Wu Xinxing, Peng Chong, Nelson Peter T, Cheng Qiang
Institute for Biomedical Informatics University of Kentucky Lexington Kentucky USA.
Department of Computer Science and Engineering Qingdao University Shandong China.
Alzheimers Dement (N Y). 2022 Nov 3;8(1):e12363. doi: 10.1002/trc2.12363. eCollection 2022.
Limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage-specific future conversions at an individual level.
After using the Kaplan-Meier estimation and log-rank test to confirm the heterogeneity of LATE progression, we developed a deep learning-based approach to assess the stage-specific probabilities of time to LATE conversions for different subjects.
Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score was less than 0.14. Moreover, we identified the top 10 important predictors for each disease conversion scenario to help explain the estimation results, which were clinicopathologically meaningful and most were also statistically significant.
Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.
边缘叶为主的年龄相关性TAR DNA结合蛋白43（TDP-43）脑病（LATE）是一种最近定义的神经退行性疾病。目前，尚无有效的方法在个体水平上预测特定阶段未来转化的时间。
在使用Kaplan-Meier估计和对数秩检验确认LATE进展的异质性后，我们开发了一种基于深度学习的方法来评估不同受试者发生LATE转化的特定阶段概率。
我们的方法可以准确估计疾病发病率和向下一阶段的转变：一致性指数至少为82%，综合Brier评分小于0.14。此外，我们确定了每种疾病转化情况的前10个重要预测因素，以帮助解释估计结果，这些因素具有临床病理意义，且大多数在统计学上也具有显著性。
我们的研究有可能在LATE转化实际发生前数年为其未来病程提供个体化评估。
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