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

IF 0.2 4区 医学 Q4 PATHOLOGY
V. Micheletti , E. Marchal , M. Gambiez , L. Dercle , M.E. Meyer , X. Palard Novello , A. Girard
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引用次数: 0

Abstract

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.
人工智能辅助[18F]FDG PET/CT 定量:大 B 细胞淋巴瘤中一种节省时间的方法
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来源期刊
CiteScore
0.30
自引率
0.00%
发文量
160
审稿时长
19.8 weeks
期刊介绍: Le but de Médecine nucléaire - Imagerie fonctionnelle et métabolique est de fournir une plate-forme d''échange d''informations cliniques et scientifiques pour la communauté francophone de médecine nucléaire, et de constituer une expérience pédagogique de la rédaction médicale en conformité avec les normes internationales.
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