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

Zheng, Qian; Zhang, Yiyu; Li, Honglun; Tong, Xiangrong; Ouyang, Minhui

doi:10.1007/s00330-022-09081-y

Cited by 8 publications

(18 citation statements)

References 33 publications

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“…A total of 15 papers were included [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The findings reported in this section are organized under macro-concept areas of interest.…”

Section: Resultsmentioning

confidence: 99%

“…In 8 out of 15 studies [ 20 , 21 , 22 , 23 , 28 , 29 , 31 , 33 ], the data used were retrieved from a public repository, 6 were from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and 2 were from the Open Access Series of Imaging Studies (OASIS) [ 21 , 22 ]. In the remaining seven studies [ 19 , 24 , 25 , 26 , 27 , 30 , 32 ], original data were collected from human participants hospitalized in different healthcare structures.…”

Section: Resultsmentioning

confidence: 99%

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Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field

Bevilacqua,

Barbarossa,

Fantechi

et al. 2023

JCM

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The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer’s disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer’s disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer’s disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer’s disease patients—both at the dementia and pre-dementia phases of the disease—from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer’s disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer’s.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field

Bevilacqua,

Barbarossa,

Fantechi

et al. 2023

JCM

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“…The pooled sensitivity, specificity, precision, accuracy, and AUC of the mentioned task were 0.6938 (95% CI: [0.6465; 0.7374], I^2 = 44.0%, I^2 95% CI: [0.0%; 77.8%]) (Figure 2), 0.8173 (95% CI: [0.6117; 0.9270], I^2 = 91.3%, I^2 95% CI: [83.7%; 95.3%]) (‘Figure 3), 0.7468 (95% CI: [3.06; 5.35], I^2 = 93.9%, I^2 95% CI: [89.3%; 96.5%]) (Figure 4), 74.22 (SD: 13.63), and 77.52 (SD: 11.39), respectively. Since the I 2 level was considerably high in specificity and precision analyses, a sensitivity analysis was conducted, indicating the lowest heterogeneity is achieved by removing Cheung et al study (Cheung, et al, 2022) (Figure 5), and Zheng et al study (Zheng, Zhang, Li, Tong, & Ouyang, 2022) (Figure 6), respectively. Despite an I^2 level of 44%, none of the studies was detected as an outlier for sensitivity meta-analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The pooled sensitivity, specificity, precision, accuracy, and AUC of the mentioned task were 0.8822 (95% CI: [0.7888; 0.9376], I^2 = 72.6%, I^2 95% CI: [44.0%; 86.6%]) (Figure 7), 0.8849 (95% CI: [0.7978; 0.9374], I^2 = 89.2%, I^2 95% CI: [81.1%; 93.8%]) (Figure 8), 0.8779 (95% CI: [0.8255; 0.9161], I^2 = 68.2%, ], I^2 95% CI: [33.2%; 84.8%]) (Figure 9), 87.70 (SD: 0.08), and 90.42 (SD: 0.08) respectively. Since the I 2 level was considerably high in sensitivity, specificity, and precision analyses, a sensitivity analysis was conducted, indicating the lowest heterogeneity is achieved by removing Zheng et al study (Zheng, et al, 2022) (Figure 10), Q. Feng et al study (Q. Feng et al, 2018) (Figure 11), and Q. Feng et al study (Q.…”

Section: Resultsmentioning

confidence: 99%

Diagnostic Performance of MRI Radiomics for Classification of Alzheimer’s disease, Mild Cognitive Impairment, and Normal Subjects: A Systematic Review and Meta-analysis

Shahidi

Baradaran

Asgarzadeh

et al. 2023

Preprint

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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. 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: This study was performed using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. 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 October 17, 2022. Original studies discussing the diagnostic performance of MRI Radiomics in Alzheimer's disease (AD), and mild cognitive impairment (MCI) diagnosis were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), and the Radiomic Quality Score tool (RQS). Results: We identified 10 studies that met the inclusion criteria, involving a total of 3446 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 0.8822 (95% CI 0.7888-0.9376), and 0.8849 (95% CI 0.7978-0.9374), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.7882 (95% CI 0.6272-0.8917) and 0.7736 (95% CI 0.6480-0.8639), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.6938 (95% CI 0.6465-0.7374) and 0.8173 (95% CI 0.6117-0.9270), 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. However, further studies with larger sample sizes and more rigorous study designs are warranted to confirm these findings and establish the clinical utility of MRI radiomics in AD and MCI diagnosis.

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“…This area aims at producing surrogate biomarkers to reduce cost or invasivity, e.g., neuroimaging to replace lumbar puncture. Prediction of clinical measures, e.g., ADAS-Cog13 [75] and ventricular volume [76], is also used for longitudinal studies and attempts to achieve a more comprehensive evaluation of disease progression and model performance benchmarking.…”

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confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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

Cited by 8 publications

References 33 publications

Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field

Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field

Diagnostic Performance of MRI Radiomics for Classification of Alzheimer’s disease, Mild Cognitive Impairment, and Normal Subjects: A Systematic Review and Meta-analysis

A Survey of Deep Learning for Alzheimer’s Disease

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