未来的膀胱癌放射肿瘤学:预后建模、放射组学和人工智能治疗计划

IF 2.6 3区医学 Q3 ONCOLOGY

Seminars in Radiation Oncology Pub Date : 2023-01-01 DOI:10.1016/j.semradonc.2022.10.009

Nicholas S. Moore MD , Alan McWilliam PhD , Sanjay Aneja MD

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引用次数: 3

摘要

机器学习（ML）和人工智能（AI）已显示出改善放射肿瘤学患者护理的潜力。在此，我们回顾了适用于膀胱癌症治疗的最新进展，着眼于可能建议临床实施下一步的研究。算法已应用于临床记录、病理学和放射学数据，以生成准确的预后和临床结果预测模型。人工智能在自动轮廓绘制和高效创建涉及多个治疗计划的工作流程方面也显示出越来越大的实用性。随着技术向癌症患者的常规临床应用发展，我们还讨论了提高算法可解释性和可靠性的新方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable 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 outcomes. 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.

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来源期刊

Seminars in Radiation Oncology 医学-核医学

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Each issue of Seminars in Radiation Oncology is compiled by a guest editor to address a specific topic in the specialty, presenting definitive information on areas of rapid change and development. A significant number of articles report new scientific information. Topics covered include tumor biology, diagnosis, medical and surgical management of the patient, and new technologies.