Respiratory Prediction Based on Multi-Scale Temporal Convolutional Network for Tracking Thoracic Tumor Movement

Overview

Journal Front Oncol

Specialty Oncology

Date 2022 Jun 13

PMID 35692785

Authors

Lijuan Shi

Shuai Han

Jian Zhao

Zhejun Kuang

Weipeng Jing

Yuqing Cui

Zhanpeng Zhu

Affiliations

Soon will be listed here.

Abstract

Radiotherapy is one of the important treatments for malignant tumors. The precision of radiotherapy is affected by the respiratory motion of human body, so real-time motion tracking for thoracoabdominal tumors is of great significance to improve the efficacy of radiotherapy. This paper aims to establish a highly precise and efficient prediction model, thus proposing to apply a depth prediction model composed of multi-scale enhanced convolution neural network and temporal convolutional network based on empirical mode decomposition (EMD) in respiratory prediction with different delay times. First, to enhance the precision, the unstable original sequence is decomposed into several intrinsic mode functions (IMFs) by EMD, and then, a depth prediction model of parallel enhanced convolution structure and temporal convolutional network with the characteristics specific to IMFs is built, and finally training on the respiratory motion dataset of 103 patients with malignant tumors is conducted. The prediction precision and time efficiency of the model are compared at different levels with those of the other three depth prediction models so as to evaluate the performance of the model. The result shows that the respiratory motion prediction model determined in this paper has superior prediction performance under different lengths of input data and delay time, and, furthermore, the network update time is shortened by about 60%. The method proposed in this paper will greatly improve the precision of radiotherapy and shorten the radiotherapy time, which is of great application value.

Citing Articles

Artificial intelligence to predict outcomes of head and neck radiotherapy.

Bang C, Bernard G, Le W, Lalonde A, Kadoury S, Bahig H Clin Transl Radiat Oncol. 2023; 39:100590.

PMID: 36935854 PMC: 10014342. DOI: 10.1016/j.ctro.2023.100590.

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