RadCalc Automated Verification System | EN

Feasibility study in planned dose validation of the Monaco treatment planning system

The work evaluates the feasibility and clinical accuracy of the RadCalc Automated Validation System for secondary dose verification of Monaco treatment planning system (TPS) plans. The results demonstrate that RadCalc delivers reliable dose validation comparable to established phantom-based QA tools like ArcCHECK and MatriXX, supporting its use in routine clinical workflow.

Authors
Hou Dongmei (1), Zhang Qiuhang (2), Xu Jiankun (1)
1 Beijing Clinical Research Center for Geriatric Diseases, Beijing, China
2 Department of Radiotherapy, Xuanwu Hospital, Capital Medical University, Beijing, China

Source
Journal of Geriatric Medicine

Purpose / Objective
To explore whether the RadCalc Automated Validation System can be reliably used to validate planned dose distributions created in the Monaco TPS, offering a software-based alternative to traditional QA phantoms.

Materials / Methods
RadCalc machine modeling was commissioned and validated using 107 clinical cases (head, breast, lung, and abdominal tumors) treated from 2021–2023. Dose verification was performed using γ-passing rate metrics under 3%/2 mm and 3%/3 mm criteria. Results from RadCalc were compared against measurements from ArcCHECK and MatriXX phantoms for IMRT and VMAT plans.

Results
Across all test fields and clinical plans, γ-passing rates with RadCalc exceeded 96% (3%/2 mm) and 98% (3%/3 mm). While minor statistical differences were observed in some plan types (notably head and lung VMAT plans), overall agreement with ArcCHECK and MatriXX was high. The system passed all required commissioning tests and showed robust performance across different anatomical sites.

Conclusion
The RadCalc Automated Validation System provides dose verification accuracy on par with phantom-based QA tools and can be confidently used for clinical dose validation in Monaco TPS workflows.

Please note: The original paper is published in Chinese. This text was translated and edited with AI. AI can make mistakes. LAP accepts no liability for the content.