Towards Optimizing Quality Assurance Outcomes of Knowledge-Based Radiation Therapy Treatment Plans Using Machine Learning

Knowledge-based planning (KBP) techniques have been shown to provide improvements in plan quality, consistency, and efficiency for advanced radiation therapies such as volumetric modulated arc therapy (VMAT). While the potential clinical benefits of KBP methods are generally well known, comparatively less is understood regarding the impact of using these systems on resulting plan complexity and pre-treatment quality assurance (QA) measurements, especially for in-house KBP systems. Therefore, the overarching purpose of this work was to assess QA implications with using an in-house KBP system and explore data-driven methods for mitigating increased plan complexity and QA error rates without compromising dosimetric plan quality. Specifically, this study evaluated differences in dose, complexity, and QA outcomes between reference clinical plans and plans designed with a previously established in-house KBP system. Further, a machine learning model – trained and tested using a database of 500 previous VMAT treatment plans and QA measurements – was developed to predict VMAT QA measurements based on selected mechanical features of the plan. This model was deployed as a feedback mechanism within a heuristic optimization algorithm designed to modify plan parameters (identified by the machine learning model as important for accurately predicting QA outcomes) towards improving the predicted delivery accuracy of the plan. While KBP plans achieved average reductions of 6.4 Gy (p \u3c 0.001) and 8.2 Gy (p \u3c 0.001) in mean bladder and rectum dose compared to reference clinical plans across thirty-one prostate patients, significant (p \u3c 0.05) increases in both complexity and QA measurement errors were observed. A support vector machine (SVM) was developed – using a database of 500 previous VMAT plans – to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A QA-based optimization algorithm was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. The feasibility was evaluated on 13 prostate VMAT plans designed with an in-house KBP method. Using a maximum random leaf gap displacement setting of 3 mm, predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with minimal differences in dose and radiobiological metrics

Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy

Objectives: Radiotherapy plan quality may vary considerably depending on planner's experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at- risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics. Methods: Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement. Results: The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements. Conclusion: Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models. Advances in knowledge: In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation

Database Plan Quality Impact on Knowledge-based Radiation Therapy Treatment Planning of Prostate Cancer

Purpose: Knowledge-based planning (KBP) leverages plan data from a database of previously treated patients to inform the plan design of a new patient. This work investigated bladder and rectum dose-volume prediction improvements in a common KBP method using a Pareto plan database in VMAT planning for prostate cancer. Methods: We formed an anonymized retrospective patient database of 124 VMAT plans for prostate cancer treated at our institution. From these patient data, two plan databases were compiled. The clinical plan database (CPD) contained planning data from each patient’s clinical plan, which were manually optimized by various planners. The multi-criteria optimization database (MCOD) contained Pareto plan data from plans created using a standardized MCO protocol. Overlap volume histograms, incorporating fractional OAR volumes only within the treatment fields, were computed for each patient and used to match new patient anatomy to similar database patients. For each database patient, CPD and MCOD KBP predictions were generated for D_10, D_30, D_50, D_65, and D_80 of the bladder and rectum in a leave-one-out manner. Prediction achievability was verified through a re-planning study on a subset of 31 randomly selected database patients using the lowest KBP predictions, regardless of plan database origin, as planning goals. Results: MCOD model predictions were significantly lower (p \u3c 0.001) than CPD model predictions for all five bladder dose-volumes and rectum D_50 (p = 0.004) and D_65 (p \u3c 0.001), while CPD model predictions for rectum D_10 (p = 0.005) and D_30 (p \u3c 0.001) were significantly less than MCOD model predictions. KBP model predictions were statistically equivalent to re-planned values for all predicted dose-volumes, excluding D_10 of bladder (p = 0.03) and rectum (p = 0.04). Compared to clinical plans, re-plans showed significant average reductions in D_mean for bladder (7.8 Gy; p \u3c 0.001) and rectum (9.4 Gy; p \u3c 0.001), while maintaining statistically similar PTV, femoral head, and penile bulb dose. Conclusion: KBP dose-volume predictions derived from Pareto plans were lower overall than those resulting from manually optimized clinical plans. A re-planning study showed the KBP dose-volume predictions were achievable and led to significant reductions in bladder and rectum dose

Optimization Problems in Radiation Therapy Treatment Planning.

Radiation therapy is one of the most common methods used to treat many types of cancer. External beam radiation therapy and the models associated with developing a treatment plan for a patient are studied. External beams of radiation are used to deliver a highly complex so-called dose distribution to a patient that is designed to kill the cancer cells while sparing healthy organs and normal tissue. Treatment planning models and optimization are used to determine the delivery machine instructions necessary to produce a desirable dose distribution. These instructions make up a treatment plan. This thesis studies four problems in radiation therapy treatment plan optimization. First, treatment planners generate a plan with a number of competing treatment plan criteria. The relationship between criteria is not known a priori. A methodology is developed for physicians and treatment planners to efficiently navigate a clinically relevant region of the Pareto frontier generated by trading off these different criteria in an informed way. Second, the machine instructions for intensity modulated radiation therapy, a common treatment modality, consist of the locations of the external beams and the non-uniform intensity profiles delivered from each of these locations. These decisions are traditionally made with separate, sequential models. These decisions are integrated into a single model and propose a heuristic solution methodology. Third, volumetric modulated arc therapy (VMAT), a treatment modality where the beam travels in a coplanar arc around the patient while continuously delivering radiation, is a popular topic among optimizers studying treatment planning due to the difficult nature of the problem and the lack of a universally accepted treatment planning method. While current solution methodologies assume a predetermined coplanar path around the patient, that assumption is relaxed and the generation of a non-coplanar path is integrated into a VMAT planning algorithm. Fourth, not all patient information is available when developing a treatment plan pre-treatment. Some information, like a patient's sensitivity to radiation, can be realized during treatment through physiological tests. Methodologies of pre-treatment planning considering adaptation to new information are studied.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113366/1/troylong_1.pd

Column Generation-Based Techniques for Intensity-Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) Treatment Planning

RÉSUMÉ: Les statistiques ont estimé à environ 14,1 millions le nombre de cas de cancer en 2018 dans le monde, et qui devrait passer à 24 millions d’ici 2035. La radiothérapie est l’une des premières méthodes de traitement du cancer, qu’environ 50% des patients reçoivent au cours de leur maladie. Cette méthode endommage le matériel génétique des cellules cancéreuses, détruisant ainsi leur capacité de reproduction. Cependant, les cellules normales sont également affectées par le rayonnement ; par conséquent, le traitement doit être effectué de manière à maximiser la dose de rayonnement aux tumeurs, tout en minimisant les effets néfastes des radiations sur les tissus sains. Les techniques d’optimisation sont utilisées afin de déterminer la dose et la position du rayonnement à administrer au corps du patient. Ce projet aborde la radiothérapie externe à travers la radiothérapie par modulation d’intensité (IMRT), ainsi qu’une nouvelle forme appelée modulation d’intensité volumétrique par thérapie par arcs (VMAT). En IMRT, un nombre fini de directions sont déterminées pour le rayonnement du faisceau, tandis qu’en VMAT l’accélérateur linéaire tourne autour du corps du patient alors que le faisceau est allumé. Cette technologie permet de modifier dynamiquement la forme du faisceau et le débit de dose pendant le traitement. Le problème de planification du traitement consiste à choisir une séquence de distribution des formes de faisceaux, à optimiser le dé bit de dose du faisceau et à déterminer la vitesse de rotation du portique, si nécessaire. Cette recherche tire profit de la méthode de génération de colonnes, en tant que méthode d’optimisation efficace en particulier pour les problèmes à grande échelle. Cette technique permet d’améliorer le temps de traitement et les objectifs cliniques non linéaires et non convexes, dans la planification de traitement en VMAT. Un nouveau modèle multi-objectif de génération de colonnes pour l’IMRT est également développé. Dans le premier essai, nous développons un nouvel algorithme de génération de colonnes qui optimise le compromis entre le temps et la qualité du traitement délivré pour la planification de traitement en VMAT. Pour ce faire, une génération simultanée de colonnes et de rangées est développée, afin de relier les colonnes, contenant la configuration des ouvertures de faisceaux, aux rangées du modèle, représentant la restriction de temps de traitement. De plus, nous proposons une technique de regroupement par grappe modifiée, afin d’agréger des éléments de volume similaires du corps du patient, et de réduire efficacement le nombre de contraintes dans le modèle. Les résultats de calcul montrent qu’il est possible d’obtenir un traitement de haute qualité sur quatre processeurs en parallèle. Dans le deuxième essai, nous développons une approche de planification automatique intégrant les critères de l’histogramme dose-volume (DVH). Les DVH sont la représentation de dose la plus courante pour l’évaluation de la qualité de traitement en technologie VMAT. Nous profitons de la procédure itérative de génération de colonnes pour ajuster les paramètres du modèle lors de la génération d’ouverture, et répondre aux critères DVH non linéaires, sans tenir compte des contraintes dures dans le modèle. Les résultats sur les cas cliniques montrent que notre méthodologie a été significativement améliorée, pour obtenir des plans cliniquement acceptables sans intervention humaine par rapport à une simple optimisation VMAT. De plus, la comparaison avec un système de planification de traitement commercial existant montre que la qualité des plans obtenus à partir de la méthode proposée, en particulier pour les tissus sains, est largement meilleure alors que le temps de calcul est moindre. Dans le troisième essai, nous abordons la planification de traitement en IMRT, qui est formulée comme un problème d’optimisation convexe à grande échelle, avec un espace de faisabilité simplex. Nous intégrons d’abord une nouvelle approche de solution basée sur la méthode Frank-Wolfe, appelée Blended Conditional Gradients, dans la génération de colonnes, pour améliorer les performances de calcul de la méthode. Nous proposons ensuite une technique de génération de colonnes multi-objectif, pour obtenir directement des ouvertures qui se rapprochent d’un ensemble efficace de plans de traitement non dominés. A cette fin, nous trouvons les limites inférieure et supérieure du front de Pareto, et générons une colonne avec un vecteur de poids des objectifs pré-assigné ou nouveau, réduisant la distance maximale de deux bornes. Nous prouvons que cet algorithme converge vers le front de Pareto. Les résultats de recherche d’un bon compromis de traitement entre la destruction des volumes cibles et la protection des structures saines dans un espace objectif bidimensionnel, montrent l’efficacité de l’algorithme dans l’approche du front de Pareto, avec des plans de traitement livrables en 3 minutes environ, et évitant un grand nombre de colonnes. Cette méthode s’applique également à d’autres classes de problèmes d’optimisation convexe, faisant appel à la fois à une génération de colonnes et à une optimisation multi-objectifs.----------ABSTRACT: The statistics have estimated about 18.1 million cancer cases in 2018 around the world, which is expected to increase to 24 million by 2035. Radiation therapy is one of the most important cancer treatment methods, which about 50% of patients receive during their illness. This method works by damaging the genetic material within cancerous cells and destroying their ability to reproduce. However, normal cells are also affected by radiation; therefore, the treatment should be performed in such a way that it maximizes the dose of radiation to tumors, while simultaneously minimizing the adverse effects of radiations to healthy tissues. The optimization techniques are useful to determine where and how much radiation should be delivered to patient’s body. In this project, we address the intensity-modulated radiation therapy (IMRT) as a widelyused external radiotherapy method and also a novel form called volumetric modulated arc therapy (VMAT). In IMRT, a finite number of directions are determined for the beam radiation, while in VMAT, the linear accelerator rotates around the patient’s body while the beam is on. These technologies give us the ability of changing the beam shape and the dose rate dynamically during the treatment. The treatment planning problem consists of selecting a delivery sequence of beam shapes, optimizing the dose rate of the beam, and determining the rotation speed of the gantry, if required. In this research, we take advantages of the column generation technique, as a leading optimization method specifically for large-scale problems, to improve the treatment time and non-linear non-convex clinical objectives in VMAT treatment planning, and also develop a new multi-objective column generation framework for IMRT. In the first essay, we develop a novel column generation algorithm optimizing the trade-off between delivery time and treatment quality for VMAT treatment planning. To this end, simultaneous column-and-row generation is developed to relate the configuration of beam apertures in columns to the treatment time restriction in the rows of the model. Moreover, we propose a modified clustering technique to aggregate similar volume elements of the patient’s body and efficiently reduce the number of constraints in the model. The computational results show that a high-quality treatment is achievable using a four-thread CPU. In the second essay, we develop an automatic planning approach integrating dose-volume histogram (DVH) criteria, the most common method of treatment evaluation in practice, for VMAT treatment planning. We take advantage of the iterative procedure of column generation to adjust the model parameters during aperture generation and meet nonlinear DVH criteria without considering hard constraints in the model. The results on clinical cases show that our methodology had significant improvement to obtain clinically acceptable plans without human intervention in comparison to simple VMAT optimization. In addition, the comparison to an existing commercial treatment planning system shows the quality of the obtained plans from the proposed method, especially for the healthy tissues, is significantly better while the computational time is less. In the third essay, we address the IMRT treatment planning, which is formulated as a large scale convex optimization problem with simplex feasibility space. We first integrate a novel Frank-Wolfe-based solution approach, so-called Blended Conditional Gradients, into the column generation to improve the computational performance for the method. We then propose a multi-objective column generation technique to directly obtain apertures that approximate an efficient non-dominated set of treatment plans. To this end, we find lower and upper bounds for the Pareto front and generate a column with a pre-assigned or new weight-vector of the objectives, reducing the maximum distance of two bounds. We prove this algorithm converges to the Pareto front. The results in a two-dimensional objective space to find the trade-off plans between the treat of target volumes and sparing the healthy structures show the efficiency of the algorithm to approximate the Pareto front with deliverable treatment plans in about 3 minutes, avoiding a large number of columns. This method is also applicable for other classes of convex optimization problems requiring both column generation and multi-objective optimization

Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning

Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime

DEVELOPMENT OF A ROBUST LINAC-BASED RADIOSURGERY PROGRAM FOR MULTIPLE BRAIN METASTASES & ESTIMATION THE RADIOBIOLOGICAL RESPONSE OF INDIRECT CELL KILL

Accurate and precise delivery of Stereotactic Radiosurgery (SRS) using Gamma Knife (GK) unit by Leksell is a gold standard for multiple intracranial lesions. SRS provides less brain toxicity compared to whole brain radiotherapy techniques historically used. However, these treatments are limited in availability and are accompanied by long treatment times with painful, intolerable headframe fixation. With advancements in linear accelerator (Linac) based SRS, multiple brain lesions can be treated separately with individual isocenters or, more recently, altogether with a single isocenter multi-target (SIMT) volumetric modulated arc therapy (VMAT) technique. SIMT methods reduce the challenges of treating patients with GK by significantly decreasing treatment times, improving patient comfort and clinic workflow. This dissertation explores the usability of SIMT VMAT and presents potential solutions to the challenges of treating multiple brain lesions using Linac-based SRS. Treating multiple brain lesions simultaneously with a SIMT VMAT plan is an efficient treatment option for SRS; however, it does not account for patient setup uncertainty, which degrades treatment delivery accuracy. This dissertation quantifies the loss of target coverage by simulating patient setup errors that would be seen on daily cone beam CT imaging during patient set up and verification. These simulations resulted in dosimetric discrepancies up to 70% (average, 30%), providing suboptimal SRS treatments. It was also found that small tumors were more susceptible to these setup uncertainties and would experience greater losses of target coverage. This means SIMT-VMAT, in its current use, is not an accurate SRS treatment modality for brain metastases. This dissertation aims to provide potential solutions to minimize these spatial uncertainties discussed. First, a novel risk-adapted correction strategy was explored where dose is escalated for small targets at a large distance from the isocenter. These treatments with up to ±1o/1 mm set up errors in all 6-directions demonstrated promising plan quality and treatment delivery accuracy with less spread of intermediate dose to the normal brain. Second, a dual isocenter planning strategy that groups lesions based on brain hemisphere location was proposed. These plans provided similar target coverage and dose conformity as compared to the SIMT plans with less low and intermediate dose to the brain and less dose to surrounding critical organs. These techniques could potentially improve target localization accuracy and be delivered within a standard treatment slot. Though these SIMT VMAT treatments for multiple brain metastases could be at risk of detrimental spatial uncertainties, recent clinical outcome studies suggest high rates of tumor local-control and positive treatment outcomes. In this dissertation, this is explained through a combination of both direct and indirect cell kill. A single dose of 15 Gy or more will cause damage to the weak cellular vasculature of the brain tumors, ultimately resulting in secondary cell death. By inducing clinically observable systematic set up errors, the role of secondary cell death is modeled to define the relationship between achieving required target coverage and spatial uncertainty. For 20 Gy prescription, it was found that patient set up errors of 1.3 mm/1.3°in all 6-directions must be maintained in order to achieve a target dose of 15 Gy or higher with no additional brain toxicity. At this range of uncertainty, devascularization would occur resulting in positive tumor local control, providing guidance to treating physicians for clinically acceptable patient setup errors and perhaps resulting acceptable treatment outcomes. A prospective clinical trial is necessary to further validate this radiobiological model, incorporating secondary cell death with direct cell kill using a single-isocenter VMAT plan for multiple brain lesions

Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

BACKGROUND: Planning radiosurgery to multiple intracranial metastases is complex and shows large variability in dosimetric quality among planners and treatment planning systems (TPS). This project aimed to determine whether autoplanning using the Muliple Brain Mets (AutoMBM) software can improve plan quality and reduce inter-planner variability by crowdsourcing results from prior international planning study. METHODS: Twenty-four institutions autoplanned with AutoMBM on a five metastases case from a prior international planning competition from which population statistics (means and variances) of 23 dosimetric metrics and resulting composite plan score (maximum score = 150) of other TPS (Eclipse, Monaco, RayStation, iPlan, GammaPlan, MultiPlan) were crowdsourced. Plan results of AutoMBM and each of the other TPS were compared using two sample t-tests for means and Levene's tests for variances. Plan quality of AutoMBM was correlated with the planner' experience and compared between academic and non-academic centers. RESULTS: AutoMBM produced plans with comparable composite plan score to GammaPlan, MultiPlan, Eclipse and iPlan (127.6 vs. 131.7 vs. 127.3 vs. 127.3 and 126.7; all p > 0.05) and superior to Monaco and RayStation (118.3 and 108.6; both p 0.05). CONCLUSIONS: By plan crowdsourcing prior international plan challenge, AutoMBM produces high and consistent plan quality independent of the planning experience and the institution that is crucial to addressing the technical bottleneck of SRS to intracranial metastases