Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin's lymphoma patients staged with FDG-PET/CT.
Adolescent
Adult
Aged
Aged, 80 and over
Artificial Intelligence
Biological Transport
/ genetics
Biopsy
Bone Marrow
/ diagnostic imaging
Child
Female
Fluorodeoxyglucose F18
/ administration & dosage
Hodgkin Disease
/ diagnosis
Humans
Male
Middle Aged
Multimodal Imaging
Musculoskeletal System
/ diagnostic imaging
Neural Networks, Computer
Positron Emission Tomography Computed Tomography
Radiopharmaceuticals
/ administration & dosage
Skeleton
/ diagnostic imaging
Young Adult
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 05 2021
17 05 2021
Historique:
received:
08
01
2021
accepted:
23
04
2021
entrez:
18
5
2021
pubmed:
19
5
2021
medline:
30
10
2021
Statut:
epublish
Résumé
To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin's lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017-2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25-0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.
Identifiants
pubmed: 34001922
doi: 10.1038/s41598-021-89656-9
pii: 10.1038/s41598-021-89656-9
pmc: PMC8128858
doi:
Substances chimiques
Radiopharmaceuticals
0
Fluorodeoxyglucose F18
0Z5B2CJX4D
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10382Références
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