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Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma

Authors
 Yae Won Park  ;  Jihwan Eom  ;  Sooyon Kim  ;  Hwiyoung Kim  ;  Sung Soo Ahn  ;  Cheol Ryong Ku  ;  Eui Hyun Kim  ;  Eun Jig Lee  ;  Sun Ho Kim  ;  Seung-Koo Lee 
Citation
 JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM, Vol.106(8) : e3069-e3077, 2021-08 
Journal Title
JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM
ISSN
 0021-972X 
Issue Date
2021-08
Keywords
machine learning ; magnetic resonance imaging ; pituitary neoplasms ; prolactinoma ; radiomics
Abstract
Context: Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning.

Objective: To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients.

Design: Retrospective study.

Setting: Severance Hospital, Seoul, Korea.

Patients: A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA nonresponders) were allocated to the training (n = 141) and test (n = 36) sets. Radiomic features (n = 107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set.

Results: The ensemble classifier showed an area under the curve (AUC) of 0.81 [95% confidence interval (CI), 0.74-0.87] in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95% CI, 0.67-0.96), 77.8%, 78.6%, and 77.3%, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set.

Conclusions: Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.
Full Text
https://academic.oup.com/jcem/article/106/8/e3069/6170124
DOI
10.1210/clinem/dgab159
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
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 Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Ku, Cheol Ryong(구철룡) ORCID logo https://orcid.org/0000-0001-8693-9630
Kim, Eui Hyun(김의현) ORCID logo https://orcid.org/0000-0002-2523-7122
Kim, Hwiyoung(김휘영)
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
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184422
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