Automated determination of the ion-recombination correction factor (ksat) in ultra-high dose rate electron radiation therapy.

BACKGROUND: Plane-parallel ionization chambers are the recommended secondary standard systems for clinical reference dosimetry of electrons. Dosimetry in high dose rate and dose-per-pulse (DPP) is challenging as ionization chambers are subject to ion recombination, especially when dose rate and/or DPP is increased beyond the range of conventional radiotherapy. The lack of universally accepted models for correction of ion recombination in UDHR is still an issue as it is, especially in FLASH-RT research, which is crucial in order to be able to accurately measure the dose for a wide range of dose rates and DPPs. PURPOSE: The objective of this study was to show the feasibility of developing an Artificial Intelligence model to predict the ion-recombination factor-ksat for a plane-parallel Advanced Markus ionization chamber for conventional and ultra-high dose rate electron beams based on machine parameters. In addition, the predicted ksat of the AI model was compared with the current applied analytical models for this correction factor. METHODS: A total number of 425 measurements was collected with a balanced variety in machine parameter settings. The specific ksat values were determined by dividing the output of the reference dosimeter (optically stimulated luminescence [OSL]) by the output of the AM chamber. Subsequently, a XGBoost regression model was trained, which used the different machine parameters as input features and the corresponding ksat value as output. The prediction accuracy of this regression model was characterized by R2-coefficient of determination, mean absolute error and root mean squared error. In addition, the model was compared with the Two-Voltage (TVA) method and empirical Petersson model for 19 different dose-per-pulse values ranging from conventional to UDHR regimes. The Akiake Information criterion (AIC) was calculated for the three different models. RESULTS: The XGBoost regression model reached a R2-score of 0.94 on the independent test set with a MAE of 0.067 and RMSE of 0.106. For the additional 19 random data points, the ksat values predicted by the XGBoost model showed to be in agreement, within the uncertainties, with the ones determined by the Petersson model and better than the TVA method for doses per pulse >3.5 Gy with a maximum deviation from the ground truth of 14.2%, 16.7%, and -36.0%, respectively, for DPP >4 Gy. CONCLUSION: The proposed method of using AI for ksat determination displays efficiency. For the investigated DPPs, the ksat values obtained with the XGBoost model were in concurrence with the ones obtained with the current available analytical models within the boundaries of uncertainty, certainly for the DPP characterizing UDHR. But the overall performance of the AI model, taking the number of free parameters into account, lacked efficiency. Future research should optimize the determination of the experimental ksat, and investigate the determination the ksat for DPPs higher than the ones investigated in this study, while also evaluating the prediction of the proposed XGBoost model for UDHR machines of different centers.

Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy.

Background and purpose: Treatment plans in radiotherapy are subject to measurement-based pre-treatment verifications. In this study, plan complexity metrics (PCMs) were calculated per beam and used as input features to develop a predictive model. The aim of this study was to determine the robustness against differences in machine type and institutional-specific quality assurance (QA). Material and methods: A number of 567 beams were collected, where 477 passed and 90 failed the pre-treatment QA. Treatment plans of different anatomical regions were included. One type of linear accelerator was represented. For all beams, 16 PCMs were calculated. A random forest classifier was trained to distinct between acceptable and non-acceptable beams. The model was validated on other datasets to investigate its robustness. Firstly, plans for another machine type from the same institution were evaluated. Secondly, an inter-institutional validation was conducted on three datasets from different centres with their associated QA. Results: Intra-institutionally, the PCMs beam modulation, mean MLC gap, Q1 gap, and Modulation Complexity Score were the most informative to detect failing beams. Eighty-tree percent of the failed beams (15/18) were detected correctly. The model could not detect over-modulated beams of another machine type. Inter-institutionally, the model performance reached higher accuracy for centres with comparable equipment both for treatment and QA as the local institute. Conclusions: The study demonstrates that the robustness decreases when major differences appear in the QA platform or in planning strategies, but that it is feasible to extrapolate institutional-specific trained models between centres with similar clinical practice. Predictive models should be developed for each machine type.

A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer.

Background and Purpose: Clinical Artificial Intelligence (AI) implementations lack ground-truth when applied on real-world data. This study investigated how combined geometrical and dose-volume metrics can be used as performance monitoring tools to detect clinically relevant candidates for model retraining. Materials and Methods: Fifty patients were analyzed for both AI-segmentation and planning. For AI-segmentation, geometrical (Standard Surface Dice 3 mm and Local Surface Dice 3 mm) and dose-volume based parameters were calculated for two organs (bladder and anorectum) to compare AI output against the clinically corrected structure. A Local Surface Dice was introduced to detect geometrical changes in the vicinity of the target volumes, while an Absolute Dose Difference (ADD) evaluation increased focus on dose-volume related changes. AI-planning performance was evaluated using clinical goal analysis in combination with volume and target overlap metrics. Results: The Local Surface Dice reported equal or lower values compared to the Standard Surface Dice (anorectum: (0.93 ± 0.11) vs (0.98 ± 0.04); bladder: (0.97 ± 0.06) vs (0.98 ± 0.04)). The ADD metric showed a difference of (0.9 ± 0.8)Gy for the anorectum D1cm3. The bladder D5cm3 reported a difference of (0.7 ± 1.5)Gy. Mandatory clinical goals were fulfilled in 90 % of the DLP plans. Conclusions: Combining dose-volume and geometrical metrics allowed detection of clinically relevant changes, applied to both auto-segmentation and auto-planning output and the Local Surface Dice was more sensitive to local changes compared to the Standard Surface Dice. This monitoring is able to evaluate AI behavior in clinical practice and allows candidate selection for active learning.

Quality Assurance for AI-Based Applications in Radiation Therapy.

Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment. However, the QA for AI-based systems is an emerging area, which has not been intensively explored and requires interactive collaborations between medical doctors, medical physics experts, and commercial/research AI institutions. This article summarizes the current QA methodologies for AI modules of every subdomain in RT with further focus on persistent shortcomings and upcoming key challenges and perspectives.

Machine learning-based treatment couch parameter prediction in support of surface guided radiation therapy.

Purpose: A fully independent, machine learning-based automatic treatment couch parameters prediction was developed to support surface guided radiation therapy (SGRT)-based patient positioning protocols. Additionally, this approach also acts as a quality assurance tool for patient positioning. Materials/Methods: Setup data of 183 patients, divided into four different groups based on used setup devices, was used to calculate the difference between the predicted and the acquired treatment couch value. Results: Couch parameters can be predicted with high precision µ = 0.90 , σ = 0.92 . A significant difference (p < 0.01) between the variances of Lung and Brain patients was found. Outliers were not related to the prediction accuracy, but are due to inconsistencies during initial patient setup. Conclusion: Couch parameters can be predicted with high accuracy and can be used as starting point for SGRT-based patient positioning. In case of large deviations (>1.5 cm), patient setup has to be verified to optimally use the surface scanning system.

Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm.

Objective.The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially leads to significant errors with impact on the overall treatment quality. Additionally, when the time needed to correct auto-segmentations approaches the time to delineate target and organs at risk from scratch, the usability of the DL model can be questioned. Therefore, an automated quality assurance framework was developed with the aim to detect in advance aberrant auto-segmentations.Approach. Five organs (prostate, bladder, anorectum, femoral head left and right) were auto-delineated on CT acquisitions for 48 prostate patients by an in-house trained primary DL model. An experienced radiation oncologist assessed the correctness of the model output and categorised the auto-segmentations into two classes whether minor or major adaptations were needed. Subsequently, an independent, secondary DL model was implemented to delineate the same structures as the primary model. Quantitative comparison metrics were calculated using both models' segmentations and used as input features for a machine learning classification model to predict the output quality of the primary model.Main results. For every organ, the approach of independent validation by the secondary model was able to detect primary auto-segmentations that needed major adaptation with high sensitivity (recall = 1) based on the calculated quantitative metrics. The surface DSC and APL were found to be the most indicated parameters in comparison to standard quantitative metrics for the time needed to adapt auto-segmentations.Significance. This proposed method includes a proof of concept for the use of an independent DL segmentation model in combination with a ML classifier to improve time saving during QA of auto-segmentations. The integration of such system into current automatic segmentation pipelines can increase the efficiency of the radiotherapy contouring workflow.

X­irradiation induces acute and early term inflammatory responses in atherosclerosis­prone ApoE­/­ mice and in endothelial cells.

Thoracic radiotherapy is an effective treatment for many types of cancer; however it is also associated with an increased risk of developing cardiovascular disease (CVD), appearing mainly ≥10 years after radiation exposure. The present study investigated acute and early term physiological and molecular changes in the cardiovascular system after ionizing radiation exposure. Female and male ApoE­/­ mice received a single exposure of low or high dose X­ray thoracic irradiation (0.1 and 10 Gy). The level of cholesterol and triglycerides, as well as a large panel of inflammatory markers, were analyzed in serum samples obtained at 24 h and 1 month after irradiation. The secretion of inflammatory markers was further verified in vitro in coronary artery and microvascular endothelial cell lines after exposure to low and high dose of ionizing radiation (0.1 and 5 Gy). Local thoracic irradiation of ApoE­/­ mice increased serum growth differentiation factor­15 (GDF­15) and C­X­C motif chemokine ligand 10 (CXCL10) levels in both female and male mice 24 h after high dose irradiation, which were also secreted from coronary artery and microvascular endothelial cells in vitro. Sex­specific responses were observed for triglyceride and cholesterol levels, and some of the assessed inflammatory markers as detailed below. Male ApoE­/­ mice demonstrated elevated intercellular adhesion molecule­1 and P­selectin at 24 h, and adiponectin and plasminogen activator inhibitor­1 at 1 month after irradiation, while female ApoE­/­ mice exhibited decreased monocyte chemoattractant protein­1 and urokinase­type plasminogen activator receptor at 24 h, and basic fibroblast growth factor 1 month after irradiation. The inflammatory responses were mainly significant following high dose irradiation, but certain markers showed significant changes after low dose exposure. The present study revealed that acute/early inflammatory responses occurred after low and high dose thoracic irradiation. However, further research is required to elucidate early asymptomatic changes in the cardiovascular system post thoracic X­irradiation and to investigate whether GDF­15 and CXCL10 could be considered as potential biomarkers for the early detection of CVD risk in thoracic radiotherapy­treated patients.

Probabilistic dose prediction using mixture density networks for automated radiation therapy treatment planning.

We demonstrate the application of mixture density networks (MDNs) in the context of automated radiation therapy treatment planning. It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain decision making associated with inherently conflicting clinical tradeoffs, in contrast to deterministic methods previously investigated in the literature. A two-component Gaussian MDN is trained on a set of treatment plans for postoperative prostate patients with varying extents to which rectum dose sparing was prioritized over target coverage. Examination on a test set of patients shows that the predicted modes follow their respective ground truths well, both spatially and in terms of their dose-volume histograms. A special dose mimicking method based on the MDN output is used to produce deliverable plans and thereby showcase the usability of voxel-wise predictive densities. Thus, this type of MDN may serve to support clinicians in managing clinical tradeoffs and has the potential to improve the quality of plans produced by an automated treatment planning pipeline.

Stereotactic Ablative Radiation Therapy to All Lesions in Patients With Oligometastatic Cancers: A Phase 1 Dose-Escalation Trial.

PURPOSE: Increasing evidence suggests that patients with a limited number of metastases benefit from SABR to all lesions. However, the optimal dose and fractionation remain unknown. This is particularly true for bone and lymph node metastases. Therefore, a prospective, single-center, dose-escalation trial was initiated. METHODS: Dose-Escalation trial of STereotactic ablative body RadiOtherapY for non-spine bone and lymph node metastases (DESTROY) was an open-label phase 1 trial evaluating SABR to nonspine bone and lymph node lesions in patients with up to 3 metastases. Patients with European Cooperative Oncology Group performance status ≤1, an estimated life expectancy of at least 6 months, and histologically confirmed nonhematological malignancy were eligible. Three SABR fractionation regimens, ie, 5 fractions of 7.0 Gy versus 3 fractions of 10.0 Gy versus a single fraction of 20.0 Gy, were applied in 3 consecutive patient cohorts. The rate of ≥grade 3 toxicity, scored according to the Common Toxicity Criteria for Adverse Events v. 4.03, up to 6 months after SABR, was the primary endpoint. The trial was registered on clinicaltrials.gov (NCT03486431). RESULTS: Between July 2017 and December 2018, 90 patients were enrolled. In total 101 metastases were treated. No ≥grade 3 toxicity was observed in any of the enrolled patients (95% CI 0.0%-12.3% for the first cohort with 28 analyzable patients; 95% CI 0.0%-11.6% for the second and third cohort with 30 analyzable patients each). Treatment-related grade 2 toxicities occurred in 4 out of 30 versus 2 out of 30 versus 2 out of 30 patients for the 5, 3 and 1 fraction schedule, respectively. Actuarial local control rate at 12 months was 94.5%. CONCLUSION: All 3 treatment schedules were feasible and effective with remarkably low toxicity rates and high local control rates. From a patient and resource point of view, the single-fraction schedule is undoubtedly most convenient.

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance.

Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation.

BACKGROUND AND PURPOSE: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice. MATERIALS AND METHODS: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications. RESULTS: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice. CONCLUSION: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.

Factors associated with palliative care knowledge among internal medicine house staff.

PURPOSE: To assess knowledge and associated factors in palliative care. METHODS: Self-administered survey of 88 internal medicine house officers in 1996. RESULTS: Twenty-one interns and 36 residents completed the survey for a response rate of 65%. Most house officers reported 1-5 hours of prior formal training in palliative care, 1-5 hours in pain management, and 6-20 hours in ethics. The mean knowledge score was 75% correct (SD = 8); pain management scores were lowest (70%). Overall, interns had a significantly lower mean score than residents (70% vs. 77%; p = 0.001). In multivariate analysis, only the year of residency was significantly associated with knowledge score; prior formal training in palliative care, pain management, or ethics was not. One third of house officers rated themselves as "not at all" or "only slightly" at ease in caring for a dying patient. These self-ratings were not associated with prior training or knowledge, but were higher in residents compared to interns. CONCLUSIONS: Palliative care knowledge and ease with dying patients were higher in later years of residency but were not associated with prior formal palliative care training. These data highlight the continued need to evaluate and improve training in palliative care and pain management.