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Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

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Title
Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach
Author(s)
Kim, Jun Pyo; Kim, Jonghoon; Jang, Hyemin; Kim, Jaeho; Kang, Sung Hoon; Kim, Ji Sun; Lee, Jongmin; Na, Duk L.; Kim, Hee Jin; Seo, Sang Won; Hyunjin Park
Publication Date
2021-03
Journal
Scientific Reports, v.11, no.1
Publisher
Nature Research
Abstract
© 2021, The Author(s).Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71–0.74, AUC for validation = 0.68–0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.
URI
https://pr.ibs.re.kr/handle/8788114/9558
DOI
10.1038/s41598-021-86114-4
ISSN
2045-2322
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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