Automated robust SBPT planning using EUD-based prediction of SBRT plan for patients with lung cancer. (September 2021)

• Record Type: Journal Article
• Authors: Wei, Long; Wang, Wei; Dai, Zhitao; Li, Yang; Shang, Haijiao
• Is Part Of: Computer methods and programs in biomedicine. Volume 209(2021)
• Publication Date: 2021-09
• Subjects: Lung cancer/SBRT treatment -- Equivalent uniform dose (EUD) -- Stereotactic body proton therapy -- Automated robust planning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106338
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: In our study, we developed an auto SBPT planning methods based on patient data in robust SBRT plan, which came from our previously studies. Our novelty lies in the developing program for converting the manual trial-and-error process of the planer into an automation script that can simulate all the steps of manual planning and ultimately achieve one-click SBPT plan. Furthermore, our methods take range uncertainties into account and can generate a robust SBPT(RSBPT) plan against CT density and patient setup errors. The goal of this study was to investigate whether the auto robust SBPT methods was able to generate a robustly proton plan of equal or better quality compared with manual methods. If so, the approach could be used within an automated workflow to generate SBPT plan to reduce planning time. Abstract: Purpose: To evaluate the quality of robust stereotactic body proton therapy (RSBPT) plans generated by one-clicking scripting method for patients with lung cancer. Materials and methods: Retrospective analysis was performed on fifty lung cancer patients whose plan with robustly stereotactic body radiation therapy (SBRT). Thirty out of fifty patients were used for training to build a regression model, based on robust SBRT reference doses, to predict EUD values of ROIs for robust SBPT planning. Thereafter, robust SBPT plans with both automated EUD-Based mimicking (Automated Robust Proton ARP) and manual (Manual Robust Proton MRP) methods were evaluated in the remaining 20 patients. Plans were compared in terms of dosimetric parameters and planning time. Results: A statistically significantly improvement in target dose fall off was observed for ARP plans compare to MRP plans (Dose fall off: 135 for MRP and 88 for ARP, p < 0.01), while no differences in target coverage and conformity. A statistically significantly reduce in normal lung tissue were observed for ARP plans compare to MRP plans (Lung [Dmean cGy (RBE)]: MRP: 478 vs. ARP: 351, p < 0.01; Lung [V5Gy (RBE) (%)]: MRP: 16.1 vs. ARP: 12.1, p < 0.01; Lung [V20Gy (RBE) (%)]: MRP: 8.5 vs. ARP: 6.8, p < 0.01). Planning time was reduced for ARP plans compare to MRP plans (optimization time: 12 min for MRP vs. 8 min for ARP; total plan time: 23 min for MRP vs. 18 min for ARP). Conclusion: The automated robust SBPT plans using EUD-Based mimicking of SBRT reference dose improve target dose fall off, reduced the radiation doses to the lungs, reduce planning time, which might be beneficial for patient with lung cancer in clinical.