Radiomics incorporating deep features for predicting Parkinson's disease in ¹²³I-Ioflupane SPECT.

ABSTRACT:

Purpose

¹²³I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using ¹²³I-Ioflupane SPECT images at year 0.

Methods

In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and ¹²³I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptake

SUBMITTER: Jiang H 

PROVIDER: S-EPMC11236833 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature