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European Radiology

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24-08-2022 | Magnetic Resonance Imaging | Head and Neck

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

Authors: Qiang Zheng, Yiyu Zhang, Honglun Li, Xiangrong Tong, Minhui Ouyang

Published in: European Radiology | Issue 10/2022

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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Handels RL, Wolfs CA, Aalten P, Joore MA, Verhey FR, Severens JL (2014) Diagnosing Alzheimer’s disease: a systematic review of economic evaluations. Alzheimers Dement 10:225–237CrossRef

Jack CR Jr, Albert MS, Knopman DS et al (2011) Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:257–262CrossRef

Querfurth HW, LaFerla FM (2010) Alzheimer’s disease. N Engl J Med 362:329–344CrossRef

Petersen RC (2016) Mild cognitive impairment. Continuum (Minneap Minn) 22:404–418

Petersen RC, Roberts RO, Knopman DS et al (2009) Mild cognitive impairment: ten years later. Arch Neurol 66:1447–1455CrossRef

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRef

Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRef

Chaddad A, Desrosiers C, Niazi T (2018) Deep radiomic analysis of MRI related to Alzheimer’s disease. IEEE Access 6:58213–58221CrossRef

Sorensen L, Igel C, Liv Hansen N et al (2016) Early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp 37:1148–1161CrossRef

10.

Zhao K, Ding Y, Han Y et al (2020) Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease: diagnosis, longitudinal progress and biological basis. Sci Bull 65:1103–1113CrossRef

11.

Feng F, Wang P, Zhao K et al (2018) Radiomic features of hippocampal subregions in Alzheimer’s disease and amnestic mild cognitive impairment. Front Aging Neurosci 10:290CrossRef

12.

Rohlfing T, Brandt R, Menzel R, Maurer CR Jr (2004) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21:1428–1442CrossRef

13.

Coupe P, Manjon JV, Fonov V, Pruessner J, Robles M, Collins DL (2011) Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 54:940–954CrossRef

14.

Zhu H, Tang Z, Cheng H, Wu Y, Fan Y (2019) Multi-atlas label fusion with random local binary pattern features: application to hippocampus segmentation. Sci Rep 9:16839CrossRef

15.

Zhu H, Cheng H, Yang X, Fan Y, Alzheimer’s Disease Neuroimaging I (2017) Metric learning for multi-atlas based segmentation of hippocampus. Neuroinformatics 15:41–50CrossRef

16.

Hao Y, Wang T, Zhang X et al (2014) Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation. Hum Brain Mapp 35:2674–2697CrossRef

17.

Han X (2013) Learning-boosted label fusion for multi-atlas auto-segmentation. International Workshop on Machine Learning in Medical Imaging. Springer, pp 17–24

18.

Zheng Q, Wu Y, Fan Y (2018) Integrating semi-supervised and supervised learning methods for label fusion in multi-atlas based image segmentation. Front Neuroinform 12:69CrossRef

19.

Mohs RC, Rosen WG, Davis KL (1983) The Alzheimer’s disease assessment scale: an instrument for assessing treatment efficacy. Psychopharmacol Bull 19:448–450PubMed

20.

Jack CR Jr, Bernstein MA, Fox NC et al (2008) The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27:685–691CrossRef

21.

Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86:420CrossRef

22.

Zhao K, Zheng Q, Che T et al (2020) Regional radiomics similarity networks (R2SNs) in the human brain: reproducibility, small-world properties and a biological basis. Netw Neurosci:1–30

23.

Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248CrossRef

24.

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef

25.

Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762CrossRef

26.

Coroller TP, Grossmann P, Hou Y et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350CrossRef

27.

Huang X, Cheng Z, Huang Y et al (2018) CT-based radiomics signature to discriminate high-grade from low-grade colorectal adenocarcinoma. Acad Radiol 25:1285–1297CrossRef

28.

Feng Q, Ding Z (2020) MRI radiomics classification and prediction in Alzheimer’s disease and mild cognitive impairment: a review. Curr Alzheimer Res 17:297–309CrossRef

29.

Freedman LP, Venugopalan G, Wisman R (2017) Reproducibility2020: progress and priorities. F1000Res 6:604CrossRef

30.

Velasco-Annis C, Akhondi-Asl A, Stamm A, Warfield SK (2018) Reproducibility of brain MRI segmentation algorithms: empirical comparison of Local MAP PSTAPLE, FreeSurfer, and FSL-FIRST. J Neuroimaging 28:162–172CrossRef

31.

Jin D, Wang P, Zalesky A et al (2020) Grab-AD: generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer’s disease. Hum Brain Mapp 41:3379–3391CrossRef

32.

Jin D, Zhou B, Han Y et al (2020) Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease. Adv Sci (Weinh) 7:2000675CrossRef

33.

Li H, Habes M, Wolk DA et al (2019) A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimers Dement 15:1059–1070CrossRef

34.

Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C (2017) A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 155:530–548CrossRef

35.

Beheshti I, Demirel H, Alzheimer’s Disease Neuroimaging I (2016) Feature-ranking-based Alzheimer’s disease classification from structural MRI. Magn Reson Imaging 34:252–263CrossRef

36.

Zheng Q, Wu Y, Fan Y (2018) Integrating semi-supervised and supervised learning methods for label fusion in multi-atlas based image segmentation. Front Neuroinf 12:69CrossRef

Title: How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer’s disease analysis?
Authors: Qiang Zheng
Yiyu Zhang
Honglun Li
Xiangrong Tong
Minhui Ouyang
Publication date: 24-08-2022
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Alzheimer's Disease
Alzheimer's Disease
Mild Neurocognitive Disorder
Published in: European Radiology / Issue 10/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-09081-y

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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