Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Kim, Jun Pyo; Kim, Jong-Hoon; Kim, Jae-Ho; Kang, Sung Hoon; Kim, Ji Sun; Lee, Jongmin; Na, Duk L.; Kim, Hee Jin; Seo, Sang Won; Park, Hyunjin

doi:10.1038/s41598-021-86114-4

Cited by 26 publications

(51 citation statements)

References 67 publications

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“…Apart from the classification task, radiomics could display unique advantages in the field of frontotemporal dementia. Radiomics features have, in fact, demonstrated an optimal predictive power in terms of response to therapy or clinical outcomes in the field of oncology and neurodegeneration ( Feng and Ding, 2020 ; Conti et al, 2021 ; Kim et al, 2021 ). Hence, radiomics, even in combination with non-imaging data such as clinical scales and biological markers, might reasonably be used to enhance the predictive potential of medical imaging in FTD subtypes.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI

et al. 2022

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Radiomics has been proposed as a useful approach to extrapolate novel morphological and textural information from brain Magnetic resonance images (MRI). Radiomics analysis has shown unique potential in the diagnostic work-up and in the follow-up of patients suffering from neurodegenerative diseases. However, the potentiality of this technique in distinguishing frontotemporal dementia (FTD) subtypes has so far not been investigated. In this study, we explored the usefulness of radiomic features in differentiating FTD subtypes, namely, the behavioral variant of FTD (bvFTD), the non-fluent and/or agrammatic (PNFA) and semantic (svPPA) variants of a primary progressive aphasia (PPA). Classification analyses were performed on 3 Tesla T1-weighted images obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative. We included 49 patients with bvFTD, 25 patients with PNFA, 34 patients with svPPA, and 60 healthy controls. Texture analyses were conducted to define the first-order statistic and textural features in cortical and subcortical brain regions. Recursive feature elimination was used to select the radiomics signature for each pairwise comparison followed by a classification framework based on a support vector machine. Finally, 10-fold cross-validation was used to assess classification performances. The radiomics-based approach successfully identified the brain regions typically involved in each FTD subtype, achieving a mean accuracy of more than 80% in distinguishing between patient groups. Note mentioning is that radiomics features extracted in the left temporal regions allowed achieving an accuracy of 91 and 94% in distinguishing patients with svPPA from those with PNFA and bvFTD, respectively. Radiomics features show excellent classification performances in distinguishing FTD subtypes, supporting the clinical usefulness of this approach in the diagnostic work-up of FTD.

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Section: Discussionmentioning

confidence: 99%

Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI

et al. 2022

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“…Studies have suggested neuroimaging modalities as good predictors for Aβ+ status. One group proposed the least absolute shrinkage selection (LASSO) regression method to predict Aβ+ in 440 aMCI subjects [28]. Radiomics features were extracted from MRI images with hippocampus and precuneus as regions of interest (ROIs) and were used alone or in combination with baseline non-imaging predictors.…”

Section: Protein Biomarkers For Admentioning

confidence: 99%

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Seo

Lee

2022

Life

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Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

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“…By themselves, amyloid biomarkers are insufficient in monitoring the progression of AD after symptom onset [7]. N-status can be assessed via brain magnetic resonance imaging (MRI) or brain 18 F-FDG-PET. Besides its utility in the diagnostic process of AD, 18 F-FDG-PET has proven utility in disentangling several phenotypes of dementia and cognitive decline [8,9].…”

Section: Introductionmentioning

confidence: 99%

Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment

Doering

Hoenig

Bischof

et al. 2022

Eur J Nucl Med Mol Imaging

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Background In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning–based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype 18F-FDG PET, age, and sex. Methods Three hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4 non-carriers (APOE4-nc; majority class: amyloid negative (Aβ-)) and carriers (APOE4-c; majority class: amyloid positive (Aβ +)) from 18F-FDG-PET, age, and sex. Classifiers were tested on two different datasets. Finally, frequencies of progression to dementia were compared between gold standard and predicted A-status. Results Aβ- in APOE4-nc and Aβ + in APOE4-c were predicted with a precision of 87% and a recall of 79% and 51%, respectively. Predicted A-status and gold standard A-status were at least equally indicative of risk of progression to dementia. Conclusion We developed an algorithm allowing approximation of A-status in MCI with good reliability using APOE-genotype, 18F-FDG PET, age, and sex information. The algorithm could enable better estimation of individual risk for developing AD based on existing biomarker information, and support efficient selection of patients who would benefit most from further etiological clarification. Further potential utility in clinical routine and clinical trials is discussed.

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Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Cited by 26 publications

References 67 publications

Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI

Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment

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