Assessment of quantitative staging PET/computed tomography parameters using machine learning for early detection of progression in diffuse large B-cell lymphoma.

Objective: This study aimed to investigate the role of volumetric and dissemination parameters obtained from pretreatment 18-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) in predicting progression/relapse in patients with diffuse large B-cell lymphoma (DLBCL) with machine learning algorithms.

Methods: Patients diagnosed with DLBCL histopathologically, treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone, and followed for at least 1 year were reviewed retrospectively. Quantitative parameters such as tumor volume [total metabolic tumor volume (tMTV)], tumor burden [total lesion glycolysis (tTLG)], and the longest distance between two tumor foci ( Dmax ) were obtained from PET images with a standard uptake value threshold of 4.0. The MTV obtained from the volume of interest with the highest volume was noted as metabolic bulk volume (MBV). By analyzing the patients' PET parameters and clinical information with machine learning algorithms, models that attempt to predict progression/recurrence over 1 year were obtained.

Results: Of the 90 patients included, 16 had progression within 1 year. Significant differences were found in tMTV, tTLG, MBV, and Dmax values between patients with and without progression. The area under curve (AUC) of the model obtained with clinical data was 0.701. While a model with an AUC of 0.871 was obtained with a random forest algorithm using PET parameters, the model obtained with the Naive Bayes algorithm including clinical data in PET parameters had an AUC of 0.838.

Conclusion: Using quantitative parameters derived from staging PET with machine learning algorithms may enable us to detect early progression in patients with DLBCL and improve early risk stratification and guide treatment decisions in these patients.