Machine learning for prediction of survival outcomes with immune-checkpoint inhibitors in urothelial cancer
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Date
2021
Authors
Abuhelwa, A.Y.
Kichenadasse, G.
McKinnon, R.A.
Rowland, A.
Hopkins, A.M.
Sorich, M.J.
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Journal article
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Cancers, 2021; 13(9, article no. 2001):1-9
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Abstract
Machine learning (ML) is a form of artificial intelligence that could be used to enhance the efficiency of developing accurate prediction models for survival outcomes with cancer medicines, which is critical in informing disease prognosis and care planning. We used data from two recent clinical trials to develop and validate ML‐based clinical prediction models of the overall and progression‐free survival rates in patients with urothelial cancer initiating the immune checkpoint inhibitor (ICI) atezolizumab. We demonstrated that ML can efficiently develop an accurate prediction model of survival, enable an accurate prognostic risk classification, and provide realistic expectations of treatment outcomes in patients undergoing urothelial cancer-initiating ICIs therapy.
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Data source: Supplementary Materials, https://www.mdpi.com/2072-6694/13/9/2001/s1?version=1619004965
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Copyright 2021 by the authors. Licensee MDPI, Basel, Switzerland.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. (https://creativecommons.org/licenses/by/4.0/)