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.

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.

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.

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.

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.

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.

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 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.

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.

Combining Serial and Parallel Functionality in Functional Lung Avoidance Radiation Therapy.

PURPOSE: Functional lung avoidance (FLA) radiation therapy (RT) aims to minimize post-RT pulmonary toxicity by preferentially avoiding dose to high-functioning lung (HFL) regions. A common limitation is that FLA approaches do not consider the conducting architecture for gas exchange. We previously proposed the functionally weighted airway sparing (FWAS) method to spare airways connected to HFL regions, showing that it is possible to substantially reduce risk of radiation-induced airway injury. Here, we compare the performance of FLA and FWAS and propose a novel method combining both approaches. METHODS: We used breath-hold computed tomography (BHCT) and simulation 4-dimensional computed tomography (4DCT) from 12 lung stereotactic ablative radiation therapy patients. Four planning strategies were examined: (1) Conventional: no sparing other than clinical dose-volume constraints; (2) FLA: using a 4DCT-based ventilation map to delineate the HFL, plans were optimized to reduce mean dose and V13.50 in HFL; (3) FWAS: we autosegemented 11 to 13 generations of individual airways from each patient's BHCT and assigned priorities based on the relative contribution of each airway to total ventilation. We used these priorities in the optimization along with airway dose constraints, estimated as a function of airway diameter and 5% probability of collapse; and (4) FLA + FWAS: we combined information from the 2 strategies. We prioritized clinical dose constraints for organs at risk and planning target volume in all plans. We performed the evaluation in terms of ventilation preservation accounting for radiation-induced damage to both lung parenchyma and airways. RESULTS: We observed average ventilation preservation for FLA, FWAS, and FLA + FWAS as 3%, 8.5%, and 14.5% higher, respectively, than for Conventional plans for patients with ventilation preservation in Conventional plans <90%. Generalized estimated equations showed that all improvements were statistically significant (P ≤ .036). We observed no clinically relevant improvements in outcomes of the sparing techniques in patients with ventilation preservation in Conventional plans ≥90%. CONCLUSIONS: These initial results suggest that it is crucial to consider the parallel and the serial nature of the lung to improve post-radiation therapy lung function and, consequently, quality of life for patients.

Racial Differences in Treatments and Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Thoracic Radiation Therapy.

PURPOSE: Historical racial disparities in lung cancer surgery rates resulted in lower survival in Black patients. Our objective was to examine racial differences in thoracic radiation treatments and toxicities in patients with non-small-cell lung cancer. METHODS AND MATERIALS: A large institutional review board-approved statewide patient-level database of patients with stage II-III non-small-cell lung cancer who received definitive thoracic radiation from March 2012 to November 2019 was analyzed to assess associations between race and other variables. Race (White or Black) was defined by patient self-report. Provider-reported toxicity was defined by Common Terminology Criteria for Adverse Events version 4.0. Patient-reported toxicity was determined by the Functional Assessment of Cancer Therapy-Lung quality-of-life instrument. Univariable and multivariable regression models were fitted to assess relationships between race and variables of interest. Spearman rank-correlation coefficients were calculated between provider-reported toxicity and similar patient-reported outcomes. RESULTS: One thousand four hundred forty-one patients from 24 institutions with mean age 68 years (range, 38-94 years) were evaluated. Race was not significantly associated with radiation or chemotherapy approach. There was significantly increased patient-reported general pain in Black patients at the preradiation and end-of-radiation time points. Black patients were significantly less likely to have provider-reported grade 2+ pneumonitis (odds ratio 0.36, P = .03), even after controlling for known patient and treatment factors. Correlation coefficients between provider- and patient-reported toxicities were generally similar across race groups except for a stronger correlation between patient- and provider-reported esophagitis in White patients. CONCLUSION: In this large multi-institutional study, we found no evidence of racial differences in radiation treatment or chemotherapy approaches. We did, however, unexpectedly find that Black race was associated with lower odds of provider-reported grade 2+ radiation pneumonitis. The stronger correlation between patient- and provider-reported esophagitis and swallowing symptoms for White patients also suggests possible under-recognition of symptoms in Black patients. Further research is needed to study the implications for Black patients.

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.

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.

Association Between Physician- and Patient-Reported Symptoms in Patients Treated With Definitive Radiation Therapy for Locally Advanced Lung Cancer in a Statewide Consortium.

PURPOSE: Little data have been reported about the patient experience during curative radiation therapy (RT) for lung cancer in routine clinical practice or how this relates to treatment toxicity as reported by clinicians. The purpose of this study was to compare clinician-reported adverse events (AEs) with patient-reported outcomes (PROs), including both specific symptoms/side effects, as well as overall quality of life (QoL) during and after definitive RT for locally advanced lung cancer (LALC) in a large statewide cohort. METHODS AND MATERIALS: PROs were prospectively collected from patients treated with definitive RT for LALC at 24 institutions within the Michigan Radiation Oncology Quality Consortium between 2012 and 2018 using the Functional Assessment of Cancer Therapy trial outcome index. Physicians prospectively recorded AEs using the Common Terminology Criteria for Adverse Events, version 4.0. Patient-reported QoL changes from baseline were assessed during and after RT using the Functional Assessment of Cancer Therapy trial outcome index. Spearman correlation coefficients were calculated for AEs and similar PROs, and a multivariable analysis was used to assess associations with QoL. RESULTS: A total 1361 patients were included in the study, and 53% of respondents reported clinically meaningful declines in QoL at the end of RT. The correlation between clinician-reported esophagitis and patient-reported trouble swallowing was moderate (R = .67), but correlations between clinician-reported pneumonitis and patient-reported shortness of breath (R = .13) and cough (R = .09) were weak. Clinician-reported AEs were significantly associated with clinically meaningful declines in patient-reported QoL (R = - .46 for summary AE score). QoL was more strongly associated with fatigue (R = - .41) than lung-specific AEs. CONCLUSIONS: AEs are associated with clinically meaningful declines in QoL during and after RT for LALC, but associations between AEs and QoL are only modest. This highlights the importance of PRO data, and future research should assess whether earlier detection of PRO changes could allow for interventions that reduce the frequency of treatment-related clinically meaningful declines in QoL.

Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy.

Subtle differences in a patient's genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient's dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients' specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.

Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer.

Objective.Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop a framework, which given an initial state defined by patient-attributes, can predict future states based on pre-learned patterns from a well-defined patient population.Approach.Here, we investigate the feasibility of predicting patient anatomical changes, defined as a joint state of volume and daily setup changes, across a fractionated treatment schedule using two approaches. The first is based on a new joint framework employing quantum mechanics in combination with deep recurrent neural networks, denoted QRNN. The second approach is developed based on a classical framework, which models patient changes as a Markov process, denoted MRNN. We evaluated the performance characteristics of these two approaches on a dataset of 125 head and neck cancer patients, which was supplemented by synthetic data generated using a generative adversarial network. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) scores.Main results.The MRNN framework had slightly better performance than the QRNN framework, with MRNN (QRNN) validation AUC scores of 0.742±0.021 (0.675±0.036), 0.709±0.026 (0.656±0.021), 0.724±0.036 (0.652±0.044), and 0.698±0.016 (0.605±0.035) for system state vector sizes of 4, 6, 8, and 10, respectively. Of these, only the results from the two higher order states had statistically significant differences(p<0.05).A similar trend was also observed when the models were applied to an external testing dataset of 20 patients, yielding MRNN (QRNN) AUC scores of 0.707 (0.623), 0.687 (0.608), 0.723 (0.669), and 0.697 (0.609) for states vectors sizes of 4, 6, 8, and 10, respectively.Significance.These results suggest that both stochastic models have potential value in predicting patient changes during the course of adaptive radiotherapy.

A simulation study of ionizing radiation acoustic imaging (iRAI) as a real-time dosimetric technique for ultra-high dose rate radiotherapy (UHDR-RT).

PURPOSE: Electron-based ultra-high dose rate radiation therapy (UHDR-RT), also known as Flash-RT, has shown the ability to improve the therapeutic index in comparison to conventional radiotherapy (CONV-RT) through increased sparing of normal tissue. However, the extremely high dose rates in UHDR-RT have raised the need for accurate real-time dosimetry tools. This work aims to demonstrate the potential of the emerging technology of Ionized Radiation Acoustic Imaging (iRAI) through simulation studies and investigate its characteristics as a promising relative in vivo dosimetric tool for UHDR-RT. METHODS: The detection of induced acoustic waves following a single UHDR pulse of a modified 6 MeV 21EX Varian Clinac in a uniform porcine gelatin phantom that is brain-tissue equivalent was simulated for an ideal ultrasound transducer. The full 3D dose distributions in the phantom for a 1 × 1 cm2 field were simulated using EGSnrc (BEAMnrc∖DOSXYZnrc) Monte Carlo (MC) codes. The relative dosimetry simulations were verified with dose experimental measurements using Gafchromic films. The spatial dose distribution was converted into an initial pressure source spatial distribution using the medium-dependent dose-pressure relation. The MATLAB-based toolbox k-Wave was then used to model the propagation of acoustic waves through the phantom and perform time-reversal (TR)-based imaging reconstruction. The effect of the various linear accelerator (linac) operating parameters, including linac pulse duration and pulse repetition rate (frequency), were investigated as well. RESULTS: The MC dose simulation results agreed with the film measurement results, specifically at the central beam region up to 80% dose within approximately 5% relative error for the central profile region and a local relative error of <6% for percentage dose depth. IRAI-based FWHM of the radiation beam was within approximately 3 mm relative to the MC-simulated beam FWHM at the beam entrance. The real-time pressure signal change agreed with the dose changes proving the capability of the iRAI for predicting the beam position. IRAI was tested through 3D simulations of its response to be based on the temporal changes in the linac operating parameters on a dose per pulse basis as expected theoretically from the pressure-dose proportionality. The pressure signal amplitude obtained through 2D simulations was proportional to the dose per pulse. The instantaneous pressure signal amplitude decreases as the linac pulse duration increases, as predicted from the pressure wave generation equations, such that the shorter the linac pulse the higher the signal and the better the temporal (spatial) resolutions of iRAI. The effect of the longer linac pulse duration on the spatial resolution of the 3D constructed iRAI images was corrected for linac pulse deconvolution. This correction has improved the passing rate of the 1%/1 mm gamma test criteria, between the pressure-constructed and dosimetric beam characteristics, to as high as 98%. CONCLUSIONS: A full simulation workflow was developed for testing the effectiveness of iRAI as a promising relative dosimetry tool for UHDR-RT radiation therapy. IRAI has shown the advantage of 3D dose mapping through the dose signal linearity and, hence, has the potential to be a useful dosimeter at depth dose measurement and beam localization and, hence, potentially for in vivo dosimetry in UHDR-RT.