Original Research ARTICLE
Hippocampus radiomic biomarkers for the diagnosis of amnestic mild cognitive impairment: A machine learning method
- 1Hangzhou First People's Hospital, China
- 2Zhejiang Provincial People's Hospital, China
- 3GE Healthcare Life Sciences (China), China
- 4University of North Carolina at Chapel Hill, United States
Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building the classification models in aMCI diagnosis.
Methods: For this target, we recruited 42 subjects with aMCI and 44 normal controls (NC). The right and left hippocampus were segmented respectively for each subject using an efficient learning-based method. Then, the radiomic analysis was applied to calculate and select the radiomic features. Finally, two logistic regression models were built based on the selected features obtained from the right and left hippocampus correspondingly.
Results: There were 385 features derived after calculation and 4 features remained after feature selection from each group of data. The area under the receiver operating characteristic curve, specificity, sensitivity, positive predictive value, negative predictive value, precision, recall, and F-Score of the classification evaluation index of the right hippocampus logistic regression model were 0.76, 0.71, 0.69, 0.69, 0.71, 0.69, 0.69, and 0.69, and those of the left hippocampus model were 0.79, 0.71, 0.54, 0.64, 0.63, 0.64, 0.54, and 0.58, respectively.
Conclusion: Results demonstrate the potential hippocampal radiomic biomarkers are valid for the aMCI diagnosis. The MRI-based radiomic analysis, with further improvement and validation, can be used to identify patients with aMCI and guide the individual treatment.
Keywords: Alzheimer’s disease, amnestic mild cognitive impairment, Magnetic Resonance Imaging, Hippocampus, Radiomics, machine learning
Received: 13 Jun 2019;
Accepted: 06 Nov 2019.
Copyright: © 2019 Feng, Song, Wang, Pang, Liao, Jiang, Shen and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Zhongxiang Ding, Hangzhou First People's Hospital, Hangzhou, Zhejiang Province, China, email@example.com