Titre :

Evaluation of a convolution neural network for baseline total tumor metabolic volume on [ ¹⁸F]FDG PET in diffuse large B cell lymphoma.

Auteur(s) :

Karimdjee, Moutarza [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Delaby, Gauthier [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Huglo, Damien [Auteur]
Thérapies Lasers Assistées par l'Image pour l'Oncologie (ONCO-THAI) - U1189
Baillet, Clio [Auteur]
Groupe de Recherche sur les formes Injectables et les Technologies Associées (GRITA) - ULR 7365
Willaume, Alexandre [Auteur]
Institut Catholique de Lille [ICL]
Dujardin, Simon [Auteur]
Institut Catholique de Lille [ICL]
Bailliez, Alban [Auteur]
Institut Catholique de Lille [ICL]

Titre de la revue :

European Radiology

Nom court de la revue :

Eur Radiol

Numéro :

33

Pagination :

3386–3395

Éditeur :

Springer Link

Date de publication :

2023-01-06

ISSN :

1432-1084

Mot(s)-clé(s) :

Positron emission tomography
Artificial intelligence
Tumor volume
Lymphoma

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Objectives New PET data-processing tools allow for automatic lesion selection and segmentation by a convolution neural network using artificial intelligence (AI) to obtain total metabolic tumor volume (TMTV) and total ...
Lire la suite >Objectives New PET data-processing tools allow for automatic lesion selection and segmentation by a convolution neural network using artificial intelligence (AI) to obtain total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) routinely at the clinical workstation. Our objective was to evaluate an AI implemented in a new version of commercial software to verify reproducibility of results and time savings in a daily workflow. Methods Using the software to obtain TMTV and TLG, two nuclear physicians applied five methods to retrospectively analyze data for 51 patients. Methods 1 and 2 were fully automated with exclusion of lesions ≤ 0.5 mL and ≤ 0.1 mL, respectively. Methods 3 and 4 were fully automated with physician review. Method 5 was semi-automated and used as reference. Time and number of clicks to complete the measurement were recorded for each method. Inter-instrument and inter-observer variation was assessed by the intra-class coefficient (ICC) and Bland-Altman plots. Results Between methods 3 and 5, for the main user, the ICC was 0.99 for TMTV and 1.0 for TLG. Between the two users applying method 3, ICC was 0.97 for TMTV and 0.99 for TLG. Mean processing time (± standard deviation) was 20 s ± 9.0 for method 1, 178 s ± 125.7 for method 3, and 326 s ± 188.6 for method 5 (p < 0.05). Conclusion AI-enabled lesion detection software offers an automated, fast, reliable, and consistently performing tool for obtaining TMTV and TLG in a daily workflow.Lire moins >

Langue :

Anglais

Audience :

Internationale

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• Groupe de Recherche sur les formes Injectables et les Technologies Associées (GRITA) - ULR 7365

Date de dépôt :

2023-05-30T10:01:48Z
2023-09-13T08:36:03Z