OBJECTIVE: 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). PATIENTS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: 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). PATIENTS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Manuel S Eisenberg; John C Cheville; R Houston Thompson; Dharam Kaushik; Christine M Lohse; Stephen A Boorjian; Brian A Costello; Bradley C Leibovich Journal: J Urol Date: 2013-01-23 Impact factor: 7.450
Authors: Anirban P Mitra; Jie Cai; Gus Miranda; Sumeet Bhanvadia; David I Quinn; Anne K Schuckman; Hooman Djaladat; Siamak Daneshmand Journal: J Urol Date: 2021-11-08 Impact factor: 7.450