A Study on Enhancing Treatment Efficiency through Energy Layer Reduction In IMPT By Admm: A Retrospective Analysis of Lung Cancer Cases
Abstract
Purpose
In proton radiotherapy, layer switching time constitutes the most time-consuming procedure. By rationally reducing the total energy layers, treatment efficiency can be enhanced without compromising dose distribution or dosimetric evaluations. In China, lung cancer patients undergoing proton therapy often present with large tumors characterized by extended maximum diameters, which prolongs treatment duration. Consequently, reducing the number of energy layers holds practical significance in proton radiotherapy.
Methods
We developed an optimizer based on the ADMM using the MatRad open-source radiotherapy planning toolkits. A total of 24 lung cancer patients who previously underwent proton therapy at Shandong Cancer Hospital were enrolled (clinical TPS: Eclipse 16.1). Patients' DICOM files were exported and replanned on the MatRad. The plans in MatRad used identical beam directions and optimization objectives, with a uniform minimum MU threshold of 5. A plan set meeting clinical requirements was identified by evaluating the dose distribution and DVH of CTV and critical OARs.
Results
The ADMM algorithm demonstrates robust energy layer optimization capabilities. Acceptable plans can be found within 60 energy layers for both T2-T4 stage lung cancers. Compared to currently used TPS systems, the number of energy layers is reduced by over 40%. This significantly decreases the total beam delivery time. The number of energy layers threshold showed a positive correlation with Anterior-Posterior and Superior-Inferior dimensions (p<0.01). Additionally, during plan design, it was observed that central lung cancers and increasing beam numbers slightly raised the energy layer threshold.
Conclusion
The potential application of the ADMM algorithm in lung proton radiotherapy planning design was explored. Under ADMM algorithm optimization, a set of plans meeting clinical requirements can be simultaneously generated. This enables physicists to strike a balance between plan quality and treatment efficiency. If implemented in clinical practice, rational energy layer selection can maximize treatment efficiency while ensuring therapeutic efficacy.