HYBRID EVENT: You can participate in person at Orlando, USA or Virtually from your home or work.

6th Edition of International Conference on Neurology and Brain Disorders

October 24 -26, 2022

October 24 -26, 2022 | Orlando, Florida, USA
INBC 2022

Du Yang

Speaker at Neurology and Brain Disorders 2022 - Du Yang
Shanghai Jiao Tong University School of Medicine, China
Title : Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Abstract:

Late-onset Alzheimer’s disease (LOAD) and early-onset Alzheimer’s disease (EOAD) are different subtypes of AD. This study aimed to build and validate radiomics models of the hippocampus for EOAD and LOAD. Thirty-six EOAD patients, 36 LOAD patients, 36 YCs, and 36 OCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were enrolled and allocated to training and test sets of the EOAD-YC groups, LOAD-OC groups, and EOAD-LOAD groups. Independent external validation sets including 15 EOAD patients, 15 LOAD patients, 15 YCs, and 15 OCs from Shanghai Mental Health Center were constructed, respectively. Bilateral hippocampal segmentation and feature extraction were performed for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. Support vector machine (SVM) models were constructed based on the identified features to distinguish EOAD from YC subjects, LOAD from OC subjects, and EOAD from LOAD subjects. The areas under the receiver operating characteristic curves (AUCs) and accuracy of the SVM model were 0.90 and 0.77 in the test set and 0.91 and 0.87 in the validation set for EOAD and YC subjects, respectively; for LOAD and OC subjects, the AUC and accuracy were 0.94 and 0.86 in the test set and 0.92 and 0.78 in the validation set, respectively. For the SVM model of EOAD and LOAD subjects, the AUC was 0.87 and the accuracy was 0.79 in the test set; additionally, the AUC was 0.86 and the accuracy was 0.77 in the validation set. The findings of this study provide insights into the potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD. This study is the first to show that SVM classification analysis based on hippocampal radiomic features is a valuable method for clinical applications in EOAD.

What will audience learn from your presentation?

  • Different hippocampus-based radiomic features between LOAD and EOAD
  • Potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD

Biography:

Dr. Du studied Psychiatry and Mental Health at the Sichuang university,China and graduated as a master in 2020. She then joined the research group of Prof. Li at Shanghai Jiao Tong University. She studied for her PhD degree in 2021 at Shanghai Mental Health Center.  She has published several research articles in SCI(E) journals.

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