medRxiv : the preprint server for health sciences · 2026
Predicting Conversion from SCD to MCI: A Machine Learning Study.
СтатьяПрепринтНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kamal Farooq, Metz Amelie, Chadwick Katherine, Moqadam Roqaie, Morrison Cassandra, Dadar Mahsa, Alzheimer’s Disease Neuroimaging Initiative, Consortium for the Early Identification of Alzheimer’s Disease-Quebec (CIMA-Q), PREVENT-AD Research Group
Аннотация
BACKGROUND: Subjective cognitive decline (SCD) may precede mild cognitive impairment (MCI), but not all individuals with SCD progress to MCI. Identifying which individuals are most likely to convert and over what time frame remains an important goal in Alzheimer's disease research. MRI measures of white matter hyperintensity (WMH) burden and gray matter (GM) atrophy may improve prediction beyond demographic and cognitive predictors, but their incremental value across different time intervals has not been established.
METHODS: Data were obtained from four longitudinal cohorts (ADNI, NACC, CIMA-Q, and PREVENT-AD). A total of 1,352 participants with SCD at baseline were included. Machine learning models (logistic regression, random forest, XGBoost) were used to predict conversion from SCD to MCI at 2 years, 3 years, 4 years, and 5-year horizons. Four feature sets were compared: base (age, sex, education, APOE4, hypertension), base and cognition (adding MoCA and Trail Making Test Part B), base and MRI (adding regional WMH and GM volumes), and combined (all features).
RESULTS: The base and MRI set achieved the highest Area Under the Curve (AUC) at the 3-year (0.900), 4-year (0.879), and 5-year (0.923) horizons. The combined set achieved the highest AUC at the 2-year horizon only (0.884). MRI features produced larger AUC gains over the base model than cognitive features at the 3, 4, 5-year horizons. Parietal WMH was the most frequently selected MRI predictor.
CONCLUSIONS: MRI features, particularly regional WMH and GM volumes, provided greater predictive value than cognitive features at longer prediction horizons. A select number of regional MRI features predicted SCD to MCI conversion with high accuracy up to 5 years in advance.
METHODS: Data were obtained from four longitudinal cohorts (ADNI, NACC, CIMA-Q, and PREVENT-AD). A total of 1,352 participants with SCD at baseline were included. Machine learning models (logistic regression, random forest, XGBoost) were used to predict conversion from SCD to MCI at 2 years, 3 years, 4 years, and 5-year horizons. Four feature sets were compared: base (age, sex, education, APOE4, hypertension), base and cognition (adding MoCA and Trail Making Test Part B), base and MRI (adding regional WMH and GM volumes), and combined (all features).
RESULTS: The base and MRI set achieved the highest Area Under the Curve (AUC) at the 3-year (0.900), 4-year (0.879), and 5-year (0.923) horizons. The combined set achieved the highest AUC at the 2-year horizon only (0.884). MRI features produced larger AUC gains over the base model than cognitive features at the 3, 4, 5-year horizons. Parietal WMH was the most frequently selected MRI predictor.
CONCLUSIONS: MRI features, particularly regional WMH and GM volumes, provided greater predictive value than cognitive features at longer prediction horizons. A select number of regional MRI features predicted SCD to MCI conversion with high accuracy up to 5 years in advance.