基于 ResNet 预测放疗中个体化 QA 中的误差幅度。

Predicting the error magnitude in patient-specific QA during radiotherapy based on ResNet.

机构信息

Institute of Modern Physics, Fudan University, Shanghai, China.

Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, China.

出版信息

J Xray Sci Technol. 2024;32(3):797-807. doi: 10.3233/XST-230251.

DOI:10.3233/XST-230251

PMID:38457139

Abstract

BACKGROUND

The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude.

OBJECTIVE

The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet.

METHODS

A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude.

RESULTS

In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS.

CONCLUSIONS

In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

摘要

背景

误差幅度与患者特定剂量密切相关，在 QA 中评估放疗计划的实施中起着重要作用。以前没有研究调查过深度学习预测误差幅度的可行性。

目的

本研究旨在基于 ResNet 预测放疗中不同传输误差类型的误差幅度。

方法

从 Eclipse 中选择了 34 例胸部癌症调强放疗（IMRT）计划（172 个射野），其中 30 个计划（151 个射野）用于模型训练和验证，4 个计划（21 个射野）用于外部测试。引入准直器失准（COLL）、MU 变化、随机多叶准直器移位（MLCR）和系统 MLC 移位（MLCS）。这些原始计划的射野剂量预测的剂量分布被定义为参考剂量分布（RDD），而引入误差的计划的剂量分布则被定义为引入误差的剂量分布（EDD）。ResNet 中使用了不同的输入来预测误差幅度。

结果

在测试集中，基于剂量差、伽马分布和 RDD+EDD 的误差类型预测准确率分别为 98.36%、98.91%和 100%；均方根误差（RMSE）分别为 1.45-1.54、0.58-0.90、0.32-0.36 和 0.15-0.24；平均绝对误差（MAE）分别为 1.06-1.18、0.32-0.78、0.25-0.27 和 0.11-0.18，用于 COLL、MU、MLCR 和 MLCS。