目的探讨基于双参数磁共振成像(biparametric magnetic resonance imaging,bp-MRI)的前列腺影像报告和数据系统2.1版(prostate imaging report and data system version 2.1,PI-RADS v2.1)联合前列腺特异性抗原密度(prostate specific a...目的探讨基于双参数磁共振成像(biparametric magnetic resonance imaging,bp-MRI)的前列腺影像报告和数据系统2.1版(prostate imaging report and data system version 2.1,PI-RADS v2.1)联合前列腺特异性抗原密度(prostate specific antigen density,PSAD)鉴别诊断总前列腺特异性抗原(total prostate specific antigen,tPSA)4~20 ng/mL临床显著性前列腺癌(clinically significant prostate cancer,csPCa)的价值及风险分层。材料与方法回顾性分析了宁夏医科大学总医院2017年10月至2023年6月304例PSA 4~20 ng/mL前列腺疾病患者的bp-MRI图像和临床资料。根据病理结果分为csPCa组(Gleason评分≥7分,n=66)和非csPCa(Gleason评分<7分及良性疾病,n=238)。经单因素、多因素logistic回归分析筛选独立危险因子并建立联合模型,再用决策曲线分析(decision curve analysis,DCA)其临床净效益。以受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)比较独立危险因子与联合模型的诊断效能,并对独立危险因子进行等级划分和组合。结果联合模型(PI-RADS v2.1+PSAD)的诊断效能最好(AUC为0.901,95%CI:0.858~0.944)。将PI-RADS v2.1与PSAD等级划分并组合,当PI-RADS v2.1≤2且PSAD≤0.15 ng/mL^(2),csPCa阳性率为0%;当PI-RADS v2.1为3分且PSAD<0.30 ng/mL^(2)时,csPCa阳性率<15%;当PI-RADS v2.1为4~5分且PSAD为0.15~0.29 ng/mL^(2)时,csPCa阳性率为46.5%;当PI-RADS v2.1为4~5分且PSAD≥0.30 ng/mL^(2)时,csPCa阳性率高达81.3%。结论PI-RAD v2.1≤2或PI-RAD v2.1=3且PSAD值<0.30 ng/ml2的患者可避免不必要的活检。PI-RADS v2.1联合PSAD能显著提高tPSA 4~20 ng/mL csPCa的诊断效能,将二者联合有助于穿刺前对csPCa的患者进行风险评估,以减少部分患者不必要的穿刺,并为临床提供一定的决策指导。展开更多
Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential ...Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.展开更多
基金The National Natural Science Foundation of China-Regional Science“Identification of novel drug targets for lung cancer via Mendelian randomization analysis based on blood proteomics”(62362062)The 2025 Xinjiang University Excellent Graduate Innovation Project“Research on identification of therapeutic targets and predictive factors for mental disorders based on proteomics”(XJDX2025YJS151)。
文摘Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.
