Real-Time Adaptive Radiation Therapy with ABiL-Net for Lung Cancer for Personalized Dose Distribution Optimization

DOI

10.2991/978-94-6463-718-2_144How to use a DOI?

Keywords

ABiL-Net; Attention Mechanism; Bi-LSTM; Radiology; Lung Cancer; Predictive Modeling

Abstract

We propose a dynamic, prediction-oriented lung cancer radiation therapy with the ABiL-Net model by the combination of Attention Mechanism and Bi-LSTM. By simulating potential spatial and temporal tumor variations in real-time, the system allows for fine-tuning of radiation therapy parameters. ABiL-Net\Appends labels to precisely identify Regions of Interest (ROIs) pertinent in decadal investigations utilizing advanced imaging modalities for tumor segmentation, thereby elevating the diagnostic capacity required for therapy. Experimental results show that ABiL-Net is robust: The Dice Similarity Coefficient (0.98 ± 0.01) and Hausdorff Distance (2.50 ± 1.00 mm) of ABiL-Net outperforms traditional approaches significantly. This research emphasizes the value of predictive modeling for individualized and adaptive radiation therapy in the context of evolving tumor response.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Appawala Jayanthi
AU  - B. Eswara Reddy
PY  - 2025
DA  - 2025/05/23
TI  - Real-Time Adaptive Radiation Therapy with ABiL-Net for Lung Cancer for Personalized Dose Distribution Optimization
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1736
EP  - 1744
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_144
DO  - 10.2991/978-94-6463-718-2_144
ID  - Jayanthi2025
ER  -