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

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Abstract

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.

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Abbreviations

ACC:

Accuracy

AD:

Alzheimer’s disease

ADAS-cog13:

Alzheimer’s disease assessment scale

ADNI:

The Alzheimer’s Disease Neuroimaging Initiative database

ANTs:

The Advanced Normalization Tools

AUC:

The area under the ROC curve

EDSD:

The European DTI Study on Dementia database

HRFs:

Hippocampal radiomic features

ICC:

Intraclass correlation coefficient

LLL:

Local label learning

MAIS:

Multi-atlas image segmentation

MCI:

Mild cognitive impairment

ML:

Metric learning

MMSE:

Mini-mental state examination

MV:

Majority voting

NC:

Normal control

NLP:

Nonlocal patch

RF:

Random forest

RF-SSLP:

Random forest-semi-supervised label propagation

RLBP:

Random local binary pattern

ROC:

Receiver operating characteristic

SEN:

Sensitivity

SPE:

Specificity

SVM:

Support vector machine

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Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The ADNI was funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd, and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provided funds to support ADNI clinical sites in Canada. Private sector contributions were facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization was the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data were disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Funding

This study has received funding by the National Natural Science Foundation of China (61802330, 61802331, 61801415).

Author information

Authors and Affiliations

  1. School of Computer and Control Engineering, Yantai University, No30, Qingquan Road, Laishan District, Yantai, 264005, Shandong, China

    Qiang Zheng, Yiyu Zhang & Xiangrong Tong

  2. Departments of Medical Oncology and Radiology, Affiliated Yantai Yuhuangding Hospital of Qingdao University Medical College, Yantai, 264000, China

    Honglun Li

  3. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA

    Minhui Ouyang

Authors
  1. Qiang Zheng
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  5. Minhui Ouyang
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Corresponding author

Correspondence to Qiang Zheng.

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Guarantor

The scientific guarantor of this publication is Qiang Zheng from Yantai University, the lead author of the study.

Conflict of interest

The authors declare no competing interests.

Statistics and biometry

One of the authors (Minhui Ouyang, the last author, Children’s Hospital of Philadelphia) has significant statistical expertise.

Informed consent

Informed written consent was obtained from all subjects (patients) in this study.

Ethics approval

The study was approved by the institutional review boards of all the participating institutions.

Study subjects or cohorts overlap:

It should be noted that 990 of the 1650 subjects in ADNI cohort have been previously reported (Zhao et al, Jin et al). These prior studies focused on whether the hippocampal radiomics feature (Zhao et al) or 3D attention network model (Jin et al) can distinguish AD from NC, whereas the present study aims to test how segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer’s disease analysis.

Zhao K, Ding Y, Han Y, et al Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease: diagnosis, longitudinal progress and biological basis. Science Bulletin. 2020;65(13):1103-13.

Jin D, Wang P, Zalesky A, et al Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer’s Disease. Hum Brain Mapp. 2020;41(12):3379-91.

Methodology

• retrospective

• case-control study

• multicenter study

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Zheng, Q., Zhang, Y., Li, H. et al. How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer’s disease analysis?. Eur Radiol 32, 6965–6976 (2022). https://doi.org/10.1007/s00330-022-09081-y

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