目的:对穿刺活检单针阳性前列腺癌术后病理升级的危险因素进行分析,并尝试构建预测穿刺单针阳性前列腺癌患者术后病理升级的数学模型。方法:回顾分析2015年1月至2020年8月期间于北京大学第一医院诊断为前列腺癌且接受根治性前列腺切除...目的:对穿刺活检单针阳性前列腺癌术后病理升级的危险因素进行分析,并尝试构建预测穿刺单针阳性前列腺癌患者术后病理升级的数学模型。方法:回顾分析2015年1月至2020年8月期间于北京大学第一医院诊断为前列腺癌且接受根治性前列腺切除术的患者1 349例,选取其中穿刺活检单针阳性患者的临床资料,将其分为术后病理较穿刺病理升级组及未升级组,比较两组的年龄、体重指数、临床分期、前列腺影像报告和数据系统(prostate imaging reporting and data system, PI-RADS)评分、磁共振成像(magnetic resonance imaging, MRI)报告的前列腺体积、前列腺穿刺活检的Gleason评分、穿刺前及术前血清前列腺特异性抗原(prostate specific antigen, PSA)、手术方式、术后病理分期的差异,将单因素分析中P<0.1的术前变量纳入多因素Logistic回归并绘制列线图,通过受试者工作特征曲线对模型进行评价。结果:共有71例患者符合纳入排除标准,其中术后病理升级组34例,未升级组37例,两组患者的年龄(P=0.585)、体重指数(P=0.165)、手术方式(P=0.08)、MRI前列腺体积(P=0.067)、临床分期(P=0.678)、PI-RADS评分(P=0.203)、穿刺前PSA(P=0.359)、术前PSA(P=0.739)、PSA密度差(P=0.063)、穿刺Gleason评分(P=0.068)差异均无统计学意义,两组患者穿刺阳性针中肿瘤组织占比(P=0.007)、术后病理分期(P<0.001)及术后Gleason评分(P<0.001)差异有统计学意义。将单因素分析中P<0.1的术前变量,即MRI前列腺体积、PSA密度差、穿刺阳性针中的肿瘤组织占比、穿刺Gleason评分纳入多因素Logistic回归分析,只有MRI前列腺体积组间差异有统计学意义。进一步根据多因素Logistic回归结果绘制列线图,受试者工作特征曲线的曲线下面积为0.773。结论:对于穿刺病理单针阳性的前列腺癌患者,若前列腺体积较小或穿刺阳性针中肿瘤组织占比较少,需警惕术后病理较穿刺病理升级的可能;对于可能出现病理升级的患者,需谨慎考虑术前的危险分层。本模型可初步用于预测穿刺活检单针阳性前列腺癌患者术后病理升级的可能性。展开更多
Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in d...Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.展开更多
文摘目的:对穿刺活检单针阳性前列腺癌术后病理升级的危险因素进行分析,并尝试构建预测穿刺单针阳性前列腺癌患者术后病理升级的数学模型。方法:回顾分析2015年1月至2020年8月期间于北京大学第一医院诊断为前列腺癌且接受根治性前列腺切除术的患者1 349例,选取其中穿刺活检单针阳性患者的临床资料,将其分为术后病理较穿刺病理升级组及未升级组,比较两组的年龄、体重指数、临床分期、前列腺影像报告和数据系统(prostate imaging reporting and data system, PI-RADS)评分、磁共振成像(magnetic resonance imaging, MRI)报告的前列腺体积、前列腺穿刺活检的Gleason评分、穿刺前及术前血清前列腺特异性抗原(prostate specific antigen, PSA)、手术方式、术后病理分期的差异,将单因素分析中P<0.1的术前变量纳入多因素Logistic回归并绘制列线图,通过受试者工作特征曲线对模型进行评价。结果:共有71例患者符合纳入排除标准,其中术后病理升级组34例,未升级组37例,两组患者的年龄(P=0.585)、体重指数(P=0.165)、手术方式(P=0.08)、MRI前列腺体积(P=0.067)、临床分期(P=0.678)、PI-RADS评分(P=0.203)、穿刺前PSA(P=0.359)、术前PSA(P=0.739)、PSA密度差(P=0.063)、穿刺Gleason评分(P=0.068)差异均无统计学意义,两组患者穿刺阳性针中肿瘤组织占比(P=0.007)、术后病理分期(P<0.001)及术后Gleason评分(P<0.001)差异有统计学意义。将单因素分析中P<0.1的术前变量,即MRI前列腺体积、PSA密度差、穿刺阳性针中的肿瘤组织占比、穿刺Gleason评分纳入多因素Logistic回归分析,只有MRI前列腺体积组间差异有统计学意义。进一步根据多因素Logistic回归结果绘制列线图,受试者工作特征曲线的曲线下面积为0.773。结论:对于穿刺病理单针阳性的前列腺癌患者,若前列腺体积较小或穿刺阳性针中肿瘤组织占比较少,需警惕术后病理较穿刺病理升级的可能;对于可能出现病理升级的患者,需谨慎考虑术前的危险分层。本模型可初步用于预测穿刺活检单针阳性前列腺癌患者术后病理升级的可能性。
文摘Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.
