{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jiang H"],"funding":["External Industrial Research Collaboration Grant from Zhejiang Fuchuan Medical Technology Co., Ltd.","Fujian Provincial Health Technology Project","Joint Funds for the innovation of Science and Technology, Fujian province","Science and Technology Development Fund of Macau","National Natural Science Foundation of China","Collaborative Research Grant from University of Macau"],"pagination":["60"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11236833"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_abstract":["<h4>Purpose</h4><sup>123</sup>I-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 <sup>123</sup>I-Ioflupane SPECT images at year 0.<h4>Methods</h4>In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and <sup>123</sup>I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptake"],"journal":["EJNMMI physics"],"pubmed_title":["Radiomics incorporating deep features for predicting Parkinson's disease in <sup>123</sup>I-Ioflupane SPECT."],"pmcid":["PMC11236833"],"funding_grant_id":["MYRG-CRG2022-00011-ICMS","2023Y9143","2023GGA020","82302263","EF062/FST/MSP/2023/ZJFCYLKJ","0016/2023/RIB1"],"pubmed_authors":["Mok GSP","Wang B","Wang R","Jiang H","Lu Z","Zhang H","Du Y","Zhao Y"],"additional_accession":[]},"is_claimable":false,"name":"Radiomics incorporating deep features for predicting Parkinson's disease in <sup>123</sup>I-Ioflupane SPECT.","description":"<h4>Purpose</h4><sup>123</sup>I-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 <sup>123</sup>I-Ioflupane SPECT images at year 0.<h4>Methods</h4>In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and <sup>123</sup>I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptake","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2025-04-05T15:48:20.913Z","creation":"2025-04-05T15:48:20.913Z"},"accession":"S-EPMC11236833","cross_references":{"pubmed":["38985382"],"doi":["10.1186/s40658-024-00651-1"]}}