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Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

ABSTRACT: The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using demographics, T1 MRI features (volume, cortical thickness and radiomics), and diffusion-tensor image to distinguish amyloid-beta positivity on Florbetaben PET. We compared the performance of each algorithm based on the MRI features used. The study population included 72 patients with MCI in the amyloid-beta-negative group and 114 patients with MCI in the amyloid-beta-positive group. The machine learning algorithm using T1 volume performed better than that using only clinical information (mean area under the curve [AUC]: 0.73 vs. 0.69, p < 0.001). The machine learning algorithm using T1 volume showed better performance than that using cortical thickness (mean AUC: 0.73 vs. 0.68, p < 0.001) or texture (mean AUC: 0.73 vs. 0.71, p = 0.002). The performance of the machine learning algorithm using fractional anisotropy in addition to T1 volume was not better than that using T1 volume alone (mean AUC: 0.73 vs. 0.73, p = 0.60). Among MRI features, T1 volume was the best predictor of amyloid PET positivity. Radiomics or diffusion-tensor images did not provide additional benefits.

SUBMITTER: Jo S

PROVIDER: S-EPMC10275931 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

Jo Sungyang S Lee Hyunna H Kim Hyung-Ji HJ Suh Chong Hyun CH Kim Sang Joon SJ Lee Yoojin Y Roh Jee Hoon JH Lee Jae-Hong JH

Scientific reports 20230616 1

The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using dem ...[more]

PMID: 37328578

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Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

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Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

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