USC Institute of Urology, USC/Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089-2211, USA.
BJU Int. 2013 Apr;111(4 Pt B):E167-72. doi: 10.1111/j.1464-410X.2012.11502.x. Epub 2012 Oct 4.
To develop a model that integrates the clinical and pathological information prior to radical cystectomy to increase the accuracy of current clinical stage in prediction of pathological stage in patients with bladder cancer (BC) using a modelling approach called principal component analysis (PCA).
In a single-centre retrospective study, demographic and clinicopathological information of 1186 patients with clinically organ-confined (OC) BC was reviewed. Putative predictors of post-cystectomy pathological stage were identified using a stepwise logistic regression model. Patients were randomly divided into training data set (two-thirds of the study population, 790 patients) and test data set (one-third of the study population, 396 patients). The PCA method was used to develop the model in the training data set and the cut-off point (PCA score) to differentiate pathological OC disease from extravesical disease was determined. The model was then applied to the test data set without recalculation.
In all, 685 patients (57.7%) had pathological OC disease. Age, clinical stage, number of intravesical treatments, lymphovascular invasion, multiplicity of tumours, hydronephrosis and palpable mass were incorporated into the PCA model as predictors of pathological stage. The sensitivity and specificity of the PCA model in the test data set were 62.8% (95% CI 55.6%-68.1%) and 68.9% (95% CI 60.8%-76.0%), respectively. The positive and negative predictive values were 75.8% (95% CI 69.0%-81.6%) and 51.5% (95% CI 44.4%-58.5%), respectively.
The pre-cystectomy PCA model improved the ability to differentiate OC disease from extravesical BC and especially decreased the under-staging rate. The pre-cystectomy PCA model represented a user-friendly staging aid without the need for sophisticated statistical interpretation.
通过主成分分析（PCA）建模方法，建立一个整合根治性膀胱切除术（RC）前临床和病理信息的模型，以提高目前临床分期预测膀胱癌（BC）患者病理分期的准确性。
在单中心回顾性研究中，回顾了 1186 例临床局限（OC）BC 患者的人口统计学和临床病理信息。使用逐步逻辑回归模型确定 RC 后病理分期的潜在预测因素。患者被随机分为训练数据集（研究人群的三分之二，790 例患者）和测试数据集（研究人群的三分之一，396 例患者）。在训练数据集中使用 PCA 方法建立模型，并确定区分病理 OC 疾病和膀胱外疾病的截断点（PCA 评分）。然后将该模型应用于测试数据集，无需重新计算。
共有 685 例患者（57.7%）发生病理 OC 疾病。年龄、临床分期、膀胱内治疗次数、脉管浸润、肿瘤多发性、肾积水和可触及肿块被纳入 PCA 模型，作为病理分期的预测因素。PCA 模型在测试数据集中的灵敏度和特异度分别为 62.8%（95%CI，55.6%-68.1%）和 68.9%（95%CI，60.8%-76.0%）。阳性和阴性预测值分别为 75.8%（95%CI，69.0%-81.6%）和 51.5%（95%CI，44.4%-58.5%）。
RC 前 PCA 模型提高了区分 OC 疾病与膀胱外 BC 的能力，特别是降低了分期不足的发生率。RC 前 PCA 模型是一种易于使用的分期辅助工具，无需复杂的统计解释。
