Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning

被引：42

作者：

Kalet, Alan M. ^{[1
]}

Luk, Samuel M. H. ^{[1
]}

Phillips, Mark H. ^{[1
]}

机构：

[1] Univ Washington, Dept Radiat Oncol, Med Ctr, Seattle, WA 98195 USA

来源：

MEDICAL PHYSICS | 2020年 / 47卷 / 05期

关键词：

quality assurance; machine learning; artificial intelligence; radiotherapy; DECISION-SUPPORT-SYSTEMS; MODULATED ARC THERAPY; ERROR-DETECTION; CLINICAL-DATA; MISSING DATA; QA; PREDICTION; ONCOLOGY; CANCER; VERIFICATION;

D O I：

10.1002/mp.13445

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

The recent explosion in machine learning efforts in the quality assurance (QA) space has produced a variety of proofs-of-concept many with promising results. Expected outcomes of model implementation include improvements in planning time, plan quality, advanced dosimetric QA, predictive machine maintenance, increased safety checks, and developments key for new QA paradigms driven by adaptive planning. In this article, we outline several areas of research and discuss some of the unique challenges each area presents.

引用

页码：E168 / E177

页数：10

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