To investigate the impact of breath-hold reproducibility on liver motion using a respiratory motion management device. Methods: Forty-four patients with hepatic tumors, treated with SBRT with breathhold, were randomly selected for this study. All patients underwent three consecutive computed tomography (CT) scans using active breath-hold coordinator (ABC) with three repeated single breath-hold during simulation. The three CT scans were labeled as ABC1-CT, ABC2-CT, and ABC3-CT. Displacements of centroids of the entire livers among the three ABC-CTs were measured as a surrogate for intrafractional motion. For each patient, two different treatment plans were prepared: (a) a clinical plan using a 5-mm expansion of an ITV that encompassed all three GTVs from each of the three ABC-CTs, and (b) a research plan using a 5-mm expansion of the GTV from only ABC1-CT to create PTV. The clinical plan acceptance criteria were that 95% of the PTV and 99% of the GTV received 100% of the prescription dose. Dosimetric endpoints were analyzed and compared for the two plans. Results: All shifts in the medial-lateral direction (range: −3.9 to 2.0 mm) were within 5 mm while 7% of shifts in the anterior-posterior direction (range: −10.5 to 16.7 mm) and 11% of shifts in the superior-inferior direction (range: −17.0 to 8.7 mm) exceeded 5 mm. Six patients (14%) had an intrafraction motion greater than 5 mm in any direction. For these six patients, if a plan was created based on a PTV from a single CT (ex. ABC1-CT), 5 of 12 GTVs captured from other ABC-CTs would fail to meet the clinical acceptance criteria due to poor breath-hold reproducibility. Conclusions: Non-negligible intrafractional motion occurs in patients with poor breath-hold reproducibility. To identify this subgroup of patients, acquiring three CTs with active breath-hold during simulation is a feasible practical method.
Volumetric‐modulated arc therapy (VMAT) plans may require more control points (or segments) than some of fixed‐beam IMRT plans that are created with a limited number of segments. Increasing number of control points in a VMAT plan for a given prescription dose could create a large portion of the total number of segments with small number monitor units (MUs) per segment. The purpose of this study is to investigate the impact of the small number MU/segment on the delivery accuracy of VMAT delivered with various dose rates. Ten patient datasets were planned for hippocampus sparing for whole brain irradiation. For each dataset, two VMAT plans were created with maximum dose rates of 600 MU/min (the maximum field size of 21×40 cm2) and 1000 MU/min (the maximum field size of 15×15 cm2) for a daily dose of 3 Gy. Without reoptimization, the daily dose of these plans was purposely reduced to 1.5 Gy and 1.0 Gy while keeping the same total dose. Using the two dose rates and three different daily doses, six VMAT plans for each dataset were delivered to a physical phantom to investigate how the changes of dose rate and daily doses impact on delivery accuracy. Using the gamma index, we directly compared the delivered planar dose profiles with the reduced daily doses (1.5 Gy and 1.0 Gy) to the delivered planar dose at 3 Gy daily dose, delivered at dose rate of 600 MU/min and 1000 MU/min, respectively. The average numbers of segments with MU/segment≤1 were 35±8, 87±6 for VMAT‐600 1.5 Gy, VMAT‐600 1 Gy plans, and 30±7 and 42±6 for VMAT‐1000 1.5 Gy and VMAT‐1000 1 Gy plans, respectively. When delivered at 600 MU/min dose rate, the average gamma index passing rates (1%/1 mm criteria) of comparing delivered 1.5 Gy VMAT planar dose profiles to 3.0 Gy VMAT delivered planar dose profiles was 98.28%±1.66%, and the average gamma index passing rate of comparing delivered 1.0 Gy VMAT planar dose to 3.0 Gy VMAT delivered planar dose was 83.75%±4.86%. If using 2%/2 mm and 3%/3 mm criteria, the gamma index passing rates were greater than 97% for both 1.5 Gy VMAT and 1.0 Gy VMAT delivered planar doses. At 1000 MU/min dose rate, the average gamma index passing rates were 96.59%±2.70% for 1.5 Gy VMAT planar dose profiles and 79.37%±9.96% for 1.0 Gy VMAT planar dose profiles when compared to the 3.0 Gy VMAT planar delivered dose profile. When using 2%/2 mm and 3%/3 mm criteria, the gamma index passing rates were greater than 93% for both 1.5 Gy VMAT and 1.0 Gy VMAT planar delivered dose. Under a stricter gamma index criterion (1%/1 mm), significant differences in delivered planar dose profiles at different daily doses were detected, indicating that the known communication delay between the MU console and MLC console may affect VMAT delivery accuracy.PACS number(s): 87.56.bd, 87.55.‐x
Purpose/objectives To report our experience of combining three approaches of an automatic plan integrity check (APIC), a standard plan documentation, and checklist methods to minimize errors in the treatment planning process. Materials/methods We developed APIC program and standardized plan documentation via scripting in the treatment planning system, with an enforce function of APIC usage. We used a checklist method to check for communication errors in patient charts (referred to as chart errors). Any errors in the plans and charts (referred to as the planning errors) discovered during the initial chart check by the therapists were reported to our institutional Workflow Enhancement (WE) system. Clinical Implementation of these three methods is a progressive process while the APIC was the major progress among the three methods. Thus, we chose to compared the total number of planning errors before (including data from 2013 to 2014) and after (including data from 2015 to 2018) APIC implementation. We assigned the severity of these errors into five categories: serious (S), near miss with safety net (NM), clinical interruption (CLI), minor impediment (MI), and bookkeeping (BK). The Mann–Whitney U test was used for statistical analysis. Results A total of 253 planning error forms, containing 272 errors, were submitted during the study period, representing an error rate of 3.8%, 3.1%, 2.1%, 0.8%, 1.9% and 1.3% of total number of plans in these years respectively. A marked reduction of planning error rate in the S and NM categories was statistically significant (P < 0.01): from 0.6% before APIC to 0.1% after APIC. The error rate for all categories was also significantly reduced (P < 0.01), from 3.4% before APIC and 1.5% per plan after APIC. Conclusion With three combined methods, we reduced both the number and the severity of errors significantly in the process of treatment planning.
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