Prediction of dose distributions for non-small cell lung cancer patients using MHA-ResUNet

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2024-07-18 DOI:10.1002/mp.17308

Haifeng Zhang, Yanjun Yu, Fuli Zhang

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引用次数: 0

Abstract

Background

The current level of automation in the production of radiotherapy plans for lung cancer patients is relatively low. With the development of artificial intelligence, it has become a reality to use neural networks to predict dose distributions and provide assistance for radiation therapy planning. However, due to the significant individual variability in the distribution of non-small cell lung cancer (NSCLC) planning target volume (PTV) and the complex spatial relationships between the PTV and organs at risk (OARs), there is still a lack of a high-precision dose prediction network tailored to the characteristics of NSCLC.

Purpose

To assist in the development of volumetric modulated arc therapy (VMAT) plans for non-small cell lung cancer patients, a deep neural network is proposed to predict high-precision dose distribution.

Methods

This study has developed a network called MHA-ResUNet, which combines a large-kernel dilated convolution module and multi-head attention (MHA) modules. The network was trained based on 80 VMAT plans of NSCLC patients. CT images, PTV, and OARs were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks' performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics.

Results

The MAE between the predicted dose distribution and the manually planned dose distribution within the PTV is 1.43 Gy, and the D95 error is less than 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA-ResUNet is the smallest in PTV and OARs.

Conclusions

The proposed MHA-ResUNet network improves the receptive field and filters the shallow features to learn the relative spatial relation between the PTV and the OARs, enabling accurate prediction of dose distributions in NSCLC patients undergoing VMAT radiotherapy.

查看原文本刊更多论文

使用 MHA-ResUNet 预测非小细胞肺癌患者的剂量分布。

背景：目前，为肺癌患者制作放疗计划的自动化程度相对较低。随着人工智能的发展，使用神经网络预测剂量分布并为放疗计划提供帮助已成为现实。然而，由于非小细胞肺癌（NSCLC）计划靶体积（PTV）的分布存在明显的个体差异，且PTV与危险器官（OAR）之间的空间关系复杂，目前仍缺乏针对NSCLC特点的高精度剂量预测网络。目的：为协助制定非小细胞肺癌患者的容积调强弧形治疗（VMAT）计划，提出了一种深度神经网络来预测高精度剂量分布：本研究开发了一种名为 MHA-ResUNet 的网络，它结合了大核扩张卷积模块和多头注意力（MHA）模块。该网络基于 80 例 NSCLC 患者的 VMAT 计划进行训练。CT 图像、PTV 和 OAR 被输入独立的输入通道。剂量分布作为训练模型的输出。该网络的性能与几种常用网络的性能进行了比较，并根据PTV和OAR内的体素级平均绝对误差（MAE）以及临床剂量-体积指标的误差对网络的性能进行了评估：PTV内预测剂量分布与人工计划剂量分布之间的MAE为1.43 Gy，D95误差小于1 Gy。与其他三种常用网络相比，MHA-ResUNet 在 PTV 和 OAR 中的剂量误差最小：结论：所提出的 MHA-ResUNet 网络改善了感受野，过滤了浅层特征，学习了 PTV 和 OAR 之间的相对空间关系，从而能够准确预测接受 VMAT 放射治疗的 NSCLC 患者的剂量分布。

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

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.