Ben-Josef E, Shamsa F, Forman J D
Department of Radiation Oncology, Wayne State University, The Barbara Ann Karmanos Cancer Institute, Detroit, Michigan, USA.
Cancer. 1998 Apr 1;82(7):1334-42. doi: 10.1002/(sici)1097-0142(19980401)82:7<1334::aid-cncr17>3.0.co;2-7.
Clinical research of prostate carcinoma could be enhanced by models that allow early and reliable prediction of outcome. In this study, the authors describe a model-building strategy and compare different models.
The sample population was comprised of 158 patients treated definitively with radiotherapy. Univariate and multivariate logistic regression analyses were conducted to identify prognostic factors and select the best predictive model. Variables included age, race, method of diagnosis (needle biopsy vs. transurethral resection of the prostate), stage, grade, pretreatment prostate specific antigen (PSA), in-treatment PSA (PSA(tx)), posttreatment PSA (PSA(post)), and nadir PSA. The following indices were used to compare discriminatory power: log-likelihood function, Akaike information criterion, the generalized coefficient of determination, and the area under the receiver operating characteristic curve.
At last follow-up, 49 patients (31%) had recurrence of carcinoma. By univariate analysis, the failure rate was significantly higher in patients with advanced stage, higher grade, higher pretherapy PSA, and nadir PSA > 1 ng/mL (P < 0.0001). Pretherapy PSA was associated significantly with stage, age, and nadir PSA (P = 0.001, P = 0.001, and P = 0.001, respectively). All PSA measurements were significantly interrelated. Nadir PSA was the most predictive variable. Significant gains (P = 0.01) in predictive power were derived from inclusion of PSA(tx), but not PSA (post). Age, race, stage, grade, and method of diagnosis contributed predictive power in addition to that derived from PSA levels (P = 0.01, log-likelihood test). The authors' model of choice predicts outcome with an overall correctness, sensitivity, specificity, and false-negative rate of 81.8%, 87.2%, 79.6%, and 12.8%, respectively.
Applying the strategy described, a model was selected that allowed accurate prediction of failure shortly after the completion of therapy.
能够对结果进行早期且可靠预测的模型可增强前列腺癌的临床研究。在本研究中，作者描述了一种模型构建策略并比较了不同模型。
样本人群由158例接受根治性放疗的患者组成。进行单因素和多因素逻辑回归分析以确定预后因素并选择最佳预测模型。变量包括年龄、种族、诊断方法（穿刺活检与经尿道前列腺切除术）、分期、分级、治疗前前列腺特异性抗原（PSA）、治疗中PSA（PSA(tx)）、治疗后PSA（PSA(post)）以及最低点PSA。使用以下指标比较判别能力：对数似然函数、赤池信息准则、广义决定系数以及受试者工作特征曲线下面积。
在最后一次随访时，49例患者（31%）出现癌复发。单因素分析显示，晚期、高分级、治疗前PSA水平较高以及最低点PSA>1 ng/mL的患者失败率显著更高（P<0.0001）。治疗前PSA与分期、年龄和最低点PSA显著相关（分别为P = 0.001、P = 0.001和P = 0.001）。所有PSA测量值均显著相关。最低点PSA是最具预测性的变量。纳入PSA(tx)可显著提高预测能力（P = 0.01），但纳入PSA(post)则不然。除了PSA水平所提供的预测能力外，年龄、种族、分期、分级和诊断方法也具有预测能力（P = 0.01，对数似然检验）。作者选择的模型预测结果的总体正确率、敏感性、特异性和假阴性率分别为81.8%、87.2%、79.6%和12.8%。
应用所描述的策略，选择了一个能够在治疗完成后不久准确预测失败情况的模型。
