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Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging
Yae Won Park1, Dongmin Choi2, Mina Park3
1Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Background:
Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients.
Objective:
To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles.
Methods:
A total of 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated.
Results:
The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set.
Conclusion:
Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.
Insights
Radiomics analysis of MRI scans can predict amyloid-β (Aβ) levels in mild cognitive impairment (MCI) patients. Combining radiomics with clinical data significantly improves prediction accuracy for cerebrospinal fluid (CSF) Aβ42 status.
Area of Science:
- Neuroimaging
- Biomarker Discovery
- Artificial Intelligence in Medicine
Background:
- Noninvasive identification of amyloid-β (Aβ) is crucial for managing mild cognitive impairment (MCI).
- Cerebrospinal fluid (CSF) Aβ42 levels are a key biomarker, but invasive testing is a limitation.
Purpose of the Study:
- To evaluate if radiomics features from the hippocampus can predict CSF Aβ42 status in MCI patients.
- To determine if integrating radiomics with clinical profiles enhances prediction accuracy.
Main Methods:
- 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used.
- Radiomics features were extracted from hippocampal MRI, and random forest models were developed.
- Three models were compared: radiomics-only, clinical-only, and a combined radiomics-clinical model.
Main Results:
- The combined model achieved the highest prediction accuracy with an Area Under the Curve (AUC) of 0.823.
- The clinical model showed an AUC of 0.758, while the radiomics model had an AUC of 0.674.
- Hippocampal volume alone had a lower predictive performance (AUC = 0.543).
Conclusions:
- Radiomics analysis of MRI data can predict CSF Aβ42 status in MCI patients.
- The integration of radiomics and clinical data offers a promising noninvasive approach for patient stratification.
- This approach may help triage patients for more invasive and costly Aβ testing.
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