Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence

被引:5
|
作者
Moore, Nicholas S. [1 ]
McWilliam, Alan [2 ]
Aneja, Sanjay [1 ,3 ]
机构
[1] Yale Sch Med, Dept Therapeut Radiol, New Haven, CT USA
[2] Univ Manchester, Fac Biol Med & Hlth, Div Canc Sci, Manchester, England
[3] Yale Sch Med, Dept Therapeut Radiol, New Haven, CT 06511 USA
关键词
D O I
10.1016/j.semradonc.2022.10.009
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applica-ble to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical out-comes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.Semin Radiat Oncol 33:70-75 (c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码:70 / 75
页数:6
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