# Signal-aware deep learning–based respiratory motion prediction for lung tumor management

## TL;DR

This paper introduces a deep learning model to predict lung tumor motion during radiotherapy, aiming to improve treatment accuracy and reduce side effects.

## Contribution

A novel hybrid deep learning model is proposed to jointly model spatial and temporal respiratory motion characteristics for tumor motion prediction.

## Key findings

- The model achieves 98.37% motion-range classification performance with low prediction error.
- It effectively reconstructs physiologically coherent respiratory cycles and captures abnormal breathing patterns.
- The approach maintains stable performance across long and complex respiratory signals.

## Abstract

Respiratory motion management in radiotherapy for lung cancer patients remains a significant challenge, as it directly affects accurate tumor targeting. Furthermore, unaccounted tumor motion during treatment planning and delivery can lead to imaging artifacts and biased dose distributions, which compromises the accuracy of image-guided radiotherapy. This issue places clinicians in a dilemma between expanding treatment margins, which increases radiation exposure to healthy tissue or risking reduced targeting precision.

In this work, a hybrid deep learning model composed of dilated convolutional layers, bidirectional long-short term memory layers, and a generative autoencoder module is proposed to jointly model the spatial and temporal characteristics of respiratory motion, while enabling reconstruction of the physiologically coherent respiratory signals. Each architectural component learns complementary motion-related patterns from respiratory signals to support tumor motion prediction. The model performs motion-range classification, captures abnormal breathing patterns across spatial and temporal domains, reconstructs physiologically coherent respiratory cycles, and predicts tumor motion within an algorithmic validation framework.

Experimental evaluation demonstrates high motion-range classification performance of 98.37%, including low root-mean square error in motion prediction, while maintaining stable performance across long and complex respiratory signals over multiple breathing cycles.

This study focuses on algorithmic feasibility and establishes a computational foundation for future clinically calibrated and dosimetrically validated models. The findings indicate that the proposed approach can support future motion-aware radiotherapy planning strategies by improving motion characterization at the algorithmic level.