Two-Year Event-Free Survival Prediction in DLBCL Patients Based on In Vivo Radiomics and Clinical Parameters - Ritter, Zsombor; Papp, László; Zámbó, Katalin; Tóth, Zoltán; Dezső, Dániel; Veres, Dániel Sándor; Máthé, Domokos; Budán, Ferenc; Karádi, Éva; Balikó, Anett; Pajor, László; Szomor, Árpád; et al. (Frontiers in Oncology, 2022)
Purpose: For the identification of high risk patients in diffuse large B-cell lymphoma we investigated the prognostic significance of in vivo radiomics derived from baseline [18F]FDG PET/CT and clinical parameters. Methods: Pre-treatment [18F]FDG PET/CT scans of 85 patients diagnosed with DLBCL were assessed. The scans were carried out in two clinical centers. Two-year event-free survival was defined. After delineation of lymphoma lesions conventional PET parameters and in vivo radiomics were extracted. For 2-year EFS prognosis assessment, Center 1 dataset was utilized as training set and underwent automated machine learning analysis. The dataset of Center 2 was utilized as independent test set to validate the established predictive model built by the dataset of Center 1. Results: The automated machine learning analysis of the Center 1 dataset revealed the most important features for building 2-years event free survival are: max diameter, neighbor gray tone difference matrix (NGTDM) busyness, Total lesion glycolysis, Total metabolic tumor volume and NGTDM Coarseness. The predictive model built on Center 1 dataset yielded 79% sensitivity, 83% specificity, 69% positive predictive value, 89% negative predictive value and 0.85 AUC in by evaluating the Center 2 dataset. Conclusion: Based on our dual-center retrospective analysis, predicting 2-year event-free survival built on imaging features is feasible with a high-performance utilizing automated machine learning.