.…”