Baseline PET features to predict prognosis in primary mediastinal B cell lymphoma: a comparative analysis of different methods for measuring baseline metabolic tumour volume

摘要

PurposeThis study assessed the performance of four different methods for the estimation of metabolic tumour volume (MTV) in primary mediastinal B cell lymphoma (PMBCL).MethodMTV was estimated using either a region growing automatic software program (RG) or a fixed threshold (FT) segmentation algorithm with the three most common cut-offs proposed in the literature (i.e., 25% and 41% of the SUVmax and SUV value 2.5). We compared these four methods using phantoms that simulated different set-ups of the main imaging characteristics of PMBCL (volume, shape, 18-FDG uptake and intra-lesion distribution) and assessed their performance in 103 PMBCL patients enrolled in the International Extranodal Lymphoma Study Group-26 (IELSG-26) study.ResultsThere was good correlation between MTV values estimated in vitro and in vivo using the different methods. The 25% FT cut-off (FT25%) provided the most accurate MTV evaluation in the phantoms. The cut-off at SUV 2.5 (FT2.5) resulted in MTV overestimation that particularly increased with high SUV values. The 41% cut-off (FT41%) showed MTV underestimation that was more evident when there were high levels of heterogeneity in tracer distribution. Shape of the lesion did not affect MTV computation. The RG algorithm provided a systematic slight MTV underestimation without significant changes due to lesion characteristics. We observed analogous trends for the MTV estimation in patients, with very different derived thresholds for the four methods. Optimal cut-offs for predicting progression-free survival (PFS) ranged from 213 to 831ml. All methods predicted PFS with similar negative predictive values (94-95%) but different positive predictive values (23-45%).ConclusionsThe different methods result in significantly different MTV cut-off values. All allow risk stratification in PMBCL, but FT25% showed the best capacity to predict disease progression in the patient cohort and provided the best accuracy in the phantom model.