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Purpose: Head and neck (HN) radiation (RT) treatment planning is complex and resource intensive. Deviations and inconsistent plan quality significantly affect clinical outcomes. We sought to develop a novel automated virtual integrative (AVI) knowledge-based planning application to reduce planning time, increase consistency, and improve baseline quality. Methods and Materials: An in-house write-enabled script was developed from a library of 668 previously treated HN RT plans. Prospective hazard analysis was performed, and mitigation strategies were implemented before clinical release. The AVI-planner software was retrospectively validated in a cohort of 52 recent HN cases. A physician panel evaluated planning limitations during initial deployment, and feedback was enacted via software refinements. A final second set of plans was generated and evaluated. Kolmogorov-Smirnov test in addition to generalized evaluation metric and weighted experience score were used to compare normal tissue sparing between final AVI planner versus respective clinically treated and historically accepted plans. A t test was used to compare the interactive time, complexity, and monitor units for AVI planner versus manual optimization. Results: Initially, 86% of plans were acceptable to treat, with 10% minor and 4% major revisions or rejection recommended. Variability was noted in plan quality among HN subsites, with high initial quality for oropharynx and oral cavity plans. Plans needing revisions were comprised of sinonasal, nasopharynx, P-16 negative squamous cell carcinoma unknown primary, or cutaneous primary sites. Normal tissue sparing varied within subsites, but AVI planner significantly lowered mean larynx dose (median, 18.5 vs 19.7 Gy; P < .01) compared with clinical plans. AVI planner significantly reduced interactive optimization time (mean, 2 vs 85 minutes; P < .01). Conclusions: AVI planner reliably generated clinically acceptable RT plans for oral cavity, salivary, oropharynx, larynx, and hypopharynx cancers. Physician-driven iterative learning processes resulted in favorable evolution in HN RT plan quality with significant time savings and improved consistency using AVI planner.

Purpose: Recent clinical trials suggest hypofractionated treatment regimens are appropriate for treatment of many cancers. It is important to understand and document hypofractionation adoption because of its implications for treatment center patient volumes. There is no recent U.S. study of trends in hypofractionation adoption that includes comparisons of multiple disease sites and data since the onset of COVID-19. In this context, this study describes trends in treatment fractionation at a single academic center from 2010 to 2020. Methods and Materials: From an institutional database, records were extracted for treatment of 4 disease site categories: all cancers, breast cancer, prostate cancer, and bone metastases. For each disease site, the mean number of fractions per treatment course was reported for each year of the study period. To explore whether the COVID-19 pandemic was associated with increased hypofractionation adoption, piecewise linear regression models were used to estimate a changepoint in the time trend of mean monthly number of fractions per treatment course and to evaluate whether this changepoint coincided with pandemic onset. Results: The data set included 22,865 courses of radiation treatment and 375,446 treatment fractions. The mean number of fractions per treatment course for all cancers declined from 17.5 in 2010 to 13.6 in 2020. There was increased adoption of hypofractionation at this institution for all cancers and specifically for both breast and prostate cancer. For bone metastases, hypofractionation had largely been adopted before the study period. For most disease sites, adoption of hypofractionated treatment courses occurred before pandemic onset. Bone metastases was the only disease site where a pandemic-driven increase in hypofractionation adoption could not be ruled out. Conclusions: This study reveals increasing use of hypofractionated regimens for a variety of cancers throughout the study period, which largely occurred before the onset of the COVID-19 pandemic at this institution.

Purpose: Due to a gap in published guidance, we describe our robust cycle of in-house clinical software development and implementation, which has been used for years to facilitate the safe treatment of all patients in our clinics. Methods and Materials: Our software development and implementation cycle requires clarity in communication, clearly defined roles, thorough commissioning, and regular feedback. Cycle phases include design requirements and use cases, development, physics evaluation testing, clinical evaluation testing, and full clinical release. Software requirements, release notes, test suites, and a commissioning report are created and independently reviewed before clinical use. Software deemed to be high-risk, such as those that are writable to a database, incorporate the use of a formal, team-based hazard analysis. Incident learning is used to both guide initial development and improvements as well as to monitor the safe use of the software. Results: Our standard process builds in transparency and establishes high expectations in the development and use of custom software to support patient care. Since moving to a commercial planning system platform in 2013, we have applied our team-based software release process to 16 programs related to scripting in the treatment planning system for the clinic. Conclusions: The principles and methodology described here can be implemented in a range of practice settings regardless of whether or not dedicated resources are available for software development. In addition to teamwork with defined roles, documentation, and use of incident learning, we strongly recommend having a written policy on the process, using phased testing, and incorporating independent oversight and approval before use for patient care. This rigorous process ensures continuous monitoring for and mitigatation of any high risk hazards.

Purpose: To develop statistical dose-volume histogram (DVH)–based metrics and a visualization method to quantify the comparison of treatment plans with historical experience and among different institutions. Methods and materials: The descriptive statistical summary (ie, median, first and third quartiles, and 95% confidence intervals) of volume-normalized DVH curve sets of past experiences was visualized through the creation of statistical DVH plots. Detailed distribution parameters were calculated and stored in JavaScript Object Notation files to facilitate management, including transfer and potential multi-institutional comparisons. In the treatment plan evaluation, structure DVH curves were scored against computed statistical DVHs and weighted experience scores (WESs). Individual, clinically used, DVH-based metrics were integrated into a generalized evaluation metric (GEM) as a priority-weighted sum of normalized incomplete gamma functions. Historical treatment plans for 351 patients with head and neck cancer, 104 with prostate cancer who were treated with conventional fractionation, and 94 with liver cancer who were treated with stereotactic body radiation therapy were analyzed to demonstrate the usage of statistical DVH, WES, and GEM in a plan evaluation. A shareable dashboard plugin was created to display statistical DVHs and integrate GEM and WES scores into a clinical plan evaluation within the treatment planning system. Benchmarking with normal tissue complication probability scores was carried out to compare the behavior of GEM and WES scores. Results: DVH curves from historical treatment plans were characterized and presented, with difficult-to-spare structures (ie, frequently compromised organs at risk) identified. Quantitative evaluations by GEM and/or WES compared favorably with the normal tissue complication probability Lyman-Kutcher-Burman model, transforming a set of discrete threshold-priority limits into a continuous model reflecting physician objectives and historical experience. Conclusions: Statistical DVH offers an easy-to-read, detailed, and comprehensive way to visualize the quantitative comparison with historical experiences and among institutions. WES and GEM metrics offer a flexible means of incorporating discrete threshold-prioritizations and historic context into a set of standardized scoring metrics. Together, they provide a practical approach for incorporating big data into clinical practice for treatment plan evaluations.

Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge. Development of strategies to meet this goal is aided by examining issues with a data farming instead of a data mining conceptualization. Using this model, a vision of key data elements, clinical process changes, technology issues and solutions, and role for professional societies is presented. With a better view of technology, process and standardization factors, definition and prioritization of efforts can be more effectively directed.

Background: Spine stereotactic body radiation therapy (SBRT) is becoming widely accepted as first-line treatment of oligometastatic spine disease as well as in the postoperative setting. The reported incidence of myelopathy is very low and guidelines vary widely on the maximum tolerable dose of the spinal cord and thecal sac. Methods and materials: Between April 2008 and December 2010, radiation parameters were exported for 46 patients treated with spine SBRT at the Mayo Clinic. Using an in-house data mining program, dose-volume histogram constraints were extracted, including dose prescription, dose fractionation, planning target volume, planning target volume coverage, maximum dose to the cord, D2cc thecal sac, and D2cc spinal cord. Diagnostic magnetic resonance imaging scans and/or computed tomography myelograms were fused with the planning set to delineate the cord and thecal sac for receiver operating characteristic analysis of cord and thecal sac subvolume doses. A high-resolution planning at-risk volume was created in 1-mm increments for cord (1-7 mm) and the thecal sac (1-2 mm) to examine dose gradients that might be correlated with toxicity. Results: No patients experienced myelopathy with a median follow-up of 14 months. The most common toxicities were pain and nausea. Median values of D2cc maximum dose (maximum dose received by 2 cc of the organ at risk; biologically equivalent 2-Gy dose maximum [EQD2]) for cord and thecal sac were 38.5 Gy (range, 7.5-67.9 Gy) and 67.7 Gy (range, 15.5-155.8 Gy), respectively. Median values for high-dose subvolumes for cord and thecal sac were 2 times higher than the doses for 5% predicted grade 3 cord toxicity as recommended in the current literature. Cord D0.1cc[EQD2] ≥23.8 Gy was correlated with pain flare (n = 5). Thecal sac D2cc [EQD2] ≥29.3 Gy was a significant indicator of nausea. Conclusion: Current guidelines may overestimate the risk of myelopathy from spine SBRT. The current study's population included both radiation-naïve and retreatment cases, but no myelopathy was observed despite exceeding recommended spine limits.