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Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas

 Yae Won Park  ;  Yunjun Kang  ;  Sung Soo Ahn  ;  Cheol Ryong Ku  ;  Eui Hyun Kim  ;  Se Hoon Kim  ;  Eun Jig Lee  ;  Sun Ho Kim  ;  Seung-Koo Lee 
 PITUITARY, Vol.23(6) : 691-700, 2020-12 
Journal Title
Issue Date
Acromegaly ; Granulation pattern ; Growth hormone-secreting pituitary adenoma ; Magnetic resonance imaging ; Pituitary neoplasms ; Radiomics
Purpose: To investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients. Methods: Sixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC). Results: Four significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738-0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447-0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523-0.759], P = 0.037). Conclusion: Radiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Ku, Cheol Ryong(구철룡) ORCID logo https://orcid.org/0000-0001-8693-9630
Kim, Sun Ho(김선호) ORCID logo https://orcid.org/0000-0003-0970-3848
Kim, Se Hoon(김세훈) ORCID logo https://orcid.org/0000-0001-7516-7372
Kim, Eui Hyun(김의현) ORCID logo https://orcid.org/0000-0002-2523-7122
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Lee, Eun Jig(이은직) ORCID logo https://orcid.org/0000-0002-9876-8370
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