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- Date2021-02-23 16:08
- Update2021-02-23 16:08
- CountersignatureDivision of Research Planning
Scientific Reports, 2020.10(1), 1-10, DOI: https://doi.org/10.1038/s41598-020-75664-8.
Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers
Young Hee Jung, Hee Jin Kim; Duk L. Na; Hyun Jeong Han; Hyemin Jang; Sang Won Seo
Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, and it provides insights into the underlying vascular pathology (CAA vs. hypertensive angiopathy) to facilitate prognostic prediction and appropriate treatment decisions. In this study, we applied two interpretable machine learning algorithms, gradient boosting machine (GBM) and random forest (RF), to predict Aβ PET positivity in patients with CAA MRI markers. In the GBM algorithm, the number of lobar cerebral microbleeds (CMBs), deep CMBs, lacunes, CMBs in dentate nuclei, and age were ranked as the most influential to predict Aβ positivity. In the RF algorithm, the absence of diabetes was additionally chosen. Cut-off values of the above variables predictive of Aβ positivity were as follows: (1) the number of lobar CMBs > 16.4(GBM)/14.3(RF), (2) no deep CMBs(GBM/RF), (3) the number of lacunes > 7.4(GBM/RF), (4) age > 74.3(GBM)/64(RF), (5) no CMBs in dentate nucleus(GBM/RF). The classification performances based on the area under the receiver operating characteristic curve were 0.83 in GBM and 0.80 in RF. Our study demonstrates the utility of interpretable machine learning in the clinical setting by quantifying the relative importance and cutoff values of predictive variables for Aβ positivity in patients with suspected CAA markers.
- DOI: https://doi.org/10.1038/s41598-020-75664-8.
- ISBN or ISSN: 2045-2322
- 본 연구는 질병관리본부 연구개발과제연구비를 지원받아 수행되었습니다.
- This research was supported by a fund by Research of Korea Centers for Disease Control and Prevention.