Dosimetric potential of knowledge-based planning model trained with HyperArc plans for brain metastases.

OBJECTIVE: Dosimetric potential of knowledge-based RapidPlan planning model trained with HyperArc plans (Model-HA) for brain metastases has not been reported. We developed a Model-HA and compared its performance with that of clinical volumetric modulated arc therapy (VMAT) plans. METHODS: From 67 clinical stereotactic radiosurgery (SRS) HyperArc plans for brain metastases, 47 plans were used to build and train a Model-HA. The other 20 clinical HyperArc plans were recalculated in RapidPlan system with Model-HA. The model performance was validated with the 20 plans by comparing dosimetric parameters for normal brain tissue between clinical plans and model-generated plans. The 20 clinical conventional VMAT-based SRS or stereotactic radiotherapy plans (CL-VMAT) were reoptimized with Model-HA (RP) and HyperArc system (HA), respectively. The dosimetric parameters were compared among three plans (CL-VMAT vs. RP vs. HA) in terms of planning target volume (PTV), normal brain excluding PTVs (Brain - PTV), brainstem, chiasm, and both optic nerves. RESULTS: In model validation, the optimization performance of Model-HA was comparable to that of HyperArc system. In comparison to CL-VMAT, there were no significant differences among three plans with respect to PTV coverage (p > 0.17) and maximum dose for brainstem, chiasm, and optic nerves (p > 0.40). RP provided significantly lower V20 Gy , V12 Gy , and V4 Gy for Brain - PTV than CL-VMAT (p < 0.01). CONCLUSION: The Model-HA has the potential to significantly reduce the normal brain dose of the original VMAT plans for brain metastases.

Effects of Mechanical Performance on Deliverability and Dose Distribution by Comparing Multi Institutions' Knowledge-based Models for Prostate Cancer in Volumetric Modulated Arc Therapy.

BACKGROUND/AIM: The aim of this study was to evaluate the mechanical performance and the effect on dose distribution and deliverability of volumetric modulated arc therapy (VMAT) plans for prostate cancer created with the commercial knowledge-based planning (KBP) system (RapidPlan™). MATERIALS AND METHODS: Three institutions, A, B, and C were enrolled in this study. Each institution established and trained a KBP model with their own cases. CT data and structures for 45 patients at institution B were utilized to validate the dose-volume parameters (D2(%), D95(%), and D98(%) for target, and V50(%), V75(%), and V90(%) for rectum and bladder), and the following mechanical performance parameters and gamma passing rates of each KBP model: leaf sequence variability (LSV), aperture area variability (AAV), total monitor unit (MU), modulation complexity score for VMAT (MCSv), MU/control point (CP), aperture area (AA)/CP, and MU×AA/CP. RESULTS: Significant differences (p<0.01) in dosimetric parameters such as D2 and D98 for target and V50, V75, and V90 for bladder were observed among the three institutions. The means and standard deviations of MCSv were 0.31±0.03, 0.29±0.02, and 0.32±0.03, and the angles of maximum and minimum MU×AA/CP were 269° and 13°, 269° and 13°, and 273° and 153° at institutions A, B, and C, respectively. The mean gamma passing rate (1%/1 mm.) was >95% for all cases in each institution. Dose distribution and mechanical performance significantly differed between the three models. CONCLUSION: Each KBP model had different dose distributions and mechanical performance but could create an acceptable plan for deliverability regardless of mechanical performance.

Novel strategy with the automatic non-coplanar volumetric-modulated arc therapy for angiosarcoma of the scalp.

BACKGROUND: Total scalp irradiation presents technical and dosimetric challenges. While reports suggest that HyperArc, a new stereotactic radiosurgery planning technique applied to non-coplanar volumetric-modulated arc therapy (VMAT) technique, is associated with high conformity and rapid dose fall-off, the performance of HyperArc for total scalp irradiation has not been explored. The current study aimed to compare the dosimetric performance of HyperArc plans with those of non-coplanar VMAT plans in angiosarcoma of the scalp. METHODS: Ten patients with angiosarcoma of the scalp were included in this study. The performance of three different plans administered using TrueBeam Edge were compared: non-coplanar VMAT using flattening filter (FF) beams (VMAT-FF), HyperArc using FF beams (HyperArc-FF), and HyperArc using flattening filter free (FFF) beams (HyperArc-FFF). The dose distribution, dosimetric parameters, and dosimetric accuracy for each of these plans were evaluated. RESULTS: The three plans showed no statistically significant differences in target volume coverage, conformity, and homogeneity. The HyperArc-FF and HyperArc-FFF plans provided significantly lower mean brain doses (12.63 ± 3.31 Gy and 12.71 ± 3.40 Gy) than did the VMAT-FF plans (17.11 ± 5.25 Gy). There were almost no differences in sparing the organs at risk between the HyperArc-FF and HyperArc-FFF plans. The HyperArc-FF and HyperArc-FFF plans provided a shorter beam-on time than did the VMAT-FF plan. The 3%/2 mm gamma test pass rates were above 95% for all three plans. CONCLUSIONS: Our results suggest that the HyperArc plan can be potentially used for radiation therapy of target regions with large and complicated shapes, such as the scalp, and that there are no advantages of using FFF beams.

Knowledge-based planning for oesophageal cancers using a model trained with plans from a different treatment planning system.

Background: This study aimed to evaluate knowledge-based volume modulated arc therapy (VMAT) plans for oesophageal cancers using a model trained with plans optimised with a different treatment planning system (TPS) and to compare lung dose sparing in two TPSs, Eclipse and RayStation.Materials and methods: A total of 64 patients with stage I-III oesophageal cancers were treated using hybrid VMAT (H-VMAT) plans optimised using RayStation. Among them, 40 plans were used for training the model for knowledge-based planning (KBP) in RapidPlan. The remaining 24 plans were recalculated using RapidPlan to validate the KBP model. H-VMAT plans calculated using RapidPlan were compared with H-VMAT plans optimised using RayStation with respect to planning target volume doses, lung doses, and modulation complexity.Results: In the lung, there were significant differences between the volume ratios receiving doses in excess of 5, 10, and 20 Gy (V5, V10, and V20). The V5 for the lung with H-VMAT plans optimised using RapidPlan was significantly higher than that of H-VMAT plans optimised using RayStation (p < .01), with a mean difference of 10%. Compared to H-VMAT plans optimised using RayStation, the V10 and V20 for the lung were significantly lower with H-VMAT plans optimised using RapidPlan (p = .04 and p = .02), with differences exceeding 1.0%. In terms of modulation complexity, the change in beam output at each control point was more constant with H-VMAT plans optimised using RapidPlan than with H-VMAT plans optimised using RayStation. The range of the change with H-VMAT plans optimised using RapidPlan was one third that of H-VMAT plans optimised using RayStation.Conclusion: Two optimisers in Eclipse and RayStation had different dosimetric performance in lung sparing and modulation complexity. RapidPlan could not improve low lung doses, however, it provided an appreciate intermediated doses compared to plans optimised with RayStation.