Machine Vision and Medical Image Processing (MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Department of Systems Engineering, École deTechnologie Supérieure, Université du Québec, Montreal, Quebec, Canada.
J Alzheimers Dis. 2022;85(2):837-850. doi: 10.3233/JAD-215266.
Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy.
The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects.
We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting.
In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model.
Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.
评估认知正常（CN）和轻度认知障碍（MCI）患者患阿尔茨海默病（AD）的风险极其重要。虽然已经广泛研究了 MCI 向 AD 的进展风险，但很少有研究估计 CN 向 MCI 的转换风险。Cox 比例风险（PH）是一种广泛使用的生存分析模型，它假设预测风险关系呈线性。将 PH 模型推广到更复杂的预测风险关系可能会提高风险估计的准确性。
本研究旨在开发一种使用 Xgboost 回归器的 PH 模型，该模型基于人口统计学、遗传学、神经精神和神经影像学预测因子，用于估计 MCI 患者的 AD 风险，以及 CN 受试者的 MCI 风险。
我们用 Xgboost 回归器代替 Cox PH 线性模型，以捕捉预测因子之间的复杂相互作用和非线性预测风险关联。我们努力将模型输入限制为非侵入性和更广泛可用的预测因子，以便在更广泛的环境中更方便地应用。
在 MCI 向 AD（n = 882）的转化中，Xgboost 模型的一致性指数（C-index）为 84.5%。当该模型用于预测 CN（n = 100）个体中的 MCI 风险时，C-index 为 73.3%。在这两种应用中，Xgboost 的 C-index 均显著高于 Cox PH 模型。
使用 Xgboost 等非线性回归器可提高 CN 和 MCI 患者的 AD 痴呆风险评估。使用相对低成本（时间、侵入性和可用性）的预测因子进行合理的风险分层是可能的。讨论了改善 AD 痴呆风险估计的未来策略。
