A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT

Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations. The MOEA improvements were evaluated for example prostate cases with one target and two OARs. The modified MOEA dominated 11.3% of plans using a standard genetic algorithm package. By implementing domination advantage and protocol objectives, small diverse populations of clinically acceptable plans that were only dominated 0.2% by the Pareto front could be generated in a fraction of an hour. Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. It optimizes not only beamlet intensities but also objective function parameters on a patient-specific basis

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Quantum Inspired Machine Learning Algorithms for Adaptive Radiotherapy

Adaptive radiotherapy (ART) refers to the modification of radiotherapy treatment plans in response to patient anatomical and physiological changes over the course of treatment and has been recognized as an important step towards maximizing the curative potential of radiation therapy through personalized medicine. This dissertation explores the novel application of quantum physics principles and deep machine learning techniques to address three challenges towards the clinical implementation of ART: (1) efficient calculation of optimal treatment parameters, (2) adaptation to geometrical changes over the treatment period while mitigating associated uncertainties, and (3) understanding the relationship between individual patient characteristics and clinical outcomes. Applications of quantum and machine learning modeling in other fields support the potential of this novel approach. For efficient optimization, we developed and tested a quantum-inspired, stochastic algorithm for intensity-modulated radiotherapy: quantum tunnel annealing (QTA). By modeling the likelihood probability of accepting a higher energy solution after a particle tunneling through a potential energy barrier, QTA features an additional degree of freedom not shared by traditional stochastic optimization methods such as simulated annealing (SA). QTA achieved convergence up to 46.6% (26.8%) faster than SA for beamlet weight optimization and direct aperture optimization respectively. The results of this study suggest that the additional degree of freedom provided by QTA can improve convergence rates and achieve a more efficient and, potentially, effective treatment planning process. For geometrical adaptation, we investigated the feasibility of predicting patient changes across a fractionated treatment schedule using two approaches. The first was based on a joint framework (referred to as QRNN) employing quantum mechanics in combination with deep recurrent neural networks (RNNs). The second approach was developed based on a classical framework (MRNN), which modelled patient anatomical changes as a Markov process. We evaluated and compared these two approaches’ performance characteristics using a dataset of 125 head and neck cancer patients who received fractionated radiotherapy. The MRNN framework exhibited slightly better performance than the QRNN framework, with MRNN(QRNN) validation area under the receiver operating characteristic curve (AUC) scores [95% CI] of 0.742 [0.721-0.763] (0.675 [0.64-0.71]), 0.709 [0.683-0.735] (0.656 [0.634-0.677]), 0.724 [0.688-0.76] (0.652 [0.608-0.696]), and 0.698 [0.682-0.714] (0.605 [0.57-0.64]) for system state vector sizes of 4, 6, 8, and 10, respectively. A similar trend was also observed when the fully trained models were applied to an external testing dataset of 20 patients. These results suggest that these stochastic models provide added value in predicting patient changes during the course of adaptive radiotherapy. Towards understanding the relationship between patient characteristics and clinical outcomes, we performed a series of studies which investigated the use of quantitative patient features for predicting clinical outcomes in laryngeal cancer patients who underwent treatment in a bioselection paradigm based on surgeon-assessed response to induction chemotherapy. Among the features investigated from CT scans taken before and after induction chemotherapy, two (gross tumor volume change between pre- and post-induction chemotherapy, and nodal stage) had prognostic value for predicting patient outcomes using standard regression models. Artificial neural networks did not improve predictive performance in this case. Taken together, the significance of these studies lies in their contribution to the body of knowledge of medical physics and in their demonstration of the use of novel techniques which incorporate quantum mechanics and machine learning as a joint framework for treatment planning optimization and prediction of anatomical patient changes over time.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169954/1/jpakela_1.pd

A radiobiological Markov model for aiding decision making in proton therapy referral

Cancer is a highly prevalent disease that places a significant economic burden upon society. Radiotherapy is commonly utilised as a treatment for benign and malignant tumours. A fundamental challenge in radiotherapy is delivering a sufficient dose of radiation to eradicate a tumour while minimizing the dose deposited in surrounding healthy tissue. Excessive radiation damage to these tissues can result in treatment toxicities that may have adverse effects on patient quality of life. Proton therapy offers the potential for increased sparing of normal tissue compared with X-ray therapy, which is more commonly used in radiotherapy. However, the degree of this sparing can be highly variable between patients. Furthermore, data from Phase III clinical trials can quickly become outdated due to the long follow-up times that are required to observe late effects, together with the rapid evolution of technology. The process of deciding whether to refer a patient for proton therapy can be complex as a result. In addition, proton therapy is significantly more expensive as a treatment compared with Xray therapy. This suggests that patients who are expected to receive the greatest benefit should be prioritised. Computer models can offer a possible solution to this dilemma, by predicting the clinical outcome that may be expected as a result of a given treatment. In this work, a Markov simulation tool was developed which is capable of producing such predictions and comparing proton and X-ray radiotherapy treatment plans on an individual patient basis. The radiobiological effect of a given treatment plan is estimated in terms of the probabilities of tumour control, radiation-induced injuries and radiation-induced second cancers. These are combined in the Markov model to efficiently estimate the clinical outcome resulting from a given treatment plan. This outcome is quantified in terms of the quality adjusted life expectancy (QALE), or number of quality adjusted life years (QALYs), which is an adjustment of the raw life expectancy to account for the effect of time spent with injury or disease. The result is a model that uses several input parameters to produce a single quantitative output, indicative of the relative quality of a treatment plan. The predictions of the model can be affected by uncertainties in the radiobiological model parameters and uncertainties in dose delivery. The latter can arise as a result of changes in the target volume relative to the radiation field over the course of treatment. A consideration of these effects was incorporated into the model, as they have the potential to influence whether a patient is selected to receive proton therapy. The cost-effectiveness of a treatment is of particular importance in the current resource limited healthcare environment. The Markov model was developed to include treatment costs, including treatment of radiation therapy side effects. An application of the model to a cohort of base of skull chordoma patients revealed that all patients could be treated with proton therapy cost-effectively due to the potential for sparing of critical structures. Base of skull chordoma is typically regarded as a standard indication for proton therapy. In contrast, in a study of a cohort of left-sided breast cancer patients, it was found that the majority of patients could not be treated cost-effectively with proton therapy. This was likely due to the cardiac toxicity rate being particularly low with the deep inspiration breath hold X-ray treatment technique used for the patients in this cohort, resulting in no significant advantage from proton therapy. The developed model has the potential to form the basis of a clinically viable patient selection tool. However, the model requires external validation before being suitable for clinical implementation. Due to the limited availability of proton therapy, such a model may prove useful as Australia prepares to begin treating cancer patients with proton therapy.Thesis (Ph.D.) -- University of Adelaide, School of Physical Sciences, 201

Optimising outcomes for potentially resectable pancreatic cancer through personalised predictive medicine : the application of complexity theory to probabilistic statistical modeling

Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models.Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models