How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer’s disease analysis?

• Published in: European radiology Vol. 32; no. 10; pp. 6965 - 6976
• Main Authors: Zheng, Qiang, Zhang, Yiyu, Li, Honglun, Tong, Xiangrong, Ouyang, Minhui
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022; Springer Nature B.V
• Subjects: Accuracy; Alzheimer's disease; Biomarkers; Classification; Cognitive ability; Consistency; Dementia disorders; Diagnostic Radiology; Head and Neck; Hippocampus; Image segmentation; Imaging; Internal Medicine; Interventional Radiology; Machine learning; Magnetic resonance imaging; Mathematical analysis; Medical imaging; Medicine; Medicine & Public Health; Neurodegenerative diseases; Neuroimaging; Neuroradiology; Radiology; Radiomics; Ultrasound; Hippocampus segmentation; Alzheimer’s disease; Magnetic resonance imaging; Radiomic features; Machine learning
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-09081-y

Objectives Hippocampal radiomic features (HRFs) can serve as biomarkers in Alzheimer’s disease (AD). However, how different hippocampal segmentation methods affect HRFs in AD is still unknown. The aim of the study was to investigate how different segmentation methods affect HRF accuracy in AD analysis. Methods A total of 1650 subjects were identified from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). The mini-mental state examination (MMSE) and Alzheimer’s disease assessment scale (ADAS-cog13) were also adopted. After calculating the HRFs of intensity, shape, and textural features from each side of the hippocampus in structural magnetic resonance imaging (sMRI), the consistency of HRFs calculated from 7 different hippocampal segmentation methods was validated, and the performance of machine learning–based classification of AD vs. normal control (NC) adopting the different HRFs was also examined. Additional 571 subjects from the European DTI Study on Dementia database (EDSD) were to validate the consistency of results. Results Between different segmentations, HRFs showed a high measurement consistency ( R > 0.7), a high significant consistency between NC, mild cognitive impairment (MCI), and AD ( T -value plot, R > 0.8), and consistent significant correlations between HRFs and MMSE/ADAS-cog13 ( p < 0.05). The best NC vs. AD classification was obtained when the hippocampus was sufficiently segmented by primitive majority voting (threshold = 0.2). High consistent results were reproduced from independent EDSD cohort. Conclusions HRFs exhibited high consistency across different hippocampal segmentation methods, and the best performance in AD classification was obtained when HRFs were extracted by the naïve majority voting method with a more sufficient segmentation and relatively low hippocampus segmentation accuracy. Key Points • The hippocampal radiomic features exhibited high measurement/statistical/clinical consistency across different hippocampal segmentation methods. • The best performance in AD classification was obtained when hippocampal radiomics were extracted by the naïve majority voting method with a more sufficient segmentation and relatively low hippocampus segmentation accuracy.