Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach
Jun Pyo Kim1,2,3, Jonghoon Kim4, Hyemin Jang1,2,3
1Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea.
Abstract:
Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.
Insights
Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. This study shows that radiomics features from MRI scans, combined with clinical data, significantly improve the prediction of amyloid positivity.
Area of Science:
- Medical Imaging
- Radiology
- Neurology
Background:
- Predicting amyloid positivity is essential for diagnosing and managing mild cognitive impairment (MCI).
- Current prediction methods often rely on clinical data, which may lack sufficient accuracy.
- Structural MRI offers a non-invasive approach to gather detailed imaging data.
Purpose of the Study:
- To evaluate the efficacy of radiomics features derived from structural MRI in predicting amyloid positivity in MCI patients.
- To compare the predictive performance of MRI-based radiomics models against baseline clinical predictors.
- To determine if combining radiomics features with clinical data enhances prediction accuracy.
Main Methods:
- Extracted histogram and texture radiomics features from T1, T2 FLAIR, and Diffusion Tensor Imaging (DTI) sequences of 440 MCI patients.
- Utilized regularized regression for feature selection and prediction.
- Developed and evaluated models using baseline non-imaging predictors (age, sex, ApoE genotype), single MRI sequence radiomics, combined T1/T2 FLAIR radiomics, and a combined model with baseline and T1/T2 FLAIR radiomics.
Main Results:
- The baseline non-imaging model achieved an AUC of 0.71.
- Single MRI sequence radiomics models showed fair performance (AUC 0.68-0.74).
- Combining T1 and T2 FLAIR radiomics improved prediction (test AUC 0.75, validation AUC 0.72).
- The best performance was achieved by combining baseline features with T1 and T2 FLAIR radiomics (test AUC 0.79, validation AUC 0.76), significantly outperforming other models (p < 0.001).
Conclusions:
- Radiomics features extracted from structural MRI possess significant predictive value for amyloid positivity in MCI.
- Combining radiomics features with baseline clinical predictors substantially enhances prediction performance.
- This integrated approach offers a promising tool for improving the accuracy of amyloid positivity prediction in MCI.
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