Kuo Shu-Chun, Hung Shun-Hsing, Wang Hsien-Yi, Chien Chih-Chiang, Lu Chin-Li, Lin Hung-Jung, Guo How-Ran, Zou Jian-Fang, Lin Chian-Shiung, Huang Chien-Cheng
1] Department of Ophthalmology, Chi-Mei Medical Center, Tainan 710 [2] Department of Optometry, Chung Hwa University of Medical Technology, Tainan 710.
Asian J Androl. 2013 Nov;15(6):780-4. doi: 10.1038/aja.2013.100. Epub 2013 Oct 14.
Several nomograms for prostate cancer detection have recently been developed. Because the incidence of prostate cancer is lower in Chinese men, nomograms based on other populations cannot be directly applied to Chinese men. We, therefore, developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Chinese male population. Data were collected from 893 Chinese male referrals, 697 in the derivation set and 196 in the external validation set, who underwent initial prostate biopsies as individual screening. We analyzed age, prostate volume, total prostate-specific antigen (PSA), PSA density (PSAD), digital rectal examinations (DRE) and transrectal ultrasound (TRUS) echogenicity. Logistic regression analysis estimated odds ratio, 95% confidence intervals and P values. Independent predictors of a positive biopsy result included advanced age, small prostate volume, elevated total PSA, abnormal digital rectal examination, and hyperechoic or hypoechoic TRUS echogenicity. We developed a predictive nomogram for an initial positive biopsy using these variables. The area under the receiver-operating characteristic curve for the model was 88.8%, which was greater than that of the prediction based on total PSA alone (area under the receiver-operating characteristic curve 74.7%). If externally validated, the predictive probability was 0.827 and the accuracy rate was 78.1%, respectively. Incorporating clinical and laboratory data into a prebiopsy nomogram improved the prediction of prostate cancer compared with predictions based solely on the individual factors.
最近已经开发出了几种用于前列腺癌检测的列线图。由于中国男性前列腺癌的发病率较低,基于其他人群的列线图不能直接应用于中国男性。因此,我们利用来自中国男性人群的临床和实验室数据,开发了一种预测初次前列腺活检阳性概率的模型。数据收集自893名中国男性转诊患者,其中697名在推导集,196名在外部验证集,他们接受了作为个体筛查的初次前列腺活检。我们分析了年龄、前列腺体积、总前列腺特异性抗原(PSA)、PSA密度(PSAD)、直肠指检(DRE)和经直肠超声(TRUS)回声。逻辑回归分析估计了比值比、95%置信区间和P值。活检结果阳性的独立预测因素包括高龄、前列腺体积小、总PSA升高、直肠指检异常以及TRUS回声高回声或低回声。我们使用这些变量开发了一种初次活检阳性的预测列线图。该模型的受试者操作特征曲线下面积为88.8%,大于仅基于总PSA的预测(受试者操作特征曲线下面积74.7%)。如果经过外部验证,预测概率分别为0.827,准确率为78.1%。与仅基于个体因素的预测相比,将临床和实验室数据纳入活检前列线图可改善前列腺癌的预测。