Scalable CT-based prognostic modeling of dementia conversion in mild cognitive impairment.
Authors
Affiliations (12)
Affiliations (12)
- BeauBrain Healthcare, Inc., Seoul, South Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea.
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea.
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea.
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. [email protected].
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea. [email protected].
- BeauBrain Healthcare, Inc., Seoul, South Korea. [email protected].
Abstract
Mild cognitive impairment (MCI) is a heterogeneous condition, with up to 40% of patients progressing to dementia within three years of clinical diagnosis. While MRI, PET, and CSF biomarkers have shown prognostic value, their limited accessibility restricts routine use. We aimed to develop and validate a CT-based machine learning model to predict dementia conversion in patients with MCI. We analyzed 791 MCI patients with baseline CT and longitudinal neuropsychological assessments. Candidate predictors included demographic factors, <i>APOE</i> genotype, Clinical Dementia Rating-Sum of Boxes (CDR-SB), and CT-derived W-scores from regional CSF and ventricular volumes. CT-derived W-scores represent age-, sex-, and modality-adjusted z-scores of regional CSF and ventricular volumes. Converters were older (73.4 vs. 70.7 years, <i>p</i> < 0.001), more often female (60.4% vs. 51.4%, <i>p</i> = 0.015), and more likely <i>APOE</i> ε4 carriers (52.5% vs. 40.0%, <i>p</i> < 0.001). Logistic regression with SMOTEENN sampling achieved the best performance (Area Under the Curve [AUC] = 0.840; accuracy = 0.743; sensitivity = 0.847; specificity = 0.674), with robust generalizability in the independent test set (AUC = 0.823). Feature selection and SHAP analysis identified six key predictors, including CDR-SB, <i>APOE</i> ε4, age, and three CT-derived volumetric markers (W-score in the left inferior lateral ventricle, left parietal CSF, and left occipital CSF). Our findings demonstrate that CT-derived volumetric analysis, combined with clinical and genetic features, enables accurate and interpretable prediction of dementia conversion in MCI. The online version contains supplementary material available at 10.1038/s41598-026-45439-8.