Accuracy comparison of artificial intelligence-assisted diagnosis systems based on 18F-FDG PET/CT and structural MRI in the diagnosis of Alzheimer's disease: a meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

ZHAO Tailiang ¹ , WANG Bingbing ¹ , LIANG Wei ¹ , CHENG Sen ¹ , GAO Haidong ¹ , WANG Jianye ¹ ,  SHOU Jixin ^1,2

1. Neurosurgery Department, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, P. R. China;
2. Zhengzhou University, Zhengzhou 450052, P. R. China;

Corresponding author：

SHOU Jixin, Email: zdwfu9666@163.com

Keywords：

Artificial intelligence; Alzheimer's disease; Positron emission tomography; Magnetic resonance imaging; Meta-analysis

DOI：

10.7507/1672-2531.202405140

Video：

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Objective To conduct a meta-analysis comparing the accuracy of artificial intelligence (AI)-assisted diagnostic systems based on 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) and structural MRI (sMRI) in the diagnosis of Alzheimer's disease (AD). Methods Original studies dedicated to the development or validation of AI-assisted diagnostic systems based on 18F-FDG PET/CT or sMRI for AD diagnosis were retrieved from the Web of Science, PubMed, and Embase databases. Studies meeting the inclusion criteria were collected, and the risk of bias and clinical applicability of the included studies were assessed using the PROBAST checklist. The pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve (AUC) were calculated using a bivariate random-effects model. Results Twenty-six studies met the inclusion criteria, yielding a total of 38 2×2 contingency tables related to diagnostic performance. Specifically, 24 contingency tables were based on 18F-FDG PET/CT to distinguish AD patients from normal cognitive (NC) controls, and 14 contingency tables were based on sMRI for the same purpose. The meta-analysis results showed that for 18F-FDG PET/CT, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 89% (95%CI 88% to 91%), 93% (95%CI 91% to 94%), and 0.96 (95%CI 0.93 to 0.97), respectively. For sMRI, the AI-assisted diagnostic systems had a pooled sensitivity, specificity, and SROC-AUC of 88% (95%CI 85% to 90%), 90% (95%CI 87% to 92%), and 0.94 (95%CI 0.92 to 0.96), respectively. Conclusion AI-assisted diagnostic systems based on either 18F-FDG PET/CT or sMRI demonstrated similar performance in the diagnosis of AD, with both showing high accuracy.

Citation： ZHAO Tailiang, WANG Bingbing, LIANG Wei, CHENG Sen, GAO Haidong, WANG Jianye, SHOU Jixin. Accuracy comparison of artificial intelligence-assisted diagnosis systems based on 18F-FDG PET/CT and structural MRI in the diagnosis of Alzheimer's disease: a meta-analysis. Chinese Journal of Evidence-Based Medicine, 2024, 24(12): 1411-1418. doi: 10.7507/1672-2531.202405140 Copy

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9.	Braak H, Braak E. Evolution of neuronal changes in the course of Alzheimer's disease. J Neural Transm Suppl, 1998, 53: 127-40.
10.	Schuff N, Woerner N, Boreta L, et al. MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers. Brain, 2009, 132(Pt 4): 1067-1077.
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14.	Guo C, Ashrafian H, Ghafur S, et al. Challenges for the evaluation of digital health solutions-a call for innovative evidence generation approaches. NPJ Digit Med, 2020, 3: 110.
15.	Jackson D, Turner R. Power analysis for random-effects meta-analysis. Res Synth Methods, 2017, 8(3): 290-302.
16.	McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA, 2018, 319(4): 388-396.
17.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
18.	Um O, Cn O. Evaluating measures of indicators of diagnostic test performance: fundamental meanings and formulars. J Biometri Biostati, 2012, 3(1): 258.
19.	Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ, 2003, 327(7414): 557-560.
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21.	Zhang D, Wang Y, Zhou L, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage, 2011, 55(3): 856-867.
22.	Yun HJ, Kwak K, Lee JM, et al. Multimodal discrimination of Alzheimer's disease based on regional cortical atrophy and hypometabolism. PLoS One, 2015, 10(6): e0129250.
23.	Vemuri P, Gunter JL, Senjem ML, et al. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage, 2008, 39(3): 1186-1197.
24.	Tosun D, Schuff N, Rabinovici GD, et al. Diagnostic utility of ASL-MRI and FDG-PET in the behavioral variant of FTD and AD. Ann Clin Transl Neurol, 2016, 3(10): 740-751.
25.	Sayeed A, Petrou M, Spyrou N, et al. Diagnostic features of Alzheimer's disease extracted from PET sinograms. Phys Med Biol, 2002, 47(1): 137-148.
26.	Pan X, Adel M, Fossati C, et al. Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer's disease. Comput Methods Programs Biomed, 2019, 180: 105027.
27.	Padilla P, López M, Górriz JM, et al. NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease. IEEE Trans Med Imaging, 2012, 31(2): 207-216.
28.	Ni YC, Tseng FP, Pai MC, et al. Detection of Alzheimer's disease using ECD SPECT images by transfer learning from FDG PET. Ann Nucl Med, 2021, 35(8): 889-899.
29.	Magnin B, Mesrob L, Kinkingnéhun S, et al. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. Neuroradiology, 2009, 51(2): 73-83.
30.	Lu D, Popuri K, Ding GW, et al. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease. Med Image Anal, 2018, 46: 26-34.
31.	Liu M, Cheng D, Yan W, et al. Classification of Alzheimer's disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform, 2018, 12: 35.
32.	Li R, Perneczky R, Yakushev I, et al. Gaussian mixture models and model selection for [18f] fluorodeoxyglucose positron emission tomography classification in Alzheimer's disease. PLoS One, 2015, 10(4): e0122731.
33.	Lerch JP, Pruessner J, Zijdenbos AP, et al. Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. Neurobiol Aging, 2008, 29(1): 23-30.
34.	Kim HW, Lee HE, Oh K, et al. Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography. Biomed Eng Online, 2020, 19(1): 70.
35.	Katako A, Shelton P, Goertzen AL, et al. Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia. Sci Rep, 2018, 8(1): 13236.
36.	Ismail WN, Rajeena PPF, Ali MAS. A meta-heuristic multi-objective optimization method for Alzheimer's disease detection based on multi-modal data. Mathematics, 2023, 11(4): 764.
37.	Illán J, Górriz J. 18F-FDG-FDG PET imaging analysis for computer aided Alzheimer's diagnosis. Information Sciences, 2011, 181(4): 903-916.
38.	Gray KR, Wolz R, Heckemann RA, et al. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease. Neuroimage, 2012, 60(1): 221-229.
39.	Gray KR, Aljabar P, Heckemann RA, et al. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. Neuroimage, 2013, 65: 167-175.
40.	Feng C, Elazab A, Yang P, et al. Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 2019, 7(99): 63605-63618.
41.	Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage, 2011, 56(2): 766-781.
42.	Chen L, Qiao H, Zhu F. Alzheimer's disease diagnosis with brain structural MRI using multiview-slice attention and 3D convolution neural network. Front Aging Neurosci, 2022, 14: 871706.
43.	Yang BH, Chen C, Chou WH, et al. Classification of Alzheimer's disease from 18F-FDG and 11C-PiB PET imaging biomarkers using support vector machine. J Med Biol Eng, 2020, 40(4): 545-554.
44.	Hinrichs C, Singh V, Xu G, et al. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage, 2011, 55(2): 574-589.
45.	Brenner DJ, Hall EJ. Computed tomography--an increasing source of radiation exposure. N Engl J Med, 2007, 357(22): 2277-2284.
46.	Korley FK, Pham JC, Kirsch TD. Use of advanced radiology during visits to US emergency departments for injury-related conditions, 1998-2007. JAMA, 2010, 304(13): 1465-1471.
47.	McDonald RJ, Schwartz KM, Eckel LJ, et al. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol, 2015, 22(9): 1191-1198.
48.	Goldberg CB, Adams L, Blumenthal D, et al. To do no harm - and the most good - with AI in health care. Nat Med, 2024, 30(3): 623-627.
49.	Erickson BJ. Basic artificial intelligence techniques: machine learning and deep learning. Radiol Clin North Am, 2021, 59(6): 933-940.
50.	Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med, 2020, 26(9): 1364-1374.
51.	Cruz Rivera S, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health, 2020, 2(10): e549-e560.
52.	Balki I, Amirabadi A, Levman J, et al. Sample-size determination methodologies for machine learning in medical imaging research: a systematic review. Can Assoc Radiol J, 2019, 70(4): 344-353.

1. 2023 Alzheimer's disease facts and figures. Alzheimers Dement, 2023, 19(4): 1598-1695.
2. Ren R, Qi J, Lin S, et al. The China Alzheimer report 2022. Gen Psychiatr, 2022, 35(1): e100751.
3. Jia J, Wei C, Chen S, et al. The cost of Alzheimer's disease in China and re-estimation of costs worldwide. Alzheimers Dement, 2018, 14(4): 483-491.
4. Scheltens P, Blennow K, Breteler MM, et al. Alzheimer's disease. Lancet, 2016, 388(10043): 505-517.
5. 中华医学会神经病学分会痴呆与认知障碍学组. 阿尔茨海默病源性轻度认知障碍诊疗中国专家共识2021. 中华神经科杂志, 2022, 55(5): 421-440.
6. McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 2011, 7(3): 263-269.
7. Sperling RA, Aisen PS, Beckett LA, et al. 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, 2011, 7(3): 280-292.
8. Aisen PS. Q&A: The Alzheimer's disease neuroimaging initiative. BMC Med, 2011, 9: 101.
9. Braak H, Braak E. Evolution of neuronal changes in the course of Alzheimer's disease. J Neural Transm Suppl, 1998, 53: 127-40.
10. Schuff N, Woerner N, Boreta L, et al. MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers. Brain, 2009, 132(Pt 4): 1067-1077.
11. Newby D, Orgeta V, Marshall CR, et al. Artificial intelligence for dementia prevention. Alzheimers Dement, 2023, 19(12): 5952-5969.
12. Kim HW, Lee HE, Lee S, et al. Slice-selective learning for Alzheimer's disease classification using a generative adversarial network: a feasibility study of external validation. Eur J Nucl Med Mol Imaging, 2020, 47(9): 2197-2206.
13. Suk HI, Lee SW, Shen D, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage, 2014, 101: 569-582.
14. Guo C, Ashrafian H, Ghafur S, et al. Challenges for the evaluation of digital health solutions-a call for innovative evidence generation approaches. NPJ Digit Med, 2020, 3: 110.
15. Jackson D, Turner R. Power analysis for random-effects meta-analysis. Res Synth Methods, 2017, 8(3): 290-302.
16. McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA, 2018, 319(4): 388-396.
17. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
18. Um O, Cn O. Evaluating measures of indicators of diagnostic test performance: fundamental meanings and formulars. J Biometri Biostati, 2012, 3(1): 258.
19. Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ, 2003, 327(7414): 557-560.
20. Hinrichs C, Singh V, Mukherjee L, et al. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage, 2009, 48(1): 138-149.
21. Zhang D, Wang Y, Zhou L, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage, 2011, 55(3): 856-867.
22. Yun HJ, Kwak K, Lee JM, et al. Multimodal discrimination of Alzheimer's disease based on regional cortical atrophy and hypometabolism. PLoS One, 2015, 10(6): e0129250.
23. Vemuri P, Gunter JL, Senjem ML, et al. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage, 2008, 39(3): 1186-1197.
24. Tosun D, Schuff N, Rabinovici GD, et al. Diagnostic utility of ASL-MRI and FDG-PET in the behavioral variant of FTD and AD. Ann Clin Transl Neurol, 2016, 3(10): 740-751.
25. Sayeed A, Petrou M, Spyrou N, et al. Diagnostic features of Alzheimer's disease extracted from PET sinograms. Phys Med Biol, 2002, 47(1): 137-148.
26. Pan X, Adel M, Fossati C, et al. Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer's disease. Comput Methods Programs Biomed, 2019, 180: 105027.
27. Padilla P, López M, Górriz JM, et al. NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease. IEEE Trans Med Imaging, 2012, 31(2): 207-216.
28. Ni YC, Tseng FP, Pai MC, et al. Detection of Alzheimer's disease using ECD SPECT images by transfer learning from FDG PET. Ann Nucl Med, 2021, 35(8): 889-899.
29. Magnin B, Mesrob L, Kinkingnéhun S, et al. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. Neuroradiology, 2009, 51(2): 73-83.
30. Lu D, Popuri K, Ding GW, et al. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease. Med Image Anal, 2018, 46: 26-34.
31. Liu M, Cheng D, Yan W, et al. Classification of Alzheimer's disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform, 2018, 12: 35.
32. Li R, Perneczky R, Yakushev I, et al. Gaussian mixture models and model selection for [18f] fluorodeoxyglucose positron emission tomography classification in Alzheimer's disease. PLoS One, 2015, 10(4): e0122731.
33. Lerch JP, Pruessner J, Zijdenbos AP, et al. Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. Neurobiol Aging, 2008, 29(1): 23-30.
34. Kim HW, Lee HE, Oh K, et al. Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography. Biomed Eng Online, 2020, 19(1): 70.
35. Katako A, Shelton P, Goertzen AL, et al. Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia. Sci Rep, 2018, 8(1): 13236.
36. Ismail WN, Rajeena PPF, Ali MAS. A meta-heuristic multi-objective optimization method for Alzheimer's disease detection based on multi-modal data. Mathematics, 2023, 11(4): 764.
37. Illán J, Górriz J. 18F-FDG-FDG PET imaging analysis for computer aided Alzheimer's diagnosis. Information Sciences, 2011, 181(4): 903-916.
38. Gray KR, Wolz R, Heckemann RA, et al. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease. Neuroimage, 2012, 60(1): 221-229.
39. Gray KR, Aljabar P, Heckemann RA, et al. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. Neuroimage, 2013, 65: 167-175.
40. Feng C, Elazab A, Yang P, et al. Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 2019, 7(99): 63605-63618.
41. Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage, 2011, 56(2): 766-781.
42. Chen L, Qiao H, Zhu F. Alzheimer's disease diagnosis with brain structural MRI using multiview-slice attention and 3D convolution neural network. Front Aging Neurosci, 2022, 14: 871706.
43. Yang BH, Chen C, Chou WH, et al. Classification of Alzheimer's disease from 18F-FDG and 11C-PiB PET imaging biomarkers using support vector machine. J Med Biol Eng, 2020, 40(4): 545-554.
44. Hinrichs C, Singh V, Xu G, et al. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage, 2011, 55(2): 574-589.
45. Brenner DJ, Hall EJ. Computed tomography--an increasing source of radiation exposure. N Engl J Med, 2007, 357(22): 2277-2284.
46. Korley FK, Pham JC, Kirsch TD. Use of advanced radiology during visits to US emergency departments for injury-related conditions, 1998-2007. JAMA, 2010, 304(13): 1465-1471.
47. McDonald RJ, Schwartz KM, Eckel LJ, et al. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol, 2015, 22(9): 1191-1198.
48. Goldberg CB, Adams L, Blumenthal D, et al. To do no harm - and the most good - with AI in health care. Nat Med, 2024, 30(3): 623-627.
49. Erickson BJ. Basic artificial intelligence techniques: machine learning and deep learning. Radiol Clin North Am, 2021, 59(6): 933-940.
50. Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med, 2020, 26(9): 1364-1374.
51. Cruz Rivera S, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health, 2020, 2(10): e549-e560.
52. Balki I, Amirabadi A, Levman J, et al. Sample-size determination methodologies for machine learning in medical imaging research: a systematic review. Can Assoc Radiol J, 2019, 70(4): 344-353.

Chinese Journal of Evidence-Based Medicine

Accuracy comparison of artificial intelligence-assisted diagnosis systems based on 18F-FDG PET/CT and structural MRI in the diagnosis of Alzheimer's disease: a meta-analysis

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