Prognostic value of baseline total metabolic tumour volume of 18F-FDG PET/CT imaging in patients with angioimmunoblastic T-cell lymphoma

Abstract

Abstract Purpose The aim of this study was to explore the prognostic value of baseline metabolic parameters of 18F-FDG PET/CT imaging in patients with angioimmunoblastic T-cell lymphoma (AITL). Materials and methods Fifty-six AITL patients (average age 64.0 ± 1.3 years) diagnosed pathologically from August 2009 to August 2019 were enrolled in this retrospective study. The total metabolic tumour volume (TMTV), total lesion glycolysis (TLG), maximum standardized uptake value (SUVmax), and correlated clinical characteristics were collected and analysed. TMTV was computed with the 41% SUVmax threshold method. The chi-square test or Fisher’s exact probability method was used to compare clinical characteristics. Kaplan–Meier curves were used to describe progression-free survival (PFS) and overall survival (OS). The log-rank test was used to analyse the difference within groups. The statistically significant factors in the univariate regression analysis were incorporated into the Cox risk proportional regression model for multivariate survival analysis. Results The TMTV cut-off value was 514.6 cm3 from the ROC curve analysis. Forty (71.4%) patients progressed and 31 (55.4%) patients died within a median follow-up time of 19.1 (interquartile range 7.8–34.6) months. The 1-year and 3-year PFS rates were 42.9% and 30.1%, and the 3-year and 5-year OS rates were 45.9% and 34.4%, respectively. Univariate survival analysis showed that high TMTV and TLG may be the factors contributing to poor PFS and OS. Multivariate analysis showed that TMTV and prognostic index for T-cell lymphoma (PIT) were independent parameters for PFS and OS in AITL patients. TMTV, combined with PIT, may have better risk stratification performance than TMTV alone. Conclusions Baseline TMTV and PIT were independent prognostic predictors in AITL patients. The combination of TMTV and PIT can facilitate prognostic stratification and contribute to personalized therapy.