AI-assisted [18F]FDG PET/CT quantification: A time-saving approach in large B-cell lymphoma

Purpose

To assess if AI-assisted segmentation on [18F]FDG PET/CT significantly reduces time in quantifying Total Metabolic Tumor Volume (TMTV) and the greatest distance between two lesions (Dmax) in large B-cell lymphoma (LBCL) patients.

Methods

A retrospective study at two institutions included 56 LBCL patients (Ann-Arbor staging: 14% with no disease, 25% stage I-II, 61% stage III-IV). Two methods were compared: AI-assisted segmentation (Pionus® v1.1.0, PAIRE, Paris) automatically identified lesions with expert correction of AI errors, while expert-led segmentation was fully manual. Both methods used a 41% SUVmax threshold.

Results

AI-assisted segmentation was 41% faster, saving an average of 104 seconds per patient (152 ± 134 seconds vs. 255 ± 270 seconds, P < 0.001), with time saved correlating with TMTV (r = 0.67, P < 0.001). No significant differences in TMTV or Dmax were found between methods. Intraclass correlation coefficients were high for both TMTV (0.91 and 0.95) and Dmax (0.97 and 0.95), with strong inter-reader agreement.

Conclusion

AI-assisted segmentation significantly reduces time for TMTV and Dmax measurement in LBCL patients, while maintaining high reliability and inter-reader agreement compared to manual methods.