A feasibility study to predict 3D dose delivery accuracy for IMRT using DenseNet with log files.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-01-01 DOI:10.3233/XST-230412

Ying Huang, Ruxin Cai, Yifei Pi, Kui Ma, Qing Kong, Weihai Zhuo, Yan Kong

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引用次数: 0

Abstract

Objective: This study aims to explore the feasibility of DenseNet in the establishment of a three-dimensional (3D) gamma prediction model of IMRT based on the actual parameters recorded in the log files during delivery.

Methods: A total of 55 IMRT plans (including 367 fields) were randomly selected. The gamma analysis was performed using gamma criteria of 3% /3 mm (Dose Difference/Distance to Agreement), 3% /2 mm, 2% /3 mm, and 2% /2 mm with a 10% dose threshold. In addition, the log files that recorded the gantry angle, monitor units (MU), multi-leaf collimator (MLC), and jaws position during delivery were collected. These log files were then converted to MU-weighted fluence maps as the input of DenseNet, gamma passing rates (GPRs) under four different gamma criteria as the output, and mean square errors (MSEs) as the loss function of this model.

Results: Under different gamma criteria, the accuracy of a 3D GPR prediction model decreased with the implementation of stricter gamma criteria. In the test set, the mean absolute error (MAE) of the prediction model under the gamma criteria of 3% /3 mm, 2% /3 mm, 3% /2 mm, and 2% /2 mm was 1.41, 1.44, 3.29, and 3.54, respectively; the root mean square error (RMSE) was 1.91, 1.85, 4.27, and 4.40, respectively; the Sr was 0.487, 0.554, 0.573, and 0.506, respectively. There was a correlation between predicted and measured GPRs (P < 0.01). Additionally, there was no significant difference in the accuracy between the validation set and the test set. The accuracy in the high GPR group was high, and the MAE in the high GPR group was smaller than that in the low GPR group under four different gamma criteria.

Conclusions: In this study, a 3D GPR prediction model of patient-specific QA using DenseNet was established based on log files. As an auxiliary tool for 3D dose verification in IMRT, this model is expected to improve the accuracy and efficiency of dose validation.

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使用带有日志文件的 DenseNet 预测 IMRT 3D 剂量输送准确性的可行性研究。

研究目的本研究旨在探索 DenseNet 在建立 IMRT 的三维（3D）伽马预测模型方面的可行性，该模型基于投放过程中记录在日志文件中的实际参数：方法：随机选取了55个IMRT计划（包括367个场）。伽马分析采用的伽马标准为 3% /3 mm（剂量差/一致距离）、3% /2 mm、2% /3 mm 和 2% /2 mm，剂量阈值为 10%。此外，还收集了记录投放过程中龙门架角度、监控单元（MU）、多叶准直器（MLC）和钳口位置的日志文件。然后，将这些日志文件转换成 MU 加权通量图，作为 DenseNet 的输入；将四种不同伽马标准下的伽马通过率（GPR）作为输出；将均方误差（MSE）作为该模型的损失函数：结果：在不同的伽马标准下，三维 GPR 预测模型的准确度随着实施更严格的伽马标准而降低。在测试集中，预测模型在 3% /3 mm、2% /3 mm、3% /2 mm 和 2% /2 mm 伽马标准下的平均绝对误差（MAE）分别为 1.41、1.44、3.29 和 3.54；均方根误差（RMSE）分别为 1.91、1.85、4.27 和 4.40；Sr 分别为 0.487、0.554、0.573 和 0.506。预测的 GPR 与测量的 GPR 之间存在相关性（P < 0.01）。此外，验证集和测试集之间的准确率没有明显差异。在四种不同的伽马标准下，高 GPR 组的准确率较高，且高 GPR 组的 MAE 小于低 GPR 组：本研究基于日志文件，利用 DenseNet 建立了患者特定 QA 的三维 GPR 预测模型。作为 IMRT 中三维剂量验证的辅助工具，该模型有望提高剂量验证的准确性和效率。

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

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.