Predicting time to treatment in follicular lymphoma on watchful waiting using baseline metabolic tumour burden

Purpose Asymptomatic patients with follicular lymphoma (FL) and a low tumour burden can be followed without initial therapy, a strategy called watchful waiting (WW). Prediction of the time to treatment (TTT) is still a challenge. We investigated the prognostic value of baseline total metabolic tumour volume (TMTV) and whole-body total lesion glycolysis (WB-TLG) to predict TTT in patients with FL on WW. Methods We conducted a retrospective study of 54 patients with FL (grade 1-3a) diagnosed between June 2013 and December 2019, staged with FDG PET/CT, and managed on WW. Median age was 62 years (range 34-85), stage was advanced (III-IV) in 57%, and FLIPI score was intermediate to high (>= 2) in 52% of the patients. Results The median TMTV and WB-TLG were 7.1 and 43.3, respectively. With a median follow-up of 59 months, 41% of patients started immuno-chemotherapy. The optimal cut-points to identify patients with TTT within 24 months were 14 for TMTV (AUC 0.70; 95% CI 51-88) and 64 for WB-TLG (AUC 0.71; 95% CI 52-89) (p < 0.005). The probability of not having started treatment within 24 months was 87% for TMTV < 14 and 53% for TMTV >= 14 (p < 0.005). TMTV was independent of the FLIPI score for TTT prediction. Patients with both FLIPI >= 2 and TMTV >= 14 had only an 18% probability of not having started treatment at 36 months, while this probability was 75% in patients with TMTV < 14. Conclusion Metabolic tumour volume parameters may add information to clinical scores to better predict TTT and better stratify patients for interventional studies.

Automated summary

This study aimed to investigate the predictive value of baseline total metabolic tumour volume (TMTV) and whole-body total lesion glycolysis (WB-TLG) for time to treatment (TTT) in patients with follicular lymphoma (FL) undergoing watchful waiting (WW). The results showed that these metabolic tumour volume parameters, in combination with clinical scores, can better predict TTT and stratify patients for interventional studies.