Deep-Learning Assessed Muscular Hypodensity Independently Predicts Mortality in DLBCL Patients Younger Than 60 Years.
U-NET
convolutional neural network
diffuse large B-cell lymphoma
muscle depletion
muscle hypodensity
sarcopenia
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
07 Sep 2021
07 Sep 2021
Historique:
received:
18
06
2021
revised:
27
08
2021
accepted:
03
09
2021
entrez:
28
9
2021
pubmed:
29
9
2021
medline:
29
9
2021
Statut:
epublish
Résumé
Muscle depletion (MD) assessed by computed tomography (CT) has been shown to be a predictive marker in solid tumors, but has not been assessed in non-Hodgkin's lymphomas. Despite software improvements, MD measurement remains highly time-consuming and cannot be used in clinical practice. This study reports the development of a Deep-Learning automatic segmentation algorithm (DLASA) to measure MD, and investigate its predictive value in a cohort of 656 diffuse large B cell lymphoma (DLBCL) patients included in the GAINED phase III prospective trial (NCT01659099). After training on a series of 190 patients, the DLASA achieved a Dice coefficient of 0.97 ± 0.03. In the cohort, the median skeletal muscle index was 50.2 cm
Sections du résumé
BACKGROUND
BACKGROUND
Muscle depletion (MD) assessed by computed tomography (CT) has been shown to be a predictive marker in solid tumors, but has not been assessed in non-Hodgkin's lymphomas. Despite software improvements, MD measurement remains highly time-consuming and cannot be used in clinical practice.
METHODS
METHODS
This study reports the development of a Deep-Learning automatic segmentation algorithm (DLASA) to measure MD, and investigate its predictive value in a cohort of 656 diffuse large B cell lymphoma (DLBCL) patients included in the GAINED phase III prospective trial (NCT01659099).
RESULTS
RESULTS
After training on a series of 190 patients, the DLASA achieved a Dice coefficient of 0.97 ± 0.03. In the cohort, the median skeletal muscle index was 50.2 cm
Identifiants
pubmed: 34572728
pii: cancers13184503
doi: 10.3390/cancers13184503
pmc: PMC8466314
pii:
doi:
Banques de données
ClinicalTrials.gov
['NCT01659099']
Types de publication
Journal Article
Langues
eng
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