Signal-aware deep learningbased respiratory motion prediction for lung tumor management
artificial intelligence; lung cancer; radiotherapy; respiratory motion; treatment planning
Introduction: 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. Methods: 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. Results: 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. Discussion: 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. Copyright 2026 Das, J. and Medhi.
Das, Kaushik Pratim; Chandra, J.; Medhi, Partha Pratim
Frontiers in Oncology;Volume;16;Issue;;Article No.;1735140;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3389/fonc.2026.1735140 https://www.scopus.com/pages/publications/105031602837?origin=resultslist
Das K.P., Department of Computer Science, School of Sciences, Christ University, Karnataka, Bangalore, India; Chandra J., Department of Computer Science, School of Sciences, Christ University, Karnataka, Bangalore, India; Medhi P.P., Department of Radiation Oncology, All India Institute of Medical Sciences, Assam, Guwahati, India