title: [Impact of predictive models and decision making in prostate cancer: An integral debate].
abstract: OBJECTIVE
  To review the various methods to predict the risk of having prostate cancer, or that localized disease may be cured or progress after a given treatment.
  
  
  METHODS
  We performed a review of the various mathematic models known for the probability analysis of the event, with a critical analysis of weaknesses and strengths of each method. In a Medline update we review the most relevant papers referred to diagnosis and management of localized prostate cancer in its diagnosis and management sides, as well as the probability of developing metastatic disease and to die.
  
  
  RESULTS
  There are multiple methods and models to predict the various events in a patient candidate to diagnosis of prostate cancer, as well as to analyze the possibilities of success of a specific treatment, in many cases with an important exactness. We emphasize the heterogeneity in the methods, data and variables used for the analysis, basically about retrospective studies. Many of the most sophisticated methods, Neural Network or cart, do not present greater exactness than classic methods like logistic regression.
  
  
  CONCLUSIONS
  Predictive models are an important element for decision making in usual clinical practice, favoring the decision of a diagnosis or certain treatment is not taken in a random manner and therefore it is taken following scientific criteria. Waiting for more precise methods, we have to know no method is perfect, and therefore it is an important tool, which should not by pass personal knowledge or the experience of a specific working group.
authors: Servicio de Urología, Unidad de Uro-Oncología, Complexo Hospitalario Universitario de a Coruña, España. fgveiga@telefonica.net
authors: Francisco
authors: F
authors: Gómez Veiga
authors: Jose
authors: J
authors: Ponce Díaz-Reixa
authors: Sonia
authors: S
authors: Pértega Díaz
authors: Sara
authors: S
authors: Martínez Breijo
authors: Juan
authors: J
authors: Gonzalez Dacal
authors: Anton Zarraonaindia
authors: AZ
authors: Andraca
authors: Javier Casas
authors: JC
authors: Nebra
authors: Daniel López
authors: DL
authors: García
authors: Salvador Pita
authors: SP
authors: Fernández
authors: Venancio Chantada
authors: VC
authors: Abal
mesh_terms: D000230:Adenocarcinoma / Q000453:epidemiology* / Q000473:pathology / Q000628:therapy; D000970:Antineoplastic Agents / Q000627:therapeutic use; D001707:Biopsy, Needle; D003131:Combined Modality Therapy; D003661:Decision Support Techniques; D003663:Decision Trees; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D060787:Neoplasm Grading; D009367:Neoplasm Staging / Q000379:methods; D016571:Neural Networks, Computer; D049451:Nomograms; D011336:Probability; D011379:Prognosis; D011468:Prostatectomy / Q000379:methods; D011471:Prostatic Neoplasms / Q000453:epidemiology* / Q000473:pathology / Q000628:therapy; D011788:Quality of Life; D011878:Radiotherapy / Q000379:methods; D012044:Regression Analysis; D018570:Risk Assessment / Q000706:statistics & numerical data; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents