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Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis

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Abstract

Background

Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task.

Methods and materials

A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools.

Results

We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively.

Conclusion

MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.

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Availability of data and materials

The data and materials that support the findings of this study are available upon reasonable request.

References

  1. Lane CA, Hardy J, Schott JM (2018) Alzheimer’s disease. Eur J Neurol 25:59–70

    CAS  PubMed  Google Scholar 

  2. Lyketsos CG, Carrillo MC, Ryan JM et al (2011) Neuropsychiatric symptoms in Alzheimer’s disease. Alzheimers Dement 7:532–539

    PubMed Central  PubMed  Google Scholar 

  3. Li X, Feng X, Sun X et al (2022) Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2019. Front Aging Neurosci 14:937486

    PubMed Central  PubMed  Google Scholar 

  4. Nichols E, Steinmetz JD, Vollset SE et al (2022) Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7:e105–e125

    Google Scholar 

  5. Srivastava S, Ahmad R, Khare SK (2021) Alzheimer’s disease and its treatment by different approaches: a review. Eur J Med Chem 216:113320

    CAS  PubMed  Google Scholar 

  6. Langa KM, Levine DA (2014) The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 312:2551–2561

    CAS  PubMed Central  PubMed  Google Scholar 

  7. Jack CR, Knopman DS, Jagust WJ et al (2013) Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 12:207–216

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Vega JN, Newhouse PA (2014) Mild cognitive impairment: diagnosis, longitudinal course, and emerging treatments. Curr Psychiatry Rep 16:1–11

    CAS  Google Scholar 

  9. Gauthier S, Reisberg B, Zaudig M et al (2006) Mild cognitive impairment. The lancet 367:1262–1270

    Google Scholar 

  10. Da X, Toledo JB, Zee J et al (2014) Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage Clin 4:164–173

    PubMed  Google Scholar 

  11. Dickerson BC, Wolk DA, Initiative AsDN (2013) Biomarker-based prediction of progression in MCI: comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau. Front Aging Neurosci 5:55

    CAS  PubMed Central  PubMed  Google Scholar 

  12. Salvatore C, Cerasa A, Castiglioni I (2018) MRI characterizes the progressive course of AD and predicts conversion to Alzheimer’s dementia 24 months before probable diagnosis. Front Aging Neurosci 10:135

    PubMed Central  PubMed  Google Scholar 

  13. Geuze E, Vermetten E, Bremner JD (2005) MR-based in vivo hippocampal volumetrics: 2. Find Neuropsychiatr Disord Mol Psychiatry 10:160–184

    CAS  Google Scholar 

  14. Sperling RA, Aisen PS, Beckett LA et al (2011) Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:280–292

    PubMed Central  PubMed  Google Scholar 

  15. Salvatore C, Battista P, Castiglioni I (2016) Frontiers for the early diagnosis of AD by means of MRI brain imaging and support vector machines. Curr Alzheimer Res 13:509–533

    CAS  PubMed  Google Scholar 

  16. Yip SS, Aerts HJ (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150

    CAS  PubMed Central  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  20. Nasrabady SE, Rizvi B, Goldman JE et al (2018) White matter changes in Alzheimer’s disease: a focus on myelin and oligodendrocytes. Acta Neuropathol Commun 6:1–10

    Google Scholar 

  21. Shao Y, Chen Z, Ming S et al (2018) Predicting the development of normal-appearing white matter with radiomics in the aging brain: a longitudinal clinical study. Front Aging Neurosci 10:393

    CAS  PubMed Central  PubMed  Google Scholar 

  22. Jack CR Jr, Bennett DA, Blennow K et al (2018) NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14:535–562

    PubMed Central  PubMed  Google Scholar 

  23. Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906

    PubMed  Google Scholar 

  24. Schueler S, Schuetz GM, Dewey M (2012) The revised QUADAS-2 tool. Ann Intern Med 156:323

    PubMed  Google Scholar 

  25. Zhong J, Hu Y, Si L et al (2021) A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 31:1526–1535

    PubMed  Google Scholar 

  26. Cheung EY, Chau AC, Tang FH et al (2022) Radiomics-based artificial intelligence differentiation of neurodegenerative diseases with reference to the volumetry. Life 12:514

    PubMed Central  PubMed  Google Scholar 

  27. Zhou K, Piao S, Liu X et al (2023) A novel cascade machine learning pipeline for Alzheimer’s disease identification and prediction. Front Aging Neurosci 14:1073909

    PubMed Central  PubMed  Google Scholar 

  28. Zheng Q, Zhang Y, Li H et al (2022) How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer’s disease analysis? Eur Radiol 32:6965–6976

    PubMed  Google Scholar 

  29. Leandrou S, Lamnisos D, Bougias H et al (2023) A cross-sectional study of explainable machine learning in Alzheimer’s disease: diagnostic classification using MR radiomic features. Front Aging Neurosci 15:1149871

    PubMed Central  PubMed  Google Scholar 

  30. Feng Q, Chen Y, Liao Z et al (2018) Corpus callosum radiomics-based classification model in Alzheimer’s disease: a case-control study. Front Neurol 9:618

    PubMed Central  PubMed  Google Scholar 

  31. 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 (Beijing) 65:1103–1113

    CAS  PubMed  Google Scholar 

  32. Nichols E, Szoeke CE, Vollset SE et al (2019) Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18:88–106

    Google Scholar 

  33. Hu C, Yu D, Sun X et al (2017) The prevalence and progression of mild cognitive impairment among clinic and community populations: a systematic review and meta-analysis. Int Psychogeriatr 29:1595–1608

    PubMed  Google Scholar 

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

    PubMed Central  PubMed  Google Scholar 

  35. Sun H, Chen Y, Huang Q et al (2018) Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis. Radiology 287:620–630

    PubMed  Google Scholar 

  36. Chaddad A, Desrosiers C, Toews M (2017) Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age. Sci Rep 7:45639

    CAS  PubMed Central  PubMed  Google Scholar 

  37. Avanzo M, Stancanello J, El Naqa IJPM (2017) Beyond imaging: the promise of radiomics. Physica Med 38:122–139

    Google Scholar 

  38. Halliday G (2017) Pathology and hippocampal atrophy in Alzheimer’s disease. Lancet Neurol 16:862–864

    PubMed  Google Scholar 

  39. Catani M, Dell’Acqua F, De Schotten MTJN et al (2013) A revised limbic system model for memory, emotion and behaviour. Neurosci Biobehav Rev 37:1724–1737

    PubMed  Google Scholar 

  40. Hondius DC, van Nierop P, Li KW et al (2016) Profiling the human hippocampal proteome at all pathologic stages of Alzheimer’s disease. Alzheimers Dement 12:654–668

    PubMed  Google Scholar 

  41. Huijbers W, Mormino EC, Schultz AP et al (2015) Amyloid-β deposition in mild cognitive impairment is associated with increased hippocampal activity, atrophy and clinical progression. Brain 138:1023–1035

    PubMed Central  PubMed  Google Scholar 

  42. Zhang J, Yu C, Jiang G et al (2012) 3D texture analysis on MRI images of Alzheimer’s disease. Brain Imaging Behav 6:61–69

    PubMed  Google Scholar 

  43. Sørensen L, Igel C, Pai A et al (2017) Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NeuroImage Clin 13:470–482

    PubMed  Google Scholar 

  44. Christensen A, Alpert K, Rogalski E et al (2015) Hippocampal subfield surface deformity in nonsemantic primary progressive aphasia. Alzheimer’s Dementia 1:14–23

    PubMed Central  PubMed  Google Scholar 

  45. Du Y, Zhang S, Fang Y et al (2022) Radiomic features of the hippocampus for diagnosing early-onset and late-onset Alzheimer’s disease. Front Aging Neurosci 13:1014

    Google Scholar 

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

    PubMed  Google Scholar 

  47. Luk CC, Ishaque A, Khan M et al (2018) Alzheimer’s disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment. Alzheimer’s Dementia 10:755–763

    PubMed Central  PubMed  Google Scholar 

  48. Ranjbar S, Velgos SN, Dueck AC et al (2019) Brain MR radiomics to differentiate cognitive disorders. J Neuropsychiatry Clin Neurosci 31:210–219

    PubMed Central  PubMed  Google Scholar 

  49. Manning EN, Macdonald KE, Leung KK et al (2015) Differential hippocampal shapes in posterior cortical atrophy patients: a comparison with control and typical AD subjects. Hum Brain Mapp 36:5123–5136

    PubMed Central  PubMed  Google Scholar 

  50. Hwang EJ, Kim HG, Kim D et al (2016) Texture analyses of quantitative susceptibility maps to differentiate Alzheimer’s disease from cognitive normal and mild cognitive impairment. Med Phys 43:4718–4728

    PubMed  Google Scholar 

  51. De Oliveira M, Balthazar M, D’abreu A et al (2011) MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer disease. Am J Neuroradiol 32:60–66

    PubMed Central  PubMed  Google Scholar 

  52. 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 10:290

    PubMed Central  PubMed  Google Scholar 

  53. Feng Q, Song Q, Wang M et al (2019) Hippocampus radiomic biomarkers for the diagnosis of amnestic mild cognitive impairment: a machine learning method. Front Aging Neurosci 11:323

    PubMed Central  PubMed  Google Scholar 

  54. Liu S, Jie C, Zheng W et al (2022) Investigation of underlying association between whole brain regions and alzheimer’s disease: a research based on an artificial intelligence model. Front Aging Neurosci 14:872530

    PubMed Central  PubMed  Google Scholar 

  55. Park HY, Shim WH, Suh CH et al (2023) Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer’s disease using a high-performance interpretable deep learning network. Eur Radiol. https://doi.org/10.1007/s00330-023-09708-8

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors appreciate Mohammad Amin Habibi for his help during the manuscript preparation.

Funding

This study received no funding.

Author information

Authors and Affiliations

  1. School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran

    Ramin Shahidi

  2. Department of Radiology, Imam Ali Hospital, North Khorasan University of Medical Science, Bojnurd, Iran

    Mansoureh Baradaran

  3. Students Research Committee, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran

    Ali Asgarzadeh

  4. Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

    Sara Bagherieh

  5. Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran

    Zohreh Tajabadi

  6. Faculty of Health, Bushehr University of Medical Sciences, Bushehr, Iran

    Akram Farhadi

  7. Department of Radiology, Mayo Clinic, Rochester, MN, USA

    Setayesh Sotoudehnia Korani

  8. Department of Radiology, Tabriz University of Medical Sciences, Tabriz, Iran

    Mohammad Khalafi

  9. School of Medicine, Tehran University of Medical Science, Tehran, Iran

    Parnian Shobeiri

  10. Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University Of Medical Sciences, Tehran, Iran

    Hamidreza Sadeghsalehi

  11. Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

    Arezoo Shafieioun

  12. School of Medicine, Qom University of Medical Sciences, Qom, Iran

    Mohammad Amin Yazdanifar

  13. Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Alabama, USA

    Aparna Singhal & Houman Sotoudeh

  14. O’Neal Comprehensive Cancer Center, UAB, The University of Alabama at Birmingham, JTN 333, 619 19th St S, Birmingham, AL, 35294, USA

    Houman Sotoudeh

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  1. Ramin Shahidi
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Contributions

I. HS and AS contributed to conception and design. II. RS provided administrative support. III. HS and AS were responsible for provision of study materials. IV. MB, AF, SSK, MK, AS, and HS collected and assembled the data. V. SB, MAY, and RS analyzed and interpreted the data. VI. ZT, PS, RS, and AA wrote the manuscript. VII. All the authors contributed to final approval of manuscript.

Corresponding author

Correspondence to Houman Sotoudeh.

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Conflict of interests

There is no conflict to declare.

Ethics approval and consent to participate

Since this is a systematic review and meta-analysis, no personal information about the patients was included in this study. Therefore, we did not ask the Ethics Committee for approval. 

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We didnt perform any intervention on human or animal.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (PNG 514 KB)—Online Resource 1. Critical appraisal based on the QUADAS-2 tool - part 1.

Supplementary file 2 (PNG 166 KB)—Online Resource 2. Critical appraisal based on the QUADAS-2 tool - part 2.

Supplementary file 3 (PNG 39 KB)—Online Resource 3. Critical appraisal based on the RQS tool.

Supplementary file 4 (DOCX 15 KB)—Online Resource 4. Basic adherence rate according to the six key domains.

40520_2023_2565_MOESM5_ESM.docx

Supplementary file 5 (DOCX 17 KB)—Online Resource 5. Details of quality assessment by Radiomics Quality Score (RQS) of all included studies.

40520_2023_2565_MOESM6_ESM.docx

Supplementary file 6 (DOCX 219 KB)—Online Resource 6. Heterogeneity of (A) specificity and (B) precision of differentiating AD from MCI.

40520_2023_2565_MOESM7_ESM.docx

Supplementary file 7 (DOCX 362 KB)—Online Resource 7. Heterogeneity of (A) Sensitivity, (B) Specificity, and (C) Precision of differentiating AD from CN.

40520_2023_2565_MOESM8_ESM.docx

Supplementary file 8 (DOCX 324 KB)—Online resource 8. Heterogeneity of (A) Sensitivity, (B) Specificity, and (C) Precision of differentiating CN from MCI.

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Shahidi, R., Baradaran, M., Asgarzadeh, A. et al. Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis. Aging Clin Exp Res 35, 2333–2348 (2023). https://doi.org/10.1007/s40520-023-02565-x

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