Citations

Abstract Purpose: Head and neck (HN) radiotherapy contour quality directly impacts local control (LC) and survival; however, few departments peer review (PR) contours prior to radiotherapy planning (RTP). This study reports outcomes of a single institution’s formal HN contour PR process. Methods: A formal HN contour PR process was implemented. Prior to RTP, HN radiation oncologists reviewed contours, provided feedback and assigned contour grades as follows: R0 (no change), R1 (minor revision, not high risk) or R2 (major revision; high risk). The PR task was completed and the contour grade was recorded. Cochran-Armitage trend test was performed. Results: Pilot PR process was performed over a 7-month period for 88 patients, followed by a maintenance phase. Contours were graded as follows: R0 ( N = 51), R1 ( N = 20) and R2 ( N = 17). Over time, the number of R2 revisions decreased ( p = 0.0001); month 1 ( N = 7), month 2 ( N = 3), month 3 ( N = 5), month 4 ( N = 2) and months 5–7 ( N = 0). Conversely, the number of R0 revisions improved over time ( p = 0.0203); month 1 ( N = 5), months 2–3 ( N = 9), month 4 ( N = 5), month 5 ( N = 8), month 6 ( N = 12) and month 7 ( N = 3). Each radiation oncologist demonstrated reduction in R2 revisions during the pilot. During maintenance, all 3 radiation oncologists demonstrated low rates of R2 revisions of less than 3 cases per year. Conclusion: Incorporation of HN contour PR into routine clinical workflow is feasible. The collective experience of multiple high-volume HN radiation oncologists led to improved contour quality in the pilot for each radiation oncologist and continued to ensure high quality in the maintenance phase.

Background/Objectives: Adjuvant radiation for gynecologic malignancies often exposes organs at risk (OARs), such as the bone marrow, bowel, rectum, and bladder, to radiation, leading to toxicities that impact treatment tolerance and patient quality of life. Scanning proton beam therapy, particularly with Individual Field Simultaneous Optimization (IFSO), may offer dosimetric and biological advantages over volumetric modulated arc therapy (VMAT). This study evaluates the clinical impact of IFSO-based proton planning in post-operative gynecologic cancer patients. Materials and Methods: Fourteen patients receiving adjuvant proton therapy to 45 Gy in 25 fractions were retrospectively analyzed. Comparison VMAT plans were generated on the same datasets. Dose–volume metrics for key OARs and normal tissue complication probabilities (NTCPs) were compared using paired statistical tests. Robustness evaluations accounted for setup and range uncertainties. Results: Proton plans significantly reduced dose to bone marrow (V10Gy: 58% vs. 86%, p < 0.00001; V20Gy: 47% vs. 58%, p < 0.00001), small bowel (V20Gy: 21% vs. 56%, p < 0.00001), and femoral heads (left femoral head mean: 11Gy vs. 13Gy, p = 0.032; right femoral head mean: 11Gy vs. 13Gy, p = 0.022). NTCP modeling predicted significantly lower rates of bowel urgency (9.4% vs. 3.3%, p < 0.001) and hematologic toxicity (10.2% vs. 4.9%, p < 0.001) with proton therapy. Plans remained robust across uncertainty scenarios. Conclusions: IFSO-based scanning proton therapy provides clinically meaningful sparing of bone marrow and bowel, with the potential to reduce hematologic and gastrointestinal toxicities. These findings support its use in patients receiving adjuvant pelvic radiotherapy, particularly those undergoing extended field treatment or chemotherapy.

AbstractBackgroundBolus materials have been used for decades in radiotherapy. Most frequently, these materials are utilized to bring dose closer to the skin surface to cover superficial targets optimally. While cavity filling, such as nasal cavities, is desirable, traditional commercial bolus is lacking, requiring other solutions. Recently, investigators have worked on utilizing 3D printing technology, including commercially available solutions, which can overcome some challenges with traditional bolus.PurposeTo utilize failure modes and effects analysis (FMEA) to successfully implement a comprehensive 3D printed bolus solution to replace commercial bolus in our clinic using a series of open‐source (or free) software products.Methods3D printed molds for bespoke bolus were created by exporting the DICOM structures of the bolus designed in the treatment planning system and manipulated to create a multipart mold for 3D printing. A silicone (Ecoflex 00–30) mixture is poured into the mold and cured to form the bolus. Molds for sheet bolus of five thicknesses were also created. A comprehensive FMEA was performed to guide workflow adjustments and QA steps.ResultsThe process map identified 39 and 30 distinct steps for the bespoke and flat sheet bolus workflows, respectively. The corresponding FMEA highlighted 119 and 86 failure modes, with 69 shared between the processes. Misunderstanding of plan intent was a potential cause for most of the highest‐scoring failure modes, indicating that physics and dosimetry involvement early in the process is paramount.ConclusionFMEA informed the design and implementation of QA steps to guarantee a safe and high‐quality comprehensive implementation of silicone bolus from 3D printed molds. This approach allows for greater adaptability not afforded by traditional bolus, as well as potential dissemination to other clinics due to the open‐source nature of the workflow.

AbstractPurposeTo present a modified calibration method to reduce signal drift due to table sagging in Respiratory Gating for Scanner (RGSC) systems with a wall‐mounted camera.Materials and MethodsApproximately 70 kg of solid water phantoms were evenly distributed on the CT couch, mimicking the patient's weight. New calibration measurements were performed at 9 points at the combination of three lateral positions, the CT isocenter and ±10 cm laterally from the isocenter, and three longitudinal locations, the CT isocenter and ±30 cm or ±40 cm from the isocenter. The new calibration was tested in two hospitals.ResultsImplementing the new weighed calibration method at the extended distance yielded improved results during the DIBH scan, reducing the drift to within 1 from 3 mm. The extended calibration positions exhibited similarly reduced drift in both hospitals, reinforcing the method's robustness and its potential applicability across all centers.ConclusionThis proposed solution aims to minimize the systematic error in radiation delivery for patients undergoing motion management with wall‐mounted camera RGSC systems, especially in conjunction with a bariatric CT couchtop.

AbstractImplementing tighter intensity modulated radiation therapy (IMRT) quality assurance (QA) tolerances initially resulted in high numbers of marginal or failing QA results and motivated a number of improvements to our calculational processes. This work details those improvements and their effect on results. One hundred eighty IMRT plans analyzed previously were collected and new gamma criteria were applied and compared to the original results. The results were used to obtain an estimate for the number of plans that would require additional dose volume histogram (DVH)‐based analysis and therefore predicted workload increase. For 2 months and 133 plans, the established criteria were continued while the new criteria were applied and tracked in parallel. Because the number of marginal or failing plans far exceeded the predicted levels, a number of calculational elements were investigated: IMRT modeling parameters, calculation grid size, and couch top modeling. After improvements to these elements, the new criteria were clinically implemented and the frequency of passing, questionable, and failing plans measured for the subsequent 15 months and 674 plans. The retrospective analysis of selected IMRT QA results demonstrated that 75% of plans should pass, while 19% of IMRT QA plans would need DVH‐based analysis and an additional 6% would fail. However, after applying the tighter criteria for 2 months, the distribution of plans was significantly different from prediction with questionable or failing plans reaching 47%. After investigating and improving several elements of the IMRT calculation processes, the frequency of questionable plans was reduced to 11% and that of failing plans to less than 1%. Tighter IMRT QA tolerances revealed the need to improve several elements of our plan calculations. As a consequence, the accuracy of our plans have improved, and the frequency of finding marginal or failing IMRT QA results, remains within our practical ability to respond.

The purpose of this study is to apply the principles of statistical process control (SPC) in the context of patient specific intensity‐modulated radiation therapy (IMRT) QA to set clinic‐specific action limits and evaluate the impact of changes to the multileaf collimator (MLC) calibrations on IMRT QA results. Ten months of IMRT QA data with 247 patient QAs collected on three beam‐matched linacs were retrospectively analyzed with a focus on the gamma pass rate (GPR) and the average ratio between the measured and planned doses. Initial control charts and action limits were calculated. Based on this data, changes were made to the leaf gap parameter for the MLCs to improve the consistency between linacs. This leaf gap parameter is tested monthly using a MLC sweep test. A follow‐up dataset with 424 unique QAs were used to evaluate the impact of the leaf gap parameter change. The initial data average GPR was 98.6% with an SPC action limit of 93.7%. The average ratio of doses was 1.003, with an upper action limit of 1.017 and a lower action limit of 0.989. The sweep test results for the linacs were , and from nominal. After the adjustment of the leaf gap parameter, all sweep test results were within 0.4% of nominal. Subsequently, the average GPR was 99.4% with an SPC action limit of 97.3%. The average ratio of doses was 0.997 with an upper action limit of 1.011 and a lower action limit of 0.981. Applying the principles of SPC to IMRT QA allowed small differences between closely matched linacs to be identified and reduced. Ongoing analysis will monitor the process and be used to refine the clinical action limits for IMRT QA.PACS number: 87.55.Qr