Deep learning algorithm reveals probabilities of stage-specific time to conversion in individuals with neurodegenerative disease LATE

Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Introduction: 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. Methods: 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. Results: 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. Discussion: Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.

Original languageEnglish
Article numbere12363
JournalAlzheimer's and Dementia: Translational Research and Clinical Interventions
Volume8
Issue number1
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

Keywords

  • limbic-predominant age-related TAR DNA-binding protein 43 encephalopathy
  • machine learning
  • progression rate
  • stage-stratified analysis
  • survival models
  • time-to-event estimation

ASJC Scopus subject areas

  • Clinical Neurology
  • Psychiatry and Mental health

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