{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zou Y"],"funding":["Xiaogan City Natural Science Program Project"],"pagination":["93-105"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11909710"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["72(1)"],"pubmed_abstract":["<h4>Introduction</h4>Accurate identification and grading of clinically significant prostate cancer (csPCa, Gleason Score ≥ 7) without invasive procedures remains a significant clinical challenge. This study aims to develop and evaluate a two-stage model designed for precise Gleason grading. The model initially uses radiomics-based multiparametric MRI to identify csPCa and then refines the Gleason grading by integrating clinical indicators and radiomics features.<h4>Methods</h4>We retrospectively analysed 399 patients with PI-RADS ≥ 3 lesions, categorising them into non-significant prostate cancer (nsPCa, 263 cases) and csPCa (136 cases, subdivided by GGs). Regions of interest (ROIs) for the prostate and lesions were manually delineated on T2-weighted and apparent diffusion coefficient (ADC"],"journal":["Journal of medical radiation sciences"],"pubmed_title":["A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach."],"pmcid":["PMC11909710"],"funding_grant_id":["XGKJ2024010035"],"pubmed_authors":["Liu X","Zou Y","Xie Y","Zhang Y","Cui J","Chen L","Tian R","Wang X","Jiao C","Kang Z","Ma F"],"additional_accession":[]},"is_claimable":false,"name":"A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach.","description":"<h4>Introduction</h4>Accurate identification and grading of clinically significant prostate cancer (csPCa, Gleason Score ≥ 7) without invasive procedures remains a significant clinical challenge. This study aims to develop and evaluate a two-stage model designed for precise Gleason grading. The model initially uses radiomics-based multiparametric MRI to identify csPCa and then refines the Gleason grading by integrating clinical indicators and radiomics features.<h4>Methods</h4>We retrospectively analysed 399 patients with PI-RADS ≥ 3 lesions, categorising them into non-significant prostate cancer (nsPCa, 263 cases) and csPCa (136 cases, subdivided by GGs). Regions of interest (ROIs) for the prostate and lesions were manually delineated on T2-weighted and apparent diffusion coefficient (ADC","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-20T00:11:30.558Z","creation":"2025-04-20T00:11:30.558Z"},"accession":"S-EPMC11909710","cross_references":{"pubmed":["39698957"],"doi":["10.1002/jmrs.841"]}}