The impact of advancing the standard of care in radiotherapy on operational treatment resources.

PURPOSE: To demonstrate the impact of implementing hypofractionated prescription regimens and advanced treatment techniques on institutional operational hours and radiotherapy personnel resources in a multi-institutional setting. The study may be used to describe the impact of advancing the standard of care with modern radiotherapy techniques on patient and staff resources. METHODS: This study uses radiation therapy data extracted from the radiotherapy information system from two tertiary care, university-affiliated cancer centers from 2012 to 2021. Across all patients in the analysis, the average fraction number for curative and palliative patients was reported each year in the decade. Also, the institutional operational treatment hours are reported for both centers. A sub-analysis for curative intent breast and lung radiotherapy patients was performed to contextualize the impact of changes to imaging, motion management, and treatment technique. RESULTS: From 2012 to 2021, Center 1 had 42 214 patient plans and Center 2 had 43 252 patient plans included in the analysis. Averaged over both centers across the decade, the average fraction number per patient decreased from 6.9 to 5.2 (25%) and 21.8 to 17.2 (21%) for palliative and curative patients, respectively. The operational treatment hours for both institutions increased from 8 h 15 min to 9 h 45 min (18%), despite a patient population increase of 45%. CONCLUSION: The clinical implementation of hypofractionated treatment regimens has successfully reduced the radiotherapy workload and operational treatment hours required to treat patients. This analysis describes the impact of changes to the standard of care on institutional resources.

Improving Management and Compliance of Radiation Therapy Linear Accelerator Quality Assurance Program With Automated Tracking Tools.

Purpose: Although quality assurance (QA) is crucial in radiation oncology departments, substantial efforts are required to monitor and ensure compliance to high standards. This work aims to analyze the impact of implementing a centralized and automated tracking dashboard on compliance and variance observed in radiation therapy QA results for linear accelerators. Methods and Materials: The study was performed in a large academic center with 7 linear accelerators. An in-house QA Dashboard was implemented in 2019 with design specifications including automated monitoring and visualization of QA progress, ease of use and accessibility, ease of integration, adaptability to new technologies, and facilitation of automated reminders. Monthly QA data were collected between 2016 and 2022 to analyze compliance pre- and post-dashboard implementation. Compliance was characterized as the percentage of tests completed on time. In addition, variance trends for linear accelerator dosimetry measurements and imaging were analyzed over 7 years. Results: In total, 76,066 records were analyzed. Of these records, 73,187 QA measurements were completed on time. Overall QA compliance increased from 75% in 2016 to >99% in 2019 after successful implementation of the QA Dashboard. The main improvement was observed for tests that were implemented more recently, eg, imaging QA and formal recording of daily QA review by physicists. The coefficient of variation was reduced by approximately a factor of 2 for imaging QA after the implementation of the QA Dashboard. For recorded dosimetry measurements, no substantial change in variance was observed. Conclusions: Implementation of the QA Dashboard resulted in a distinct increase in QA compliance. Reduction in the variance in QA measurements was observed for all imaging modalities. These findings demonstrate high impact of automated tracking QA tools on improved compliance and accuracy of QA.

Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy.

PURPOSE: To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brachytherapy through a prospective clinical study in a single institution. METHODS: The study included cervical cancer patients receiving high-dose-rate brachytherapy using intracavitary (IC) or hybrid interstitial (IC/IS) applicators. For each patient, the primary radiation oncologist contoured the high-risk clinical target volume on a pre-brachytherapy MRI, indicated the approximate applicator location, and made a clinical determination of the first fraction applicator. A pre-trained ML model predicted the applicator and IC/IS needle arrangement using tumor geometry. Following the first fraction, ML and radiation oncologist predictions were compared and a replanning study determined the applicator providing optimal organ-at-risk (OAR) dosimetry. The ML-predicted applicator and needle arrangement and the clinical determination were compared to this dosimetric ground truth. RESULTS: Ten patients were accrued from December 2020 to October 2022. Compared to the dosimetrically optimal applicator, both the radiation oncologist and ML had an accuracy of 70%. ML demonstrated better identification of patients requiring IC/IS applicators and provided balanced IC and IC/IS predictions. The needle selection model achieved an average accuracy of 82.5%. ML-predicted needle arrangements matched or improved plan quality when compared to clinically selected arrangements. Overall, ML predictions led to an average total improvement of 2.0 Gy to OAR doses over three treatment fractions when compared to clinical predictions. CONCLUSION: In the context of a single institution study, the presented ML model demonstrates valuable decision-support for the applicator and needle selection process with the potential to provide improved dosimetry. Future work will include a multi-center study to assess generalizability.

Accelerated Partial Breast Irradiation Using 5 Daily Fractions: A Prospective, Phase 2, Multicenter Trial of Cosmetic Outcomes and Toxicity-ACCEL Final Results.

PURPOSE: This study investigated the cosmetic degradation and toxicity for an accelerated partial breast irradiation (APBI) prescription delivered in 5 fractions over 1 week and compared the outcomes with those of whole breast irradiation (WBI). METHODS AND MATERIALS: The trial was a multicenter, single-arm, phase 2 prospective cohort study. Eligible women 50 years of age or older with estrogen receptor-positive and human epidermal growth factor receptor 2-negative invasive ductal carcinoma or ductal carcinoma in situ after breast-conserving surgery received 27 Gy in 5 daily fractions of APBI. The primary endpoint was noninferiority of 2-year cosmesis using the RAPID trial's WBI arm as the control arm. A global consensus cosmetic score using a European Organisation for Research and Treatment of Cancer rating scale score of excellent, good, fair, or poor for each patient at baseline and 2 years was generated by a panel of 5 radiation oncologists using photographs of treated and untreated breasts. RESULTS: From 2016 to 2019, 298 eligible women were enrolled. By the 2-year follow-up, 76 patients had been lost or withdrawn and 3 had died, resulting in 219 patients available for complete, 2-year photographic cosmetic evaluation. The median follow-up for all participants was 4.7 years (IQR, 3.8-5.5 years). No patient had a fair or poor cosmetic score at the 2-year evaluation. Cosmesis was better or unchanged for 97% of patients and worse for 3% (excellent to good), and no cosmetic failures occurred. The confidence intervals were 0.88 (0.86-0.90) and 1.00 (0.99-1.00) for the RAPID and ACCEL trials, respectively. CONCLUSIONS: Cosmetic degradation with 5 daily treatments of the ACCEL trial's APBI intervention is noninferior to the WBI arm of the RAPID trial.

An Analysis of Clinical and Systemic Factors Associated with Palliative Radiotherapy Delivery and Completion at the End of Life in Alberta, Canada.

Radiotherapy (RT) is often utilized for symptom control at the end of life. Palliative RT (pRT) may not be taken to completion by patients, thus decreasing clinical benefits and adversely impacting resource allocation. We determined rates of incomplete pRT and examined predictors of non-completion using an electronic questionnaire. Methods: A questionnaire was embedded within the RT electronic prescribing system for all five cancer centers of Alberta, Canada, between 2017 and 2020. Prescribing radiation oncologists (ROs) were tasked with completing the questionnaire. Treatment variables were collected for 2040 patients prescribed pRT. Details on pRT courses delivered and completed were used to determine rates of incomplete RT. Electronic medical records of a subset of 367 patients randomly selected from the 2040 patients were then analyzed to examine for association of non-completion of RT with patient, disease, and therapy-related factors. Results: Overall, 10% of patients did not complete pRT. The rate of single fractions prescribed as a proportion of all RT fractions increased from 18% (pre-2017: pre-study era) to 29% (2017-2020: study era) (p < 0.0001). After conducting multivariate analysis on the overall group, multiple lifetime malignancies (OR:0.64) or increasing the number of pRT fractions (OR:0.08-0.17) were associated with non-completion. Being selected for stereotactic RT (OR:3.75) or survival > 30 days post-RT prescription (OR:2.20-5.02) were associated with greater rates of RT completion. The ROs' estimates of life expectancy at the time of RT prescription were not predictive of RT completion. In the multivariate analysis of the 367-patient subset, the presence of hepatic metastases (OR 2.59), survival 30-59 days (OR 6.61) and survival 90+ days (OR 8.18) post-RT prescription were associated with pRT completion. Only increasing pRT fractionation (OR:0.05-0.2) was associated with non-completion. Conclusion: One in ten patients prescribed pRT did not complete their treatment course. Decreasing pRT fractionation and improving prognostication in patients near the end of life may decrease rates of incomplete RT courses.

Assessment of dose to vaginal mucosa for gynecologic template interstitial high-dose-rate brachytherapy using Monte Carlo simulation.

Purpose: This study investigated reliable vaginal mucosa dose-volume histogram (DVH) metrics in gynecologic template interstitial high-dose-rate brachytherapy (HDR-BT) for the purpose of standardized dose reporting. Material and methods: Gynecologic template (Syed/Neblett) interstitial HDR-BT patients treated from September 2016 to November 2022 at the study institute were included in the cohort. Each patient implant included a vaginal mucosa contour defined by a 5 mm expansion from vaginal cylinder, then another volume with clinical target volume subtracted. DVH metrics were investigated between D0.1cc to D4cc. Clinical plans were re-calculated using Monte Carlo (MC) simulations both in heterogeneous material and in water. Results: The patient cohort included 61 patients with clinical plans using conventional homogeneous dose calculation (TG43). Heterogeneous vs. water MC dose differences were between -1.1% and -1.4% for all metrics investigated. DVH metrics D1cc and smaller resulted in > 5% discrepancies between TG43 and MC dose (to water) calculation due to the proximity of source positions in/nearby the vaginal mucosa. Reliability improved when DVH metric volume was larger (D2cc and D4cc). Both D2cc and D4cc presented very high linear correlation between TG43 and MC reported doses for the vagina, and average ± standard deviation dose difference was 4.6 ±2.9% and -3.0 ±1.9%, respectively. Dose differences decreased when the clinical target volume was removed: -1.5 ±3.5% and -0.8 ±2.1% for D2cc and D4cc, respectively. Conclusions: For perineal template gynecologic HDR-BT procedures, the 2 cc volume is the smallest representative volume that reliably reports vaginal dose and at minimum should be reported to establish dose and outcome evaluation.

Microwave imaging for monitoring breast cancer treatment: A pilot study.

BACKGROUND: Microwave imaging has been proposed for medical applications, creating maps related to water content of tissues. Breast imaging has emerged as a key application because the signals can be coupled directly into the breast and experience limited attenuation in fatty tissues. While the literature contains reports of tumor detection with microwave approaches, there is limited exploration of treatment monitoring. PURPOSE: This study aims to detect treatment-related changes in breast tissue with a low-resolution microwave scanner. METHODS: Microwave scans of 15 patients undergoing treatment for early-stage breast cancer are collected at up to 4 time points: after surgery (baseline), 6 weeks after accelerated partial breast radiation, as well as 1 and 2 years post-treatment. Both the treated and untreated breast are scanned at each time point. The microwave scanner consists of planar transmit and receive arrays and uses signals from 0.1 to 10 GHz. The average microwave frequency properties (permittivity) are calculated for each scan to enable quantitative comparison. Baseline and 6-week results are analyzed with a two-way ANOVA with blocking. RESULTS: Consistent properties are observed for the untreated breast over time, similar to a previous study. Comparison of the scans of the treated and untreated breast suggests increased properties related to treatment, particularly at baseline and 6-weeks following radiotherapy. Analysis of the average properties of the scans with ANOVA indicates statistically significant differences ( p < 0.05 $p < 0.05$ ) in the treated and untreated breast at these time points. CONCLUSIONS: Microwave imaging has the potential to track treatment-related changes in breast tissues.

Clinical implementation of 3D transvaginal ultrasound for intraoperative guidance of needle implant in template interstitial gynecologic high-dose-rate brachytherapy.

PURPOSE: To demonstrate novel clinical implementation of a 3D transvaginal ultrasound (3DTVUS) system for intraoperative needle insertion guidance in perineal template interstitial gynecologic high-dose-rate brachytherapy and assess its impact on implant quality. METHODS AND MATERIALS: Interstitial implants began with preimplant 3DTVUS to visualize the tumor and anatomy, with intermittent 3DTVUS to assess the implant and guide needle adjustment. Analysis includes visualization of the implant relative to anatomy, identification of cases where 3DTVUS is beneficial, dosimetry, and a survey distributed to 3DTVUS clinicians. RESULTS: Seven patients treated between November 2021 and October 2022 were included in this study. Twenty needles were inserted under 3DTVUS guidance. The tumor and vaginal wall were well-differentiated in four and all seven patients, respectively. Patients with tumours below the superior aspect of the vagina are suited for 3DTVUS. Four radiation oncologists responded to the survey. There was general agreement that 3DTVUS improves implant and anatomy visualization and is preferred over standard 2D ultrasound guidance techniques. CONCLUSIONS: Based on qualitative feedback from primary users and a small preliminary patient cohort, 3DTVUS imaging improves tumor and vaginal wall visualization during gynecologic perineal template interstitial needle implant and is a powerful tool for implant assessment in an intraoperative setting.

The transition in practice to reduce bolus use in post-mastectomy radiotherapy: A dosimetric study of skin and subcutaneous tissue.

To inform clinical practice for women receiving post-mastectomy radiotherapy (PMRT), this study demonstrates the dosimetric impact of removing daily bolus on skin and subcutaneous tissue. Two planning strategies were used: clinical field-based (n = 30) and volume-based planning (n = 10). The clinical field-based plans were created with bolus and recalculated without bolus for comparison. The volume-based plans were created with bolus to ensure a minimum target coverage of the chest wall PTV and recalculated without bolus. In each scenario, the dose to superficial structures, including skin (3 mm and 5 mm) and subcutaneous tissue (a 2 mm layer, 3 mm deep from surface) were reported. Additionally, the difference in the clinically evaluated dosimetry to skin and subcutaneous tissue in volume-based plans were recalculated using Acuros (AXB) and compared to the Anisotropic Analytical Algorithm (AAA) algorithm. For all treatment planning strategies, chest wall coverage (V90%) was maintained. As expected, superficial structures demonstrate significant loss in coverage. The largest difference observed in the most superficial 3 mm where V90% coverage is reduced from a mean (± standard deviation) of 95.1% (± 2.8) to 18.9% (± 5.6) for clinical field-based treatments with and without bolus, respectively. For volume-based planning, the subcutaneous tissue maintains a V90% of 90.5% (± 7.0) compared to the clinical field-based planning coverage of 84.4% (± 8.0). In all skin and subcutaneous tissue, the AAA algorithm underestimates the volume of the 90% isodose. Removing bolus results in minimal dosimetric differences in the chest wall and significantly lower skin dose while dose to the subcutaneous tissue is maintained. Unless the skin has disease involvement, the most superficial 3 mm is not considered part of the target volume. The continued use of the AAA algorithm is supported for the PMRT setting.

Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology.

Introduction: Artificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted software tools to actual clinical environments remains unrealized. We present the Deep Learning On-Demand Assistant (DL-ODA), a fully automated, end-to-end clinical platform that enables AI interventions for any disease site featuring an automated model-training pipeline, auto-segmentations, and QA reporting. Materials and methods: We developed, tested, and prospectively deployed the DL-ODA system at a large university affiliated hospital center. Medical professionals activate the DL-ODA via two pathways (1): On-Demand, used for immediate AI decision support for a patient-specific treatment plan, and (2) Ambient, in which QA is provided for all daily radiotherapy (RT) plans by comparing DL segmentations with manual delineations and calculating the dosimetric impact. To demonstrate the implementation of a new anatomy segmentation, we used the model-training pipeline to generate a breast segmentation model based on a large clinical dataset. Additionally, the contour QA functionality of existing models was assessed using a retrospective cohort of 3,399 lung and 885 spine RT cases. Ambient QA was performed for various disease sites including spine RT and heart for dosimetric sparing. Results: Successful training of the breast model was completed in less than a day and resulted in clinically viable whole breast contours. For the retrospective analysis, we evaluated manual-versus-AI similarity for the ten most common structures. The DL-ODA detected high similarities in heart, lung, liver, and kidney delineations but lower for esophagus, trachea, stomach, and small bowel due largely to incomplete manual contouring. The deployed Ambient QAs for heart and spine sites have prospectively processed over 2,500 cases and 230 cases over 9 months and 5 months, respectively, automatically alerting the RT personnel. Discussion: The DL-ODA capabilities in providing universal AI interventions were demonstrated for On-Demand contour QA, DL segmentations, and automated model training, and confirmed successful integration of the system into a large academic radiotherapy department. The novelty of deploying the DL-ODA as a multi-modal, fully automated end-to-end AI clinical implementation solution marks a significant step towards a generalizable framework that leverages AI to improve the efficiency and reliability of RT systems.

Radiation Therapy With Cyclin-Dependent Kinase 4/6 Inhibitors: A Multi-institutional Safety and Toxicity Study.

PURPOSE: Our purpose was to investigate radiation therapy (RT) toxicity when given with cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) compared with RT alone. METHODS AND MATERIALS: We conducted a retrospective cohort study of patients with hormonal receptor-positive and human epidermal growth factor-2 negative metastatic breast cancer treated with RT at 4 cancer centers in Alberta, Canada, between 2016 and 2020. Toxicity in patients treated with RT within 30 days of initiating to discontinuing CDK4/6i (RT + CDK4/6i) was compared with toxicity of RT in CDK4/6i-naïve patients (RT alone). The primary outcome was acute grade (G) 2 or higher, nonhematological toxicity within 30 days of RT. We also explored toxicity based on the timing of RT (prior, concurrent, post) in relation to CDK4/6i. Propensity score matching was applied to create comparable cohorts. A generalized linear mixed model was used to evaluate factors associated with acute toxicity. RESULTS: One hundred thirty-two patients (220 RT sites) in the RT + CDK4/6i and 53 patients (93 RT sites) in RT alone were eligible. The rate of acute G2 or higher nonhematological toxicity was 11.5% versus 7%, respectively (P = .439), and acute G3 or higher nonhematological toxicity was 3.7% versus 0%, respectively (P = .151). Acute toxicity in RT + CDK4/6i group was mainly observed when RT was given concurrently (67%), with most of the G3 toxicity recorded. After propensity score matching, the association of acute toxicity with RT + CDK4/6i versus RT alone was not significant on multivariable analysis (odds ratio, 3.13; 95% confidence interval, 0.74-13.2; P = .121). CONCLUSIONS: We did not observe a significant association between CDK4/6i use and acute G2 or higher nonhematological toxicity in women with metastatic breast cancer receiving palliative RT. Given the findings of G3 toxicity, caution is advised whenever CDK4/6i is given concurrently with RT.

Assessment of tissue toxicity risk in breast radiotherapy using Bayesian networks.

PURPOSE: The purpose of this analysis is to predict worsening post-treatment normal tissue toxicity in patients undergoing accelerated partial breast irradiation (APBI) therapy and to quantitatively identify which diagnostic, anatomical, and dosimetric features are contributing to these outcomes. METHODS: A retrospective study of APBI treatments was performed using 32 features pertaining to various stages of the patient's treatment journey. These features were used to inform and construct a Bayesian network (BN) based on both statistical analysis of feature distributions and relative clinical importance. The target feature for prediction was defined as a measurable worsening of telangiectasia, subcutaneous tissue induration, or fibrosis when compared against the observed baseline. Parameter learning for the network was performed using data from the 299 patients included in the ACCEL trial and predictive performance was measured. Feature importance for the BN was quantified using a novel information-theoretic approach. RESULTS: Cross-validated performance of the BN for predicting toxicity was consistently higher when compared against conventional machine learning (ML) techniques. The measured BN receiver operating characteristic area under the curve was 0.960 ± $\,{\pm}\,$ 0.013 against the best ML result of 0.942 ± $\,{\pm}\,$ 0.021 using five-fold cross-validation with separate test data across 100 trials. The volume of the clinical target volume, gross target volume, and baseline toxicity measurements were found to have the highest feature importance and mutual dependence with normal tissue toxicity in the network, representing the strongest contribution to patient outcomes. CONCLUSIONS: The BN outperformed conventional ML techniques in predicting tissue toxicity outcomes and provided deeper insight into which features are contributing to these outcomes.

An updated approach for deriving PTV margins using image guidance and deformable dose accumulation.

Objective.To demonstrate an updated approach for deriving planning target volume (PTV) margins for a patient population treated with volumetric image-guided radiotherapy.Approach.The approach uses a semi-automated workflow within commercial radiotherapy applications that combines dose accumulation with the bidirectional local distance (BLD) metric. The patient cohort is divided into derivation and validation datasets. For each patient in the derivation dataset, a treatment plan is generated with a 0 mm PTV margin (the idealized treatment scenario without the influence of the standard margin). Deformable image registration enabled dose accumulation of these zero-margin plans. PTV margins are derived by using the BLD to calculate the geometric extent of underdosed regions of the clinical target volume (CTV). The PTV margin is validated by ensuring the specified CTV coverage criterion is met when the margin is applied to the validation dataset.Main results.The methodology was applied to two cohorts: 40 oropharyngeal cancer patients and 50 early-stage breast cancer patients. Ten patients from each cohort were used for validation. PTV margins derived for the oropharyngeal and early-stage breast cancer patient cohorts were 3 and 5 mm, respectively, and ensure that 95% of the prescription dose is delivered to 98% of the CTV for 90% of patients. Dose accumulation showed that the CTV coverage criterion was achieved for at least 90% of patients when the margins were applied.Significance.This methodology can be used to derive appropriate PTV margins for realistic treatment scenarios and any disease site, which will improve our understanding of patient outcomes.

Machine learning-generated decision boundaries for prediction and exploration of patient-specific quality assurance failures in stereotactic radiosurgery plans.

INTRODUCTION: Stereotactic radiosurgery (SRS) is a form of radiotherapy treatment during which high radiation dose is delivered in a single or few fractions. These treatments require highly conformal plans with steep dose gradients, which can result in an increase in plan complexity prompting the need for stringent pretreatment patient-specific quality assurance (QA) measurements to ensure the planned and measured dose distributions agree within clinical standards. Complexity scores and machine learning (ML) techniques may help with prediction of QA outcomes; however interpretability and usability of those results continues to be an area of study. This study investigates the use of plan complexity metrics as input for an ML model to allow for prediction of QA outcomes for SRS plans as measured via three-dimension (3D) phantom dose verification. Explorations into interpretability and predictive ability, as well as a prospective in-clinic implementation using the resulting model were performed. METHODS: Four hundred ninety-eight plans (1571 volumetric modulated arc therapy arcs) were processed via in-house script to generate several complexity scores. 3D phantom dose verification measurement results were extracted and classified as pass or failure (with failures defined as below 95% voxel agreement passing 3%/1-mm gamma criteria with 10% threshold,) and 1472 of the arcs were split into training and testing sets, with 99 arcs as a sequential holdout set. A z-score scaler was trained on the training set and used to scale all other sets. Variations of multi-leaf collimator (MLC) leaf movement variability, aperture complexity, and leaf size, and monitor unit (MU) at control point weighted target area scores were used as input to a support vector classifier to generate a series of 1D, 2D, and 5D decision boundaries. The best performing 5D model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements for 38 patient plans (112 arcs). The decision to order 3D phantom dose verification measurements was recorded before and after prediction. RESULTS: Best performing 1D threshold and 2D prediction models with best performance produced a QA failure recall and QA passing recall of 1.00 and 0.55, and 0.82 and 0.82, respectively. Best performing 5D prediction model produced a QA failure recall (sensitivity) of 1.00 and QA passing recall (specificity) of 0.72. This model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements and achieved a QA failure recall of 1.00 and QA passing recall of 0.58. The decision to order 3D phantom dose verification measurements was recorded before and after measurement. A single initially unidentified failing plan of the prospective cohort was successfully predicted to fail by the model. CONCLUSION: Implementation of complexity score-based prediction models for SRS would allow for support of a clinician's decision to reduce time spent performing QA measurements and avoid patient treatment delays (i.e., in case of QA failure).

Competency-Based Medical Education in Radiation Therapy Treatment Planning.

PURPOSE: To develop a technology-enhanced education methodology with competency-based evaluation for radiation therapy treatment planning. The education program is designed for integration in the existing framework of Commission on Accreditation of Medical Physics Education Programs (CAMPEP) accredited medical physics residency programs. METHODS AND MATERIALS: This education program pairs an accessible, multi-institutional infrastructure with established medical education evaluation tools to modernize treatment planning education. This program includes 3 evaluation components: (1) competency-based evaluation, (2) inter- and intramodality comparison, and (3) learner feedback. For this study, synchronous bilateral breast cancer was selected to demonstrate a complex treatment site and nonstandardized technique. Additionally, an online study was made available to a public cohort of worldwide participants of certified Medical Dosimetrists and Medical Physicists to benchmark performance. Before evaluation, learners were given a disease site-specific education session on potential clinical treatment strategies. During the assessment, learners generated treatment plans in their institutional planning system under the direct observation of an expert evaluator. Qualitative proficiency was evaluated for all learners on a 5-point scale of graduated task independence. Quantitative dosimetry was compared between the learner cohort and public cohort. A feedback session provided learners context of multi-institutional experience through multimodality and technique comparison. After study completion, learners were provided a survey that was used to gauge their perception of the education program. RESULTS: In the public study, 34 participants submitted treatment plans. Across 3 CAMPEP-accredited residency programs, 6 learners participated in the education and evaluation program. All learners successfully completed treatment plans that met the dosimetric constraints described in the case study. All learners favorably reviewed the study either comprehensively or in specified domains. CONCLUSIONS: The competency-based education and evaluation program developed in this work has been incorporated in CAMPEP-accredited residency programs and is adaptable to other residency programs with minimal resource commitment.

Performance of a knowledge-based planning model for optimizing intensity-modulated radiotherapy plans for partial breast irradiation.

PURPOSE: To evaluate a knowledge-based (KB) planning model for RapidPlan, generated using a five-field intensity-modulated radiotherapy (IMRT) class solution beam strategy and rigorous dosimetric constraints for accelerated partial breast irradiation (APBI). MATERIALS AND METHODS: The RapidPlan model was configured using 64 APBI treatment plans and validated for 120 APBI patients who were not included in the training dataset. KB plan dosimetry was compared to clinical plan dosimetry, the clinical planning constraints, and the constraints used in phase III APBI trials. Dosimetric differences between clinical and KB plans were evaluated using paired two-tailed Wilcoxon signed-rank tests. RESULTS: KB planning was able to produce IMRT-based APBI plans in a single optimization without manual intervention that are comparable or better than the conventionally optimized, clinical plans. Comparing KB plans to clinical plans, differences in PTV, heart, contralateral breast, and ipsilateral lung dose-volume metrics were not clinically significant. The ipsilateral breast volume receiving at least 50% of the prescription dose was statistically and clinically significantly lower in the KB plans. CONCLUSION: KB planning for IMRT-based APBI provides equivalent or better dosimetry compared to conventional inverse planning. This model may be reliably applied in clinical practice and could be used to transfer planning expertise to ensure consistency in APBI plan quality.

Increasing Demand on Human Capital and Resource Utilization in Radiation Therapy: The Past Decade.

PURPOSE: To quantify the change resource utilization in radiation therapy in the context of advancing technologies and techniques over the last decade. METHODS AND MATERIALS: Prospectively, the time to complete radiation therapy workflow tasks was captured between January 1, 2020, and December 31, 2020. The institutional task workflows are specific to each technique and broadly organized into 4 categories: 3-dimenstional conformal radiation therapy, intensity modulated radiation therapy, volumetric modulated arc therapy simple, and volumetric modulated arc therapy complex. These discipline-specific task times were used to quantify a resource utilization factor, which is the median time taken to complete all tasks for each category divided by the median time for 3-dimensional conformal radiation therapy treatments. Retrospectively, all plans treated between January 1, 2012, and December 31, 2019, were quantified and categorized. The resource factor was applied to determine resource utilization. For context, institutional staffing levels were captured across the same decade for medical dosimetrists, medical physicists, and radiation oncologists. RESULTS: This analysis includes 30,229 patient plans in the retrospective data set and 4747 patient plans in the prospective data set. This analysis demonstrates that over this period, patient numbers increased by approximately 45%, whereas time-based human resources increased by almost 150%. The resource allocation factors for 3-dimenstional conformal radiation therapy, intensity modulated radiation therapy, volumetric modulated arc therapy simple, and volumetric arc therapy complex were 1.0, 2.4, 2.9, and 4.3, respectively. Across the 3 disciplines, staffing levels increased from 15 to 17 (13%) for medical dosimetrists, from 10 to 13 (30%) for medical physicists, and from 16 to 23 (44%) for radiation oncologists. CONCLUSIONS: This work demonstrates the increase in resource utilization due to the introduction of advanced technologies and changes in radiation therapy techniques over the past decade. Human resource utilization is the predominant factor and should be considered with increasing patient volume for operational planning.

Clinical Outcomes from Dose-Reduced Radiotherapy to the Prostate in Elderly Patients with Localized Prostate Cancer.

Radical treatment of localized prostate cancer in elderly patients may lead to unacceptable treatment-associated toxicities that adversely impact quality of life without improving survival outcomes. This study reports on a cohort of 54 elderly (>70 years) patients that received 4000-5000 cGy of palliative external beam radiotherapy (EBRT) as an alternative to androgen deprivation therapy (ADT). The primary outcome of interest was the period of ADT-free survival, and secondary outcomes included overall survival (OS) and metastases-free survival (MFS). Kaplan-Meier regression was used to estimate survival outcomes. Thirty-six (67%) patients achieved a break in ADT post-radiotherapy, with a median time to ADT reinitiation of 20 months. Common Terminology Criteria for Adverse Events (CTCAE) were limited to low-grade gastrointestinal (GI) or genitourinary (GU) toxicities, with no skin toxicities observed. Grade 1 GI toxicity was observed in 9 (17%) patients, and grades 1 and 2 GU toxicities were observed in 13 (24%) and 3 (6%) patients, respectively, with no higher-grade toxicities reported. Five-year MFS and OS were 56% and 78%, respectively. In summary, the treatment regimen was well-tolerated and achieved durable ADT-free survival in most patients. Dose-reduced EBRT appears to be a viable alternative to ADT in elderly patients with localized prostate cancer.

Technical Note: A standardized automation framework for monitoring institutional radiotherapy protocol compliance.

PURPOSE: To establish a framework for the standardization of monitoring radiotherapy protocol compliance. METHODS: An automated protocol compliance tool was developed using best practice in software design and a flexible framework to easily adapt to changing institutional standards. The Eclipse scripting environment was used to develop the application with the scripting application programing interface (API) and direct data extraction from ARIA. For each institutional protocol, external validation was specified in a JavaScript Object Notation (JSON) file that stores protocol specific constraints and evaluates compliance of the data from Eclipse and Aria. This tool was applied prospectively to a cohort of prostate cancer patients undergoing radiotherapy with a prescription regimen of 60 Gy in 20 fractions. RESULTS: The prospective evaluation was performed on 58 prostate cancer patients. For this cohort, the mean (standard deviation) pass rate is 92.3% (6.1%). The overall fail rate is 6.0% (5.8%); the percentage of these failures is in 2.6% in Patient Assessment, 0% in Simulation, and 97.4% in Treatment Planning. CONCLUSIONS: A protocol compliance application is developed and implemented in a standard radiotherapy information system. The application functionality is demonstrated on a cohort of 58 patients undergoing prostate radiotherapy, which highlights the utility of assessing adherence to institutional protocols. A unified method must be available for the community to ensure consistency in compliance reporting.

Dosimetric consequences of seed placement accuracy in permanent breast seed implant brachytherapy.

PURPOSE: This study quantifies the dosimetric impact of implant accuracy and derives a quantitative relationship relating implant accuracy to target dosimetry. METHODS AND MATERIALS: A framework was developed to simulate multiple implants for error combinations. Spherical clinical target volumes (CTVs) were modeled with volumes 1.4 cm3, 9.2 cm3, and 20.6 cm3, representing the range seen clinically. Each CTV was expanded 10 mm isotropically to create a planning target volume (PTV).. Random and systematic seed placement errors were simulated by shifting needles from their planned positions. Implant errors were simulated over the range of clinically practical errors in permanent breast seed implant. The relative effect on target coverage was evaluated. Regression analysis was performed to derive relationships between CTV dosimetry and the magnitude of implant accuracy. The validity of the clinically used 10 mm PTV margin for each of the CTVs was assessed. RESULTS: Introducing practical implant errors resulted in CTV V90% median (10th and 90th percentile) of 97.7% (85.9% and 100%), 96.2% (86.8% and 99.7%), and 100% (77.8% and 100%) for the typical, large, and small CTV, respectively. All CTVs show similar trends in target coverage. Polynomials were derived relating seed placement accuracy to median (R2 = 0.82) and 10th percentile (R2 = 0.78) CTV V90%.. CONCLUSIONS: This work quantitatively describes the relationship between implant accuracy and CTV coverage. Based on simulations, the 10 mm PTV margin is adequate to maintain target coverage. These equations can be used with institutional seed placement accuracy to estimate coverage.