ESTRO 2025 - Abstract Book

S2376

Interdisciplinary – Other

ESTRO 2025

2709

Digital Poster Research on Lattice Parameters in Lattice Radiotherapy(LRT) based on Artificial Intelligence Yi Peng, Ke Yuan, Hao Guo, Xianliang Wang Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Eletronic Science and Technology of China, Chengdu, China Purpose/Objective Lattice radiotherapy (LRT) is a novel spatially fractionated radiotherapy (SFRT) that has shown great therapeutic effects in clinical practice. However, it is difficult to determine the values of lattice parameters. We hypothesized that the values of lattice parameters are determined by the tumor volume. Material/Methods Medical records were retrospectively reviewed for clinical characteristics and radiotherapy planning parameters, and the relationship between the lattice parameters and tumor volume in the clinical records was analyzed. At the same time, artificial intelligence machine learning was used to automatically learn the values of lattice parameters under different tumor volumes based on the current clinical data. Results A total of 103 plans of 87 patients from June 2022 to November 2024 were counted, with tumor volumes ranging from 87cc to 6044cc, with a median volume of 870cc, and patient ages ranging from 4 to 95 years old, with a median age of 36 years old. Head tumors (20%) and chest tumors (36%) are the two most common SFRT targets. The lattice parameters in the radiotherapy plan were statistically analyzed as follows: 1) tumor volume was less than 100cc(V t < 100cc) , the lattice radius(R) took 2.0-3.0mm (41%), and the lattice spacing(D) took 20-30mm (37%); 2) V t < 600cc, R took 3.0-4.0mm (47%), D took 30-40mm (52%); 3) V t < 1400cc, R tooks 4.0-4.5mm (33%), D tooks 45-50mm (33%); 4) V t > 1400cc, R took 5mm (67%), and D was greater than 55mm (67%). An artificial intelligence neural network algorithm was used to learn 103 LRT plans, and lattices were generated in a 435cc tumor target area and a 928cc tumor target area, respectively. The radius of the lattice generated in the 435cc tumor target area was 3.5mm, and the spacing was 36mm. The radius of the lattice generated in the 928cc tumor target area was 4.5mm, and the spacing was 48mm. Conclusion Lattice parameters are crucial factors in lattice radiotherapy, which greatly affect the peak-to-valley dose ratio (PVDR) in the radiotherapy plan. However, there is no clear guidance document to analyze how to determine lattice parameters. This study reviewed and analyzed clinical records and radiotherapy data, and used artificial intelligence machine learning to determine the values of lattice parameters under different tumor volumes, providing a reference for the design of subsequent lattice radiotherapy plans.

Keywords: Lattice radiation therapy (LRT), SFRT

2729

Digital Poster Automating Treatment Plan Transfers: Implementation of a Monaco Scripting Routine João T Freitas, Pedro C Reis, João P Galhardas, Luís M Prudêncio, Sara F Santos, Tiago N Ribeiro Serviço Radioterapia, ULS Santa Maria, Lisbon, Portugal