title: Prediction tools in surgical oncology.
abstract: Artificial neural networks, prediction tables, and clinical nomograms allow physicians to transmit an immense amount of prognostic information in a format that exhibits comprehensibility and brevity. Current models demonstrate the feasibility to accurately predict many oncologic outcomes, including pathologic stage, recurrence-free survival, and response to adjuvant therapy. Although emphasis should be placed on the independent validation of existing prediction tools, there is a paucity of models in the literature that focus on quality of life outcomes. The unification of tools that predict oncologic and quality of life outcomes into a comparative effectiveness table will furnish patients with cancer with the information they need to make a highly informed and individualized treatment decision.
authors.affiliation: Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue/Q-10, Cleveland, OH 44195, USA.
authors.forename: Brandon K
authors.initials: BK
authors.lastname: Isariyawongse
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
mesh_terms: D003661:Decision Support Techniques; D018572:Disease-Free Survival; D005544:Forecasting; D006801:Humans; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D009369:Neoplasms / Q000601:surgery*; D016571:Neural Networks, Computer; D049451:Nomograms; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods; D016896:Treatment Outcome