Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-12-31 DOI:10.1007/s12194-023-00768-5
Sana Salahuddin, Saeed Ahmad Buzdar, Khalid Iqbal, Muhammad Adeel Azam, Lidia Strigari
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引用次数: 0

Abstract

This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.

利用机器学习为立体定向体放射治疗中的等中心稳定性提供高效质量保证。
本研究旨在利用机器学习(ML)预测立体定向体放射治疗(SBRT)治疗的等中心稳定性,应对质量保证(QA)中人工评估和计算时间的挑战,并支持医学物理学家提高准确性。在使用用于 SBRT 的 TrueBeam 直列加速器进行质量保证期间,使用 RUBY 模型对准直器 (C)、龙门 (G) 和工作台 (T) 的等中心参数进行了测试。该分析结合了 IsoCheck EPID 软件的统计功能。五种 ML 模型,包括逻辑回归 (LR)、决策树 (DT)、随机森林 (RF)、天真贝叶斯 (NB) 和支持向量机 (SVM) 被用于预测 QA 程序的结果。从 2020 年到 2022 年,共收集了 247 次 Winston-Lutz (WL) 测试。在我们的研究中,DT 和 RF 在三种模式(C、G 和 T)的测试准确度(Acc. test)上都取得了 93.5% 到 99.4% 的最高分,曲线下面积(AUC)值从 90% 到 100% 不等。精确度、召回率和 F1 分数表明,在质量保证中,DT 模型在预测等中心稳定性偏差方面始终优于其他 ML 模型。使用 ML 模型进行 QA 评估有助于及早预测错误,从而避免 SBRT 过程中的潜在伤害,确保对患者进行安全有效的治疗。
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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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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