ISSN: 1899-0967
Polish Journal of Radiology
Established by prof. Zygmunt Grudziński in 1926 Sun
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1/2023
vol. 88
 
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Urogenital radiology
abstract:
Original paper

A radiomic signature based on magnetic resonance imaging to determine adrenal Cushing’s syndrome

Ferhat Can Piskin
1
,
Gamze Akkus
1
,
Sevinc Puren Yucel
1
,
Bisar Akbas
1
,
Fulya Odabası
1

1.
Cukurova University Medical Faculty, Adana, Turkey
© Pol J Radiol 2023; 88: e41-e46
Online publish date: 2023/01/23
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Introduction
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 non-functional 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.

Conclusions
The developed MRI-based radiomic scores can yield high AUCs for prediction of ACS.

keywords:

non-functioning adrenal incidentalomas, adrenal Cushing’s syndrome, magnetic resonance imaging, machine learning




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