| Abstract: |
Objective. Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is a complex, non-convex problem traditionally addressed with heuristic methods. Approach. This work demonstrates the potential improvement of the proposed BOO, providing a mathematically grounded benchmark that can guide and validate heuristic BOO methods, while also offering a computationally efficient workflow suitable for clinical application. A novel framework integrating second-order cone programming (SOCP) relaxation, sparse mixed integer programming (SMIP), and deep inverse optimization is proposed. Nonconvex dose-volume constraints were managed via SOCP relaxation, ensuring convexity while maintaining sparsity. BOO was formulated as an SMIP problem with binary beam selection, solved using an augmented Lagrange method. To accelerate optimization, a neural network approximated optimal solution, improving computational efficiency eightfold. A retrospective analysis of 12 locally advanced non-small cell lung cancer (NSCLC) patients (60 Gy prescription) compared automated BOO-selected beam angles with expert selections, evaluating dosimetric metrics such as planning target volume (PTV) maximum dose, D98%, lung V20, and mean heart and esophagus dose. Main results. In 12 retrospective study, the automated BOO demonstrated superior dose conformity and sparing of critical structures. Specifically, the BOO plans achieved comparable PTV coverage (maximum: 61.7 ± 1.4 Gy vs. 62.1 ± 1.5 Gy, D98%: 59.5 ± 0.7 Gy vs. 59.5 ± 0.6 Gy, D2%: 61.2 ± 1.3 Gy vs. 61.4 ± 1.4 Gy with p-values >0.5) but demonstrated improved sparing for lungs (V20: 9.8 ± 2.2% vs. 11.5 ± 2.3%, p-value: 0.01), heart (mean: 3.3 ± 0.6 Gy vs. 4.3 ± 0.5 Gy, p-value: 0.04), esophagus (mean: 0.5 ± 1.3 Gy vs. 1.8 ± 1.5 Gy, p-value: 0.02), and spinal cord (max: 7.2 ± 3.4 Gy vs. 9.0 ± 3.2 Gy, p-value < 0.01) compared to human-selected plans. Significance. This approach highlighted the potential of BOO to enhance treatment efficacy by optimizing beam angles more effectively than manual selection. This framework establishes a benchmark for BOO in IMRT, enhancing heuristic methods through a hybrid framework that combines mathematical optimization with targeted heuristics to improve solution quality and computational efficiency. The integration of SMIP and deep inverse optimization significantly improves computational efficiency and plan quality. © 2025 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved. |
