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A radiomic signature based on magnetic resonance imaging to determine adrenal Cushing's syndrome
non-functioning adrenal incidentalomas
adrenal Cushing’s syndrome
magnetic resonance imaging
machine learning
Bibliogr. s. e46
Purpose: The aim of this study was to develop radiomics signature-based magnetic resonance imaging (MRI) to determine adrenal Cushing’s syndrome (ACS) in adrenal incidentalomas (AI). Material and methods: A total of 50 patients with AI were included in this study. The patients were grouped as nonfunctional adrenal incidentaloma (NFAI) and ACS. The lesions were segmented on unenhanced T1-weighted (T1W) in-phase (IP) and opposed-phase (OP) as well as on T2-weighted (T2-W) 3-Tesla MRIs. The LASSO regression model was used for the selection of potential predictors from 111 texture features for each sequence. The radiomics scores were compared between the groups. Results: The median radiomics score in T1W-Op for the NFAI and ACS were -1.17 and -0.17, respectively (p < 0.001). Patients with ACS had significantly higher radiomics scores than NFAI patients in all phases (p < 0.001 for all). The AUCs for radiomics scores in T1W-Op, T1W-Ip, and T2W were 0.862 (95% CI: 0.742-0.983), 0.892 (95% CI: 0.774-0.999), and 0.994 (95% CI: 0.982-0.999), respectively. Conclusion: The developed MRI-based radiomic scores can yield high AUCs for prediction of ACS.
dc.abstract.en | Purpose: The aim of this study was to develop radiomics signature-based magnetic resonance imaging (MRI) to determine adrenal Cushing’s syndrome (ACS) in adrenal incidentalomas (AI). Material and methods: A total of 50 patients with AI were included in this study. The patients were grouped as nonfunctional adrenal incidentaloma (NFAI) and ACS. The lesions were segmented on unenhanced T1-weighted (T1W) in-phase (IP) and opposed-phase (OP) as well as on T2-weighted (T2-W) 3-Tesla MRIs. The LASSO regression model was used for the selection of potential predictors from 111 texture features for each sequence. The radiomics scores were compared between the groups. Results: The median radiomics score in T1W-Op for the NFAI and ACS were -1.17 and -0.17, respectively (p < 0.001). Patients with ACS had significantly higher radiomics scores than NFAI patients in all phases (p < 0.001 for all). The AUCs for radiomics scores in T1W-Op, T1W-Ip, and T2W were 0.862 (95% CI: 0.742-0.983), 0.892 (95% CI: 0.774-0.999), and 0.994 (95% CI: 0.982-0.999), respectively. Conclusion: The developed MRI-based radiomic scores can yield high AUCs for prediction of ACS. | pl |
dc.contributor.author | Piskin, Ferhat Can | pl |
dc.contributor.author | Akkus, Gamze | pl |
dc.contributor.author | Yucel, Sevinc Puren | pl |
dc.contributor.author | Akbas, Bisar | pl |
dc.contributor.author | Odabası, Fulya | pl |
dc.date.accessioned | 2023-05-04T08:04:03Z | |
dc.date.available | 2023-05-04T08:04:03Z | |
dc.date.issued | 2023 | pl |
dc.date.openaccess | 0 | |
dc.description.accesstime | w momencie opublikowania | |
dc.description.additional | Bibliogr. s. e46 | pl |
dc.description.physical | e41-e46 | pl |
dc.description.version | ostateczna wersja wydawcy | |
dc.description.volume | 88 | pl |
dc.identifier.doi | 10.5114/pjr.2023.124435 | pl |
dc.identifier.eissn | 1899-0967 | pl |
dc.identifier.issn | 1733-134X | pl |
dc.identifier.uri | https://ruj.uj.edu.pl/xmlui/handle/item/311021 | |
dc.language | eng | pl |
dc.language.container | eng | pl |
dc.rights | Udzielam licencji. Uznanie autorstwa - Użycie niekomercyjne - Bez utworów zależnych 4.0 Międzynarodowa | * |
dc.rights.licence | CC-BY-NC-ND | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.pl | * |
dc.share.type | otwarte czasopismo | |
dc.subject.en | non-functioning adrenal incidentalomas | pl |
dc.subject.en | adrenal Cushing’s syndrome | pl |
dc.subject.en | magnetic resonance imaging | pl |
dc.subject.en | machine learning | pl |
dc.subtype | Article | pl |
dc.title | A radiomic signature based on magnetic resonance imaging to determine adrenal Cushing's syndrome | pl |
dc.title.journal | Polish Journal of Radiology | pl |
dc.type | JournalArticle | pl |
dspace.entity.type | Publication |
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