Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan.
adrenal incidentalomas
cortisol secreting adrenal mass
differential diagnosis of adrenal mass
non-secreting adrenal mass
radiomics
subclinical hypercortisolism
texture analysis
Journal
Frontiers in endocrinology
ISSN: 1664-2392
Titre abrégé: Front Endocrinol (Lausanne)
Pays: Switzerland
ID NLM: 101555782
Informations de publication
Date de publication:
2022
2022
Historique:
received:
10
02
2022
accepted:
22
04
2022
entrez:
5
7
2022
pubmed:
6
7
2022
medline:
7
7
2022
Statut:
epublish
Résumé
New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed
Identifiants
pubmed: 35784576
doi: 10.3389/fendo.2022.873189
pmc: PMC9248203
doi:
Substances chimiques
Hydrocortisone
WI4X0X7BPJ
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
873189Informations de copyright
Copyright © 2022 Maggio, Messina, D’Arrigo, Maccagno, Lardo, Palmisano, Poggi, Monti, Matarazzo, Laghi, Pugliese and Stigliano.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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