Prediction tools in surgical oncology |
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Authors: | Isariyawongse Brandon K Kattan Michael W |
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Institution: | Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue/Q-10, Cleveland, OH 44195, USA. |
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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. |
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