Abstract
BACKGROUND: Patients with follicular lymphoma (FL) who experience progression of disease within 24 months (POD24) of receiving first-line therapy had a significantly poorer prognosis than that without early progression. Due to the established prognostic relevance of positron emission tomography/computed tomography (PET/CT) parameters in FL and their clinical accessibility, we aimed to investigate the predictive role of PET/CT metabolism and dissemination parameters in POD24 for FL. METHODS: The POD24 status of 155 patients who underwent PET/CT examinations at initial diagnosis was evaluated. Various baseline characteristics were collected, along with PET/CT-derived parameters, including the maximum tumor dissemination (Dmax), maximum standardized uptake (SUVmax) value, total metabolic tumor volume (TMTV), and total lesion glycolysis (TLG). A Cox proportional regression analysis was used to identify potential risk predictors of POD24. Receiver operating characteristic (ROC) curves were used to define the optimal cut-off values. RESULTS: In our cohort, POD24 was observed in 21 (13.5%) FL patients. The univariate and multivariate Cox regression analyses revealed that elevated lactate dehydrogenase (LDH) was a significant predictor of POD24. Additionally, survival analyses based on the cut-off values showed that the risk of POD24 was significantly increased in patients with a Dmax >64.24 cm, SUVmax >11.23, TMTV >144.16 cm(2), and TLG >586.79 g. Further, a Dmax >64.24 cm, a TMTV >144.16 cm(2), and elevated LDH were selected for inclusion in a risk model [concordance index (C-index) =0.82], and the patients were divided into three risk groups, in which the rates of POD24 were 1.69%, 10.42%, and 35.29%, respectively (P<0.001). Our model exhibited excellent performance in terms of both the C-index and ROC curve analysis, surpassing the performance of models commonly used in the field. CONCLUSIONS: PET/CT parameters have prognostic value for POD24 in FL. The risk model, which combined PET/CT parameters with clinical indicators, could improve risk stratification and help guide therapeutic decisions.