Annals of nuclear medicine · 2026
A real-world decision tree model based on ¹⁸F-FDG PET/CT Z scores and Mini-Mental State Examination for differentiating mild cognitive impairment from clinically diagnosed Alzheimer disease.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Övüt Ayşe Rana, Toyran Reyhan, Bülbül Nazlı Gamze, Urhan Muammer
Аннотация
OBJECTIVE: To evaluate the ability of regional metabolic Z scores derived from ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT), medial temporal atrophy (MTA) scores, and Mini-Mental State Examination (MMSE) results to distinguish mild cognitive impairment (MCI) from clinically diagnosed Alzheimer disease (AD), and to develop an interpretable decision-tree model based on these parameters.
METHODS: This retrospective single-center study included 124 patients who underwent ¹⁸F-FDG PET/CT for suspected cognitive impairment between 2023 and 2025. Patients were classified as AD or MCI according to final clinical diagnoses established by experienced neurologists using National Institute on Aging-Alzheimer's Association criteria. Regional metabolic Z scores were generated with CortexID Suite by comparison with an age-matched healthy normative database. MTA was graded on structural magnetic resonance imaging using the Scheltens scale by two blinded nuclear medicine physicians. MMSE scores were retrieved from clinical records. A random forest algorithm was used to identify discriminative variables, which were then incorporated into a transparent decision-tree classifier. The dataset was divided into a training cohort (n = 100) and a split-sample test cohort (n = 24).
RESULTS: The final decision tree integrated regional metabolic Z scores and MMSE values in a hierarchical classification structure. In the split-sample test cohort, the model achieved an accuracy of 95.8% and a balanced accuracy of 96.4%, with 100% sensitivity and 92.9% specificity for AD classification. The area under the receiver operating characteristic curve was 0.964 (95% confidence interval, 0.894-1.000).
CONCLUSIONS: An interpretable decision-tree approach integrating quantitative ¹⁸F-FDG PET/CT metrics with cognitive assessment showed promising performance for distinguishing clinically diagnosed AD from MCI in a real-world clinical cohort.
METHODS: This retrospective single-center study included 124 patients who underwent ¹⁸F-FDG PET/CT for suspected cognitive impairment between 2023 and 2025. Patients were classified as AD or MCI according to final clinical diagnoses established by experienced neurologists using National Institute on Aging-Alzheimer's Association criteria. Regional metabolic Z scores were generated with CortexID Suite by comparison with an age-matched healthy normative database. MTA was graded on structural magnetic resonance imaging using the Scheltens scale by two blinded nuclear medicine physicians. MMSE scores were retrieved from clinical records. A random forest algorithm was used to identify discriminative variables, which were then incorporated into a transparent decision-tree classifier. The dataset was divided into a training cohort (n = 100) and a split-sample test cohort (n = 24).
RESULTS: The final decision tree integrated regional metabolic Z scores and MMSE values in a hierarchical classification structure. In the split-sample test cohort, the model achieved an accuracy of 95.8% and a balanced accuracy of 96.4%, with 100% sensitivity and 92.9% specificity for AD classification. The area under the receiver operating characteristic curve was 0.964 (95% confidence interval, 0.894-1.000).
CONCLUSIONS: An interpretable decision-tree approach integrating quantitative ¹⁸F-FDG PET/CT metrics with cognitive assessment showed promising performance for distinguishing clinically diagnosed AD from MCI in a real-world clinical cohort.