Medical physics · 2026
Development and validation of an automated, accurate in-house treatment planning system for pencil-beam scanning carbon ion radiotherapy.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Lee Sac, Yun Yongdo, Han Min Cheol, Kim Changhwan, Hong Chae-Seon, Lee Ho, Kim Dong Wook, Jung Dong Min, Kim Jin Sung, Kim Hojin
Аннотация
BACKGROUND: Carbon ion radiotherapy (CIRT) offers superior physical and biological advantages over photon and proton radiotherapy (PRT). However, due to a need for accurate modeling of the relative biological effectiveness (RBE), currently available treatment planning systems (TPS) for CIRT remain limited. This has constrained clinical and research applications by compelling reliance on a narrow set of systems and restricting flexibility for broader application.
PURPOSE: This study aimed to develop and validate a new efficient and accurate CIRT TPS for pencil-beam scanning (PBS)-based CIRT, tailored for the Heavy-Ion Therapy Center (HITC) at Yonsei Cancer Center (YCC), incorporating modified microdosimetric kinetic model (mMKM)-based RBE-weighted dose calculation and spot weight optimization.
METHODS: The proposed TPS was designed to automate the workflow from CT image import to CIRT plan optimization. Key refinements included 3D Siddon's ray tracing for spot trajectory tracking, air-gap modeling for beam profile adaptation, and 3D stopping power ratio (SPR) calculation. Gaussian beam modeling was performed to calibrate in-air measurements and Monte Carlo (MC)-driven 3D dose kernels in water. These procedures produced dose influence matrices (DIMs) and MC-estimated z 1 D ∗ $z_{1D}^*$ matrices required for spot weight optimization with mMKM-based RBE-weighted dose calculation, which was further accelerated by Adam optimizer. Validation was performed by generating plans for a water phantom (10 Gy-RBE), a lung case (15 Gy-RBE), and a prostate case (4.3 Gy-RBE), using single-field and multi-field optimizations (MFO). The resulting plans were recalculated in RayStation (v.2025) and TOPAS MC, and compared using dose-volume histogram (DVH) and gamma passing rate (GPR) at 2%/2 mm.
RESULTS: The proposed TPS successfully automated the CIRT plan optimization within approximately 1 min for ∼1000 spots, which produced uniform RBE-weighted dose coverage in virtual water phantom, the lung patient, and the prostate patient cases. When the spot weights optimized by the proposed framework were recalculated using a commercial TPS and TOPAS MC, the resulting dose distributions closely matched those of the proposed TPS. Quantitatively, GPRs exceeding 98% were achieved for both physical and RBE-weighted dose at the 2%/2 mm criterion, except for the prostate single-field case, which yielded a GPR of 97.37% for the physical dose, relative to TOPAS MC, and a GPR of 96.28% for the RBE-weighted dose, relative to RayStation. These findings confirmed strong agreement between the proposed TPS, MC simulations, and a commercial TPS.
CONCLUSION: A new, fully automated in-house CIRT TPS was developed and validated, demonstrating high dosimetric accuracy and computational efficiency, even comparable to a commercial TPS and MC simulations.
PURPOSE: This study aimed to develop and validate a new efficient and accurate CIRT TPS for pencil-beam scanning (PBS)-based CIRT, tailored for the Heavy-Ion Therapy Center (HITC) at Yonsei Cancer Center (YCC), incorporating modified microdosimetric kinetic model (mMKM)-based RBE-weighted dose calculation and spot weight optimization.
METHODS: The proposed TPS was designed to automate the workflow from CT image import to CIRT plan optimization. Key refinements included 3D Siddon's ray tracing for spot trajectory tracking, air-gap modeling for beam profile adaptation, and 3D stopping power ratio (SPR) calculation. Gaussian beam modeling was performed to calibrate in-air measurements and Monte Carlo (MC)-driven 3D dose kernels in water. These procedures produced dose influence matrices (DIMs) and MC-estimated z 1 D ∗ $z_{1D}^*$ matrices required for spot weight optimization with mMKM-based RBE-weighted dose calculation, which was further accelerated by Adam optimizer. Validation was performed by generating plans for a water phantom (10 Gy-RBE), a lung case (15 Gy-RBE), and a prostate case (4.3 Gy-RBE), using single-field and multi-field optimizations (MFO). The resulting plans were recalculated in RayStation (v.2025) and TOPAS MC, and compared using dose-volume histogram (DVH) and gamma passing rate (GPR) at 2%/2 mm.
RESULTS: The proposed TPS successfully automated the CIRT plan optimization within approximately 1 min for ∼1000 spots, which produced uniform RBE-weighted dose coverage in virtual water phantom, the lung patient, and the prostate patient cases. When the spot weights optimized by the proposed framework were recalculated using a commercial TPS and TOPAS MC, the resulting dose distributions closely matched those of the proposed TPS. Quantitatively, GPRs exceeding 98% were achieved for both physical and RBE-weighted dose at the 2%/2 mm criterion, except for the prostate single-field case, which yielded a GPR of 97.37% for the physical dose, relative to TOPAS MC, and a GPR of 96.28% for the RBE-weighted dose, relative to RayStation. These findings confirmed strong agreement between the proposed TPS, MC simulations, and a commercial TPS.
CONCLUSION: A new, fully automated in-house CIRT TPS was developed and validated, demonstrating high dosimetric accuracy and computational efficiency, even comparable to a commercial TPS and MC simulations.