A qualitative investigation of resilience and well-being among medical physics residents.

PURPOSE: Medical physics residents (MPRs) will define and shape the future of physics in medicine. We sought to better understand the residency experience, as related to resilience and well-being, through the lens of current MPRs and medical physicists (MPs) working with residents. METHODS AND MATERIALS: From February-May 2019, we conducted 32, 1-h, confidential, semi-structured interviews with MPs either currently enrolled in an accredited residency (n = 16) or currently employed by a department with an accredited residency (n = 16). Interviews centered on the topics of mentorship, work/life integration, and discrimination. Qualitative analysis methods were used to derive key themes from the interview transcripts. RESULTS: With regard to the medical physics residency experience, four key themes emerged during qualitative analysis: the demanding nature of medical physics residencies, the negative impacts of residency on MPRs during training and beyond, strategies MPRs use to cope with residency stress, and the role of professional societies in addressing residency-related change. CONCLUSIONS: Residency training is a stress-inducing time in the path to becoming a board-certified MP. By uncovering several sources of this stress, we have identified opportunities to support the resiliency and well-being of MPs in training through recommendations by professional societies, programmatic changes, and interventions at the department and residency program director level for residency programs, as well as strategies that MPRs themselves can use to support well-being on their career journey.

The impact of a high-definition multileaf collimator for spine SBRT.

PURPOSE: Advanced radiotherapy delivery systems designed for high-dose, high-precision treatments often come equipped with high-definition multi-leaf collimators (HD-MLC) aimed at more finely shaping radiation dose to the target. In this work, we study the effect of a high definition MLC on spine stereotactic body radiation therapy (SBRT) treatment plan quality and plan deliverability. METHODS AND MATERIALS: Seventeen spine SBRT cases were planned with VMAT using a standard definition MLC (M120), HD-MLC, and HD-MLC with an added objective to reduce monitor units (MU). M120 plans were converted into plans deliverable on an HD-MLC using in-house software. Plan quality and plan deliverability as measured by portal dosimetry were compared among the three types of plans. RESULTS: Only minor differences were noted in plan quality between the M120 and HD-MLC plans. Plans generated with the HD-MLC tended to have better spinal cord sparing (3% reduction in maximum cord dose). HD-MLC plans on average had 12% more MU and 55% greater modulation complexity as defined by an in-house metric. HD-MLC plans also had significantly degraded deliverability. Of the VMAT arcs measured, 94% had lower gamma passing metrics when using the HD-MLC. CONCLUSION: Modest improvements in plan quality were noted when switching from M120 to HD-MLC at the expense of significantly less accurate deliverability in some cases.

Predicting deliverability of volumetric-modulated arc therapy (VMAT) plans using aperture complexity analysis.

The purpose of this study was to evaluate the ability of an aperture complexity metric for volumetric-modulated arc therapy (VMAT) plans to predict plan delivery accuracy. We developed a complexity analysis tool as a plug-in script to Varian's Eclipse treatment planning system. This script reports the modulation of plans, arcs, and individual control points for VMAT plans using a previously developed complexity metric. The calculated complexities are compared to that of 649 VMAT plans previously treated at our institution from 2013 to mid-2015. We used the VMAT quality assurance (QA) results from the 649 treated plans, plus 62 plans that failed pretreatment QA, to validate the ability of the complexity metric to predict plan deliverability. We used a receiver operating characteristic (ROC) analysis to determine an appropriate complexity threshold value above which a plan should be considered for reoptimization before it moves further through our planning workflow. The average complexity metric for the 649 treated plans analyzed with the script was 0.132 mm-1 with a standard deviation of 0.036 mm-1. We found that when using a threshold complexity value of 0.180 mm-1, the true positive rate for correctly identifying plans that failed QA was 44%, and the false-positive rate was 7%. Used clinically with this threshold, the script can identify overly modulated plans and thus prevent a significant portion of QA failures. Reducing VMAT plan complexity has a number of important clinical benefits, including improving plan deliverability and reducing treatment time. Use of the complexity metric during both the planning and QA processes can reduce the number of QA failures and improve the quality of VMAT plans used for treatment.

Improving treatment plan evaluation with automation.

The goal of this work is to evaluate the effectiveness of Plan-Checker Tool (PCT) which was created to improve first-time plan quality, reduce patient delays, increase the efficiency of our electronic workflow, and standardize and automate the phys-ics plan review in the treatment planning system (TPS). PCT uses an application programming interface to check and compare data from the TPS and treatment management system (TMS). PCT includes a comprehensive checklist of automated and manual checks that are documented when performed by the user as part of a plan readiness check for treatment. Prior to and during PCT development, errors identified during the physics review and causes of patient treatment start delays were tracked to prioritize which checks should be automated. Nineteen of 33checklist items were automated, with data extracted with PCT. There was a 60% reduction in the number of patient delays in the six months after PCT release. PCT was suc-cessfully implemented for use on all external beam treatment plans in our clinic. While the number of errors found during the physics check did not decrease, automation of checks increased visibility of errors during the physics check, which led to decreased patient delays. The methods used here can be applied to any TMS and TPS that allows queries of the database.

SafetyNet: Streamlining and Automating QA in radiotherapy.

Proper quality assurance (QA) of the radiotherapy process can be time-consuming and expensive. Many QA efforts, such as data export and import, are inefficient when done by humans. Additionally, humans can be unreliable, lose attention, and fail to complete critical steps that are required for smooth operations. In our group we have sought to break down the QA tasks into separate steps and to automate those steps that are better done by software running autonomously or at the instigation of a human. A team of medical physicists and software engineers worked together to identify opportunities to streamline and automate QA. Development efforts follow a formal cycle of writing software requirements, developing software, testing and commissioning. The clinical release process is separated into clinical evaluation testing, training, and finally clinical release. We have improved six processes related to QA and safety. Steps that were previously performed by humans have been automated or streamlined to increase first-time quality, reduce time spent by humans doing low-level tasks, and expedite QA tests. Much of the gains were had by automating data transfer, implementing computer-based checking and automation of systems with an event-driven framework. These coordinated efforts by software engineers and clinical physicists have resulted in speed improvements in expediting patient-sensitive QA tests.

Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss.

BACKGROUND: Investigations on radiation-induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation-induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post-radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose-response models that effectively incorporate both local and distant RILI effects. PURPOSE: We develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation-induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways. METHODS: In an IRB-approved prospective study, pre-RT breath-hold CTs (BHCTs) and pre- and one-year post-RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre- and post-RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP-based estimate and predicted additional loss due radiation-induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post-RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k-fold cross-validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post-RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post-RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed. RESULTS: We extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions. CONCLUSIONS: These early findings indicate that airway damage is a crucial factor in RILI that should be included in dose-response modeling to enhance predictions of post-RT lung function.

Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods: We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results: AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions: Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.

Real-time, volumetric imaging of radiation dose delivery deep into the liver during cancer treatment.

Ionizing radiation acoustic imaging (iRAI) allows online monitoring of radiation's interactions with tissues during radiation therapy, providing real-time, adaptive feedback for cancer treatments. We describe an iRAI volumetric imaging system that enables mapping of the three-dimensional (3D) radiation dose distribution in a complex clinical radiotherapy treatment. The method relies on a two-dimensional matrix array transducer and a matching multi-channel preamplifier board. The feasibility of imaging temporal 3D dose accumulation was first validated in a tissue-mimicking phantom. Next, semiquantitative iRAI relative dose measurements were verified in vivo in a rabbit model. Finally, real-time visualization of the 3D radiation dose delivered to a patient with liver metastases was accomplished with a clinical linear accelerator. These studies demonstrate the potential of iRAI to monitor and quantify the 3D radiation dose deposition during treatment, potentially improving radiotherapy treatment efficacy using real-time adaptive treatment.

Gender-Based Discrimination and Sexual Harassment in Medical Physics.

PURPOSE: Gender-based discrimination and sexual harassment have been well-studied in the fields of science, technology, engineering, math, and medicine. However, less is known about these topics and their effect within the profession of medical physics. We aimed to better understand and clarify the views and experiences of practicing medical physicists and medical physics residents regarding gender-based discrimination and sexual harassment. METHODS AND MATERIALS: We conducted in-depth, semistructured, and confidential interviews with 32 practicing medical physicists and medical physics residents across the United States. The interviews were broad and covered the topics of discrimination, mentorship, and work/life integration. All participants were associated with a department with a residency program accredited by the Commission on Accreditation of Medical Physics Education Programs and had appointments with a clinical component. RESULTS: Participants shared views about gender-based discrimination and sexual harassment that were polarized. Some perceived that discrimination and harassment were a current concern within medical physics, while some either perceived that they were not a concern or that discrimination positively affected women and minoritized populations. Many participants shared personal experiences of discrimination and harassment, including those related to unequal compensation, discrimination against mothers, discrimination during the hiring process, gender-biased assumptions about behaviors or goals, communication biases, and overt and persistent sexual harassment. CONCLUSIONS: There is an urgent need to acknowledge, better understand, and address gender-based discrimination and sexual harassment in the field of medical physics.

A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).

Involvement of many variables, uncertainty in treatment response, and inter-patient heterogeneity challenge objective decision-making in dynamic treatment regime (DTR) in oncology. Advanced machine learning analytics in conjunction with information-rich dense multi-omics data have the ability to overcome such challenges. We have developed a comprehensive artificial intelligence (AI)-based optimal decision-making framework for assisting oncologists in DTR. In this work, we demonstrate the proposed framework to Knowledge Based Response-Adaptive Radiotherapy (KBR-ART) applications by developing an interactive software tool entitled Adaptive Radiotherapy Clinical Decision Support (ARCliDS). ARCliDS is composed of two main components: Artifcial RT Environment (ARTE) and Optimal Decision Maker (ODM). ARTE is designed as a Markov decision process and modeled via supervised learning. Given a patient's pre- and during-treatment information, ARTE can estimate treatment outcomes for a selected daily dosage value (radiation fraction size). ODM is formulated using reinforcement learning and is trained on ARTE. ODM can recommend optimal daily dosage adjustments to maximize the tumor local control probability and minimize the side effects. Graph Neural Networks (GNN) are applied to exploit the inter-feature relationships for improved modeling performance and a novel double GNN architecture is designed to avoid nonphysical treatment response. Datasets of size 117 and 292 were available from two clinical trials on adaptive RT in non-small cell lung cancer (NSCLC) patients and adaptive stereotactic body RT (SBRT) in hepatocellular carcinoma (HCC) patients, respectively. For training and validation, dense data with 297 features were available for 67 NSCLC patients and 110 features for 71 HCC patients. To increase the sample size for ODM training, we applied Generative Adversarial Networks to generate 10,000 synthetic patients. The ODM was trained on the synthetic patients and validated on the original dataset. We found that, Double GNN architecture was able to correct the nonphysical dose-response trend and improve ARCliDS recommendation. The average root mean squared difference (RMSD) between ARCliDS recommendation and reported clinical decisions using double GNNs were 0.61 [0.03] Gy/frac (mean [sem]) for adaptive RT in NSCLC patients and 2.96 [0.42] Gy/frac for adaptive SBRT HCC compared to the single GNN's RMSDs of 0.97 [0.12] Gy/frac and 4.75 [0.16] Gy/frac, respectively. Overall, For NSCLC and HCC, ARCliDS with double GNNs was able to reproduce 36% and 50% of the good clinical decisions (local control and no side effects) and improve 74% and 30% of the bad clinical decisions, respectively. In conclusion, ARCliDS is the first web-based software dedicated to assist KBR-ART with multi-omics data. ARCliDS can learn from the reported clinical decisions and facilitate AI-assisted clinical decision-making for improving the outcomes in DTR.

Development and Clinical Implementation of an Automated Virtual Integrative Planner for Radiation Therapy of Head and Neck Cancer.

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.

NRG Oncology/RTOG1205: A Randomized Phase II Trial of Concurrent Bevacizumab and Reirradiation Versus Bevacizumab Alone as Treatment for Recurrent Glioblastoma.

PURPOSE: To assess whether reirradiation (re-RT) and concurrent bevacizumab (BEV) improve overall survival (OS) and/or progression-free survival (PFS), compared with BEV alone in recurrent glioblastoma (GBM). The primary objective was OS, and secondary objectives included PFS, response rate, and treatment adverse events (AEs) including delayed CNS toxicities. METHODS: NRG Oncology/RTOG1205 is a prospective, phase II, randomized trial of re-RT and BEV versus BEV alone. Stratification factors included age, resection, and Karnofsky performance status (KPS). Patients with recurrent GBM with imaging evidence of tumor progression ≥ 6 months from completion of prior chemo-RT were eligible. Patients were randomly assigned 1:1 to re-RT, 35 Gy in 10 fractions, with concurrent BEV IV 10 mg/kg once in every 2 weeks or BEV alone until progression. RESULTS: From December 2012 to April 2016, 182 patients were randomly assigned, of whom 170 were eligible. Patient characteristics were well balanced between arms. The median follow-up for censored patients was 12.8 months. There was no improvement in OS for BEV + RT, hazard ratio, 0.98; 80% CI, 0.79 to 1.23; P = .46; the median survival time was 10.1 versus 9.7 months for BEV + RT versus BEV alone. The median PFS for BEV + RT was 7.1 versus 3.8 months for BEV, hazard ratio, 0.73; 95% CI, 0.53 to 1.0; P = .05. The 6-month PFS rate improved from 29.1% (95% CI, 19.1 to 39.1) for BEV to 54.3% (95% CI, 43.5 to 65.1) for BEV + RT, P = .001. Treatment was well tolerated. There were a 5% rate of acute grade 3+ treatment-related AEs and no delayed high-grade AEs. Most patients died of recurrent GBM. CONCLUSION: To our knowledge, NRG Oncology/RTOG1205 is the first prospective, randomized multi-institutional study to evaluate the safety and efficacy of re-RT in recurrent GBM using modern RT techniques. Overall, re-RT was shown to be safe and well tolerated. BEV + RT demonstrated a clinically meaningful improvement in PFS, specifically the 6-month PFS rate but no difference in OS.

Prospective Evaluation of Limited-Stage Small Cell Lung Cancer Radiotherapy Fractionation Regimen Usage and Acute Toxicity in a Large Statewide Quality Collaborative.

PURPOSE: National guidelines on limited-stage small cell lung cancer (LS-SCLC) treatment give preference to a hyperfractionated regimen of 45 Gy in 30 fractions delivered twice daily; however, use of this regimen is uncommon compared with once-daily regimens. The purpose of this study was to characterize the LS-SCLC fractionation regimens used throughout a statewide collaborative, analyze patient and treatment factors associated with these regimens, and describe real-world acute toxicity profiles of once- and twice-daily radiation therapy (RT) regimens. METHODS AND MATERIALS: Demographic, clinical, and treatment data along with physician-assessed toxicity and patient-reported outcomes were prospectively collected by 29 institutions within the Michigan Radiation Oncology Quality Consortium between 2012 and 2021 for patients with LS-SCLC. We modeled the influence of RT fractionation and other patient-level variables clustered by treatment site on the odds of a treatment break specifically due to toxicity with multilevel logistic regression. National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0, incident grade 2 or worse toxicity was longitudinally compared between regimens. RESULTS: There were 78 patients (15.6% overall) treated with twice-daily RT and 421 patients treated with once-daily RT. Patients receiving twice-daily RT were more likely to be married or living with someone (65% vs 51%; P = .019) and to have no major comorbidities (24% vs 10%; P = .017). Once-daily RT fractionation toxicity peaked during RT, and twice-daily toxicity peaked within 1 month after RT. After stratifying by treatment site and adjusting for patient-level variables, once-daily treated patients had 4.11 (95% confidence interval, 1.31-12.87) higher odds of treatment break specifically due to toxicity than twice-daily treated patients. CONCLUSIONS: Hyperfractionation for LS-SCLC remains infrequently prescribed despite the lack of evidence demonstrating superior efficacy or lower toxicity of once-daily RT. With peak acute toxicity after RT and lower likelihood of a treatment break with twice-daily fractionation in real-word practice, providers may start using hyperfractionated RT more frequently.

Inverse-optimized 3D conformal planning: minimizing complexity while achieving equivalence with beamlet IMRT in multiple clinical sites.

PURPOSE: Inverse planned intensity modulated radiation therapy (IMRT) has helped many centers implement highly conformal treatment planning with beamlet-based techniques. The many comparisons between IMRT and 3D conformal (3DCRT) plans, however, have been limited because most 3DCRT plans are forward-planned while IMRT plans utilize inverse planning, meaning both optimization and delivery techniques are different. This work avoids that problem by comparing 3D plans generated with a unique inverse planning method for 3DCRT called inverse-optimized 3D (IO-3D) conformal planning. Since IO-3D and the beamlet IMRT to which it is compared use the same optimization techniques, cost functions, and plan evaluation tools, direct comparisons between IMRT and simple, optimized IO-3D plans are possible. Though IO-3D has some similarity to direct aperture optimization (DAO), since it directly optimizes the apertures used, IO-3D is specifically designed for 3DCRT fields (i.e., 1-2 apertures per beam) rather than starting with IMRT-like modulation and then optimizing aperture shapes. The two algorithms are very different in design, implementation, and use. The goals of this work include using IO-3D to evaluate how close simple but optimized IO-3D plans come to nonconstrained beamlet IMRT, showing that optimization, rather than modulation, may be the most important aspect of IMRT (for some sites). METHODS: The IO-3D dose calculation and optimization functionality is integrated in the in-house 3D planning/optimization system. New features include random point dose calculation distributions, costlet and cost function capabilities, fast dose volume histogram (DVH) and plan evaluation tools, optimization search strategies designed for IO-3D, and an improved, reimplemented edge/octree calculation algorithm. The IO-3D optimization, in distinction to DAO, is designed to optimize 3D conformal plans (one to two segments per beam) and optimizes MLC segment shapes and weights with various user-controllable search strategies which optimize plans without beamlet or pencil beam approximations. IO-3D allows comparisons of beamlet, multisegment, and conformal plans optimized using the same cost functions, dose points, and plan evaluation metrics, so quantitative comparisons are straightforward. Here, comparisons of IO-3D and beamlet IMRT techniques are presented for breast, brain, liver, and lung plans. RESULTS: IO-3D achieves high quality results comparable to beamlet IMRT, for many situations. Though the IO-3D plans have many fewer degrees of freedom for the optimization, this work finds that IO-3D plans with only one to two segments per beam are dosimetrically equivalent (or nearly so) to the beamlet IMRT plans, for several sites. IO-3D also reduces plan complexity significantly. Here, monitor units per fraction (MU/Fx) for IO-3D plans were 22%-68% less than that for the 1 cm × 1 cm beamlet IMRT plans and 72%-84% than the 0.5 cm × 0.5 cm beamlet IMRT plans. CONCLUSIONS: The unique IO-3D algorithm illustrates that inverse planning can achieve high quality 3D conformal plans equivalent (or nearly so) to unconstrained beamlet IMRT plans, for many sites. IO-3D thus provides the potential to optimize flat or few-segment 3DCRT plans, creating less complex optimized plans which are efficient and simple to deliver. The less complex IO-3D plans have operational advantages for scenarios including adaptive replanning, cases with interfraction and intrafraction motion, and pediatric patients.

Penalization of aperture complexity in inversely planned volumetric modulated arc therapy.

PURPOSE: Apertures obtained during volumetric modulated arc therapy (VMAT) planning can be small and irregular, resulting in dosimetric inaccuracies during delivery. Our purpose is to develop and integrate an aperture-regularization objective function into the optimization process for VMAT, and to quantify the impact of using this objective function on dose delivery accuracy and optimized dose distributions. METHODS: An aperture-based metric ("edge penalty") was developed that penalizes complex aperture shapes based on the ratio of MLC side edge length and aperture area. To assess the utility of the metric, VMAT plans were created for example paraspinal, brain, and liver SBRT cases with and without incorporating the edge penalty in the cost function. To investigate the dose calculation accuracy, Gafchromic EBT2 film was used to measure the 15 highest weighted apertures individually and as a composite from each of two paraspinal plans: one with and one without the edge penalty applied. Films were analyzed using a triple-channel nonuniformity correction and measurements were compared directly to calculations. RESULTS: Apertures generated with the edge penalty were larger, more regularly shaped and required up to 30% fewer monitor units than those created without the edge penalty. Dose volume histogram analysis showed that the changes in doses to targets, organs at risk, and normal tissues were negligible. Edge penalty apertures that were measured with film for the paraspinal plan showed a notable decrease in the number of pixels disagreeing with calculation by more than 10%. For a 5% dose passing criterion, the number of pixels passing in the composite dose distributions for the non-edge penalty and edge penalty plans were 52% and 96%, respectively. Employing gamma with 3% dose/1 mm distance criteria resulted in a 79.5% (without penalty)/95.4% (with penalty) pass rate for the two plans. Gradient compensation of 3%/1 mm resulted in 83.3%/96.2% pass rates. CONCLUSIONS: The use of the edge penalty during optimization has the potential to markedly improve dose delivery accuracy for VMAT plans while still maintaining high quality optimized dose distributions. The penalty regularizes aperture shape and improves delivery efficiency.

A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy.

Background: Imbalanced outcome is one of common characteristics of oncology datasets. Current machine learning approaches have limitation in learning from such datasets. Here, we propose to resolve this problem by utilizing a human-in-the-loop (HITL) approach, which we hypothesize will also lead to more accurate and explainable outcome prediction models. Methods: A total of 119 HCC patients with 163 tumors were used in the study. 81 patients with 104 tumors from the University of Michigan Hospital treated with SBRT were considered as a discovery dataset for radiation outcomes model building. The external testing dataset included 59 tumors from 38 patients with SBRT from Princess Margaret Hospital. In the discovery dataset, 100 tumors from 77 patients had local control (LC) (96% of 104 tumors) and 23 patients had at least one grade increment of ALBI (I-ALBI) during six-month follow up (28% of 81 patients). Each patient had a total of 110 features, where 15 or 20 features were identified by physicians as expert knowledge features (EKFs) for LC or I-ALBI prediction. We proposed a HITL based Bayesian network (HITL-BN) approach to enhance the capability of selecting important features from imbalanced data in terms of accuracy and explainability through humans' participation by integrating feature importance ranking and Markov blanket algorithms. A pure data-driven Bayesian network (PD-BN) method was applied to the same discovery dataset of HCC patients as a benchmark. Results: In the training and testing phases, the areas under receiver operating characteristic curves of the HITL-BN models for LC or I-ALBI prediction during SBRT are 0.85 (95% confidence interval: 0.75-0.95) or 0.89 (0.81-0.95) and 0.77 or 0.78, respectively. They significantly outperformed the during-treatment PD-BN model in predicting LC or I-ALBI based on the discovery cross-validation and testing datasets from the Delong tests. Conclusion: By allowing the human expert to be part of the model building process, the HITL-BN approach yielded significantly improved accuracy as well as better explainability when dealing with imbalanced outcomes in the prediction of post-SBRT treatment response of HCC patients when compared to the PD-BN method.

A Safe and Practical Cycle for Team-Based Development and Implementation of In-House Clinical Software.

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.

Direct incorporation of patient-specific efficacy and toxicity estimates in radiation therapy plan optimization.

PURPOSE: Current radiation therapy (RT) treatment planning relies mainly on pre-defined dose-based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population-based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient-specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity-modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient-specific dose response models into the planning process. In this strategy, we integrate the concept of utility-based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non-small cell lung cancer (NSCLC) patients. METHODS AND MATERIALS: We developed a prioritized approach to patient-specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet-dose contributions to regions-of-interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade-off based on individualized dose-response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose-based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3-5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors. RESULTS: The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some patients, the utility-based plans resulted in similar local control estimates with decreased estimated toxicity. CONCLUSION: The proposed optimization approach, where the maximization of a patient's RT plan utility is prioritized over the minimization of standardized dose metrics, has the potential to improve treatment outcomes by directly accounting for variability within a patient population. The implementation of the utility-based objective function offers an intuitive, humanized approach to biological optimization in which planning trade-offs are explicitly optimized.

Uptake of Adjuvant Durvalumab After Definitive Concurrent Chemoradiotherapy for Stage III Nonsmall-cell Lung Cancer.

OBJECTIVES: The addition of adjuvant durvalumab improves overall survival in locally advanced nonsmall-cell lung cancer (NSCLC) patients treated with definitive chemoradiation, but the real-world uptake of adjuvant durvalumab is unknown. MATERIALS AND METHODS: We identified patients with stage III NSCLC treated with definitive concurrent chemoradiation from January 2018 to October 2020 from a statewide radiation oncology quality consortium, representing a mix of community (n=22 centers) and academic (n=5) across the state of Michigan. Use of adjuvant durvalumab was ascertained at the time of routine 3-month or 6-month follow-up after completion of chemoradiation. RESULTS: Of 421 patients with stage III NSCLC who completed chemoradiation, 322 (76.5%) initiated adjuvant durvalumab. The percentage of patients initiating adjuvant durvalumab increased over time from 66% early in the study period to 92% at the end of the study period. There was substantial heterogeneity by treatment center, ranging from 53% to 90%. In multivariable logistic regression, independent predictors of durvalumab initiation included more recent month (odds ratio [OR]: 1.05 per month, 95% confidence interval [CI]: 1.02-1.08, P=0.003), lower Eastern Cooperative Oncology Group score (OR: 4.02 for ECOG 0 vs. 2+, 95% CI: 1.67-9.64, P=0.002), and a trend toward significance for female sex (OR: 1.66, 95% CI: 0.98-2.82, P=0.06). CONCLUSION: Adjuvant durvalumab for stage III NSCLC treated with definitive chemoradiation was rapidly and successfully incorporated into clinical care across a range of community and academic settings in the state of Michigan, with over 90% of potentially eligible patients starting durvalumab in more recent months.

Comparative Effectiveness Analysis of 3D-Conformal Radiation Therapy Versus Intensity Modulated Radiation Therapy (IMRT) in a Prospective Multicenter Cohort of Patients With Breast Cancer.

PURPOSE: Simple intensity modulation of radiation therapy reduces acute toxicity compared with 2-dimensional techniques in adjuvant breast cancer treatment, but it remains unknown whether more complex or inverse-planned intensity modulated radiation therapy (IMRT) offers an advantage over forward-planned, 3-dimensional conformal radiation therapy (3DCRT). METHODS AND MATERIALS: Using prospective data regarding patients receiving adjuvant whole breast radiation therapy without nodal irradiation at 23 institutions from 2011 to 2018, we compared the incidence of acute toxicity (moderate-severe pain or moist desquamation) in patients receiving 3DCRT versus IMRT (either inverse planned or, if forward-planned, using ≥5 segments per gantry angle). We evaluated associations between technique and toxicity using multivariable models with inverse-probability-of-treatment weighting, adjusting for treatment facility as a random effect. RESULTS: Of 1185 patients treated with 3DCRT and conventional fractionation, 650 (54.9%) experienced acute toxicity; of 774 treated with highly segmented forward-planned IMRT, 458 (59.2%) did; and of 580 treated with inverse-planned IMRT, 245 (42.2%) did. Of 1296 patients treated with hypofractionation and 3DCRT, 432 (33.3%) experienced acute toxicity; of 709 treated with highly segmented forward-planned IMRT, 227 (32.0%) did; and of 623 treated with inverse-planned IMRT, 164 (26.3%) did. On multivariable analysis with inverse-probability-of-treatment weighting, the odds ratio for acute toxicity after inverse-planned IMRT versus 3DCRT was 0.64 (95% confidence interval, 0.45-0.91) with conventional fractionation and 0.41 (95% confidence interval, 0.26-0.65) with hypofractionation. CONCLUSIONS: This large, prospective, multicenter comparative effectiveness study found a significant benefit from inverse-planned IMRT compared with 3DCRT in reducing acute toxicity of breast radiation therapy. Future research should identify the dosimetric differences that mediate this association and evaluate cost-effectiveness.