000130444 001__ 130444
000130444 005__ 20240125162930.0
000130444 0247_ $$2doi$$a10.1016/S0022-5347(05)65651-0
000130444 0248_ $$2sideral$$a109361
000130444 037__ $$aART-2001-109361
000130444 041__ $$aeng
000130444 100__ $$0(orcid)0000-0003-0178-4567$$aBorque, A.$$uUniversidad de Zaragoza
000130444 245__ $$aThe use of neural networks and logistic regression analysis for predicting pathological stage in men undergoing radical prostatectomy: A population based study
000130444 260__ $$c2001
000130444 5203_ $$aPurpose: Clinical under staging occurs in 40% to 60% of patients who undergo radical prostatectomy for prostate cancer. To decrease under staging several methods of predicting pathological stage preoperatively have been developed based on statistical logistic regression analysis and neural networks. To our knowledge none has been validated in our homogeneous regional patient population to date. We created logistic regression and neural network models, and implemented and adapted them into our practice. We also compared the 2 methods to determine their value and practicality in daily clinical practice. We present the results of our novel approach for predicting pathological staging of prostate adenocarcinoma. Materials and Methods: Between 1986 and 1999, 600 white men from the Aragon region of Spain underwent surgery for prostate cancer; of whom 468 were selected for study. Predictive study variables included patient age, clinical stage, biopsy Gleason score and preoperative prostate specific antigen (PSA). The predicted result included in analysis was organ confined or nonorgan confined disease. Data were analyzed by multivariate logistic regression and a supervised neural network (multilayer perceptron and radial basis function). Results were compared by comparing the areas under the receiver operating characteristics curves. Results: We generated 5 logistic regression models. The model created with clinical staging, Gleason biopsy score and PSA distributed in 5 categories (p<0.001) with an area under the receiver operating characteristics curve of 0.840 proved to be most predictive of pathological stage. Similarly of the 6 neural network models evaluated the radial basis function model, which included age, clinical stage, Gleason biopsy score and preoperative PSA distributed in 5 categories with an area under the curve of 0.882, proved the most predictive but not superior to the logistic regression model. The difference in the area under the curves in the 2 chosen models was 0.042 (p=0.1). Conclusions: It is possible to generate useful predictive models of organ confined disease using logistic regression or neural networks with high indexes of clinical and statistical validity. However, using these variables neural networks did not prove to be better than logistic regression analysis. Therefore, better predictive variables must be identified, preferably nonlinear characteristics with respect to the probability of organ confined tumor, to generate better predictive models using neural networks.
000130444 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000130444 590__ $$a3.19$$b2001
000130444 591__ $$aUROLOGY & NEPHROLOGY$$b6 / 44 = 0.136$$c2001$$dQ1$$eT1
000130444 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000130444 700__ $$0(orcid)0000-0002-6474-2252$$aSanz, G.$$uUniversidad de Zaragoza
000130444 700__ $$aAllepuz, C.$$uUniversidad de Zaragoza
000130444 700__ $$aPlaza, L.
000130444 700__ $$aGil, P.
000130444 700__ $$aRioja, L.A.$$uUniversidad de Zaragoza
000130444 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000130444 7102_ $$11004$$2090$$aUniversidad de Zaragoza$$bDpto. Cirugía,Ginecol.Obstetr.$$cÁrea Cirugía
000130444 773__ $$g166, 5 (2001), 1672-1678$$pJ. urol.$$tJOURNAL OF UROLOGY$$x0022-5347
000130444 8564_ $$s99248$$uhttps://zaguan.unizar.es/record/130444/files/texto_completo.pdf$$yVersión publicada
000130444 8564_ $$s3618523$$uhttps://zaguan.unizar.es/record/130444/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000130444 909CO $$ooai:zaguan.unizar.es:130444$$particulos$$pdriver
000130444 951__ $$a2024-01-25-15:11:03
000130444 980__ $$aARTICLE