Principal component analysis based pre-cystectomy model to predict pathological stage in patients with clinical organ-confined bladder cancer
Ahmadi H, Mitra AP, Abdelsayed GA, Cai J, Djaladat H, Bruins HM, Daneshmand S. BJU Int. 2012 Oct 4. doi: 10.1111/j.1464-410X.2012.11502.x. [Epub ahead of print]


USC Institute of Urology, USC/Norris Comprehensive Cancer Center USC Department of Pathology and Center for Personalized Medicine, University of Southern California, Los Angeles, CA, USA.


What's known on the subject? and What does the study add? Clinical stage is an integral part of outlining treatment strategy and counselling patients with bladder cancer. The discordance rate between clinical stage and pathological stage, however, is currently high with more than 40% of patients with presumed organ-confined disease being upstaged after surgery. It accounts for the major part of overall pathological upstaging in patients with bladder cancer and is strongly associated with poor prognosis. There is an absolute need for additional methods to improve the accuracy of pre-surgical staging to predict post-surgical pathological stage of invasive bladder cancer for more accurate risk stratification. This study presents an internally validated pre-cystectomy principal component analysis staging model involving clinicopathological information on a large cohort of patients to predict post-surgical stage of bladder cancer. This model greatly reduces pathological upstaging to extravesical disease compared with clinical staging alone.


•  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.