Technical note: Extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research

PurposeRadiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research. MethodThe radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization. CERR allows for batch calculation and visualization of radiomics features, and provides a user-friendly data structure for radiomics metadata. All radiomics computations are vectorized for speed. Additionally, a test suite is provided for reconstruction and comparison with radiomics features computed using other software platforms such as the Insight Toolkit (ITK) and PyRadiomics. CERR was evaluated according to the standards defined by the Image Biomarker Standardization Initiative. CERR's radiomics feature calculation was integrated with the clinically used MIM software using its MATLAB((R)) application programming interface. ResultsThe CERR provides a comprehensive computational platform for radiomics analysis. Matrix formulations for the compute-intensive Haralick texture resulted in speeds that are superior to the implementation in ITK 4.12. For an image discretized into 32 bins, CERR achieved a speedup of 3.5 times over ITK. The CERR test suite enabled the successful identification of programming errors as well as genuine differences in radiomics definitions and calculations across the software packages tested. ConclusionThe CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses

Automating Intensity Modulated Radiation Therapy Treatment Planning by using Hierarchical Optimization

The intensity modulated radiation therapy (IMRT) optimizes the beam’s intensity to deliver the prescribed dose to the target while minimizing the radiation exposure to normal structures. The IMRT optimization is a complex optimization problem because of the multiple conflicting objectives in it. Due to the complexity of the optimization, the IMRT treatment planning is still a trial and error process. Hierarchical optimization was proposed to automate the treatment planning process, but its potential has not been demonstrated in a clinical setting. Moreover, hierarchical optimization is slower than the traditional optimization. The dissertation studied a sampling algorithm to reduce the hierarchical optimization time, customized an open source optimization solver to solve the nonlinear optimization formulation and demonstrated the potential of hierarchical optimization to automate the treatment planning process in a clinical setting. We generated the treatment plans of 31 prostate patients by hierarchical optimization using the same criteria as used by planners to prepare the treatment plans at Memorial Sloan Kettering Cancer Center. We found that hierarchical optimization produced the same or better treatment plans than that produced by a planner using the Eclipse treatment planning system. Therefore, the dissertation demonstrated that hierarchical optimization could automate the treatment planning process and shift the paradigm of the treatment planning from manual trial and error to an ideal automated process

Integrating visualisation into the systems modelling toolkit: Applications to clinical and health process systems

Many areas of modern society, including healthcare, can be thought of as process systems. Such systems can be understood in terms of a sequence of inputs, work activities and outputs which become difficult to understand and control as a result of complexity arising from interactions between components within these system. Such complexity can hamper the ability to make decisions relating to these process systems. Operations Research/Management Science (OR/MS) has responded to this challenge with the development of systems models, which create a representation of the system under investigation that can be used to help make decisions about the process system. OR/MS literature has developed a wide variety of techniques for systems modelling, including computational, statistical and mathematical approaches to assisting decision-making. This suite of methods can be referred to as the OR/MS toolkit for systems modelling. OR/MS practice often involves the use of visualisation to aid undertanding, interpret results from systems modelling, or otherwise assist OR/MS practitioners in working with systems. This widespread use of visualisation has occurred due to its ability to flexibly and intuitively represent large amounts of information. However, there has been little discussion within OR/MS literature on the use of visualisation as a tool for systems modelling. Likewise, there is a lack of explicit knowledge on how visualisation should be applied in an OR/MS context, and a lack of reported, general knowledge on how to integrate visualisation with conventional OR/MS tools. Given that visualisation provides an intuitive means for understanding complex information, this presents an opportunity for OR/MS experts to better model process systems and provide support for decision-makers by making use of visualisation as a tool for systems modelling. This thesis seeks to advance the use of visualisation in systems modelling by addressing these gaps. We provide a systematic review on the use of visualisation within OR/MS literature, and from this synthesise a series of general visualisation principles to guide beneficial visualisation properties, and a series of general visualisation practices which suggest means of following these principles. We then use the results of this synthesis to develop an original Visual Systems Modelling Framework which provides a method for integrating visualisation into the systems modelling toolkit. This framework draws from established OR/MS theory and practice by explicitly setting out a conceptual model describing the system, the intended purpose of modelling the system, and plans for any conventional OR/MS tools which will be used to model the system. It links these aspects of conventional OR/MS methodology to the visualisation principles identified by the systematic literature review. The thesis provides illustrative case studies of this framework in action by applying it in the development of four models for clinical or health process systems. First, we describe several novel contributions to literature on the conventional OR/MS toolkit that were made in the development of these models. These include a novel dissimilarity measure that can be used to compare and group sequences of ordinal data where sequence length can vary, and systematic validation of a method of clustering survival data using Coxian Phase-Type distributions. These contributions are then used in the case studies which follow the Visual Systems Modelling Framework. The first case study is in the context of the care of Traumatic Spinal Cord Injury (TSCI). The care of TSCI involves several healthcare processes working together in unison to treat a patient who may have many different co-existing injuries resulting from the same event which lead to the spinal cord injury. This heterogeneity among patients results in organisational uncertainty regarding the specific care pathways taken by patients, making it almost impossible to make decisions which will improve care processes for this patient group. Before improvements to this system can be made, a greater understanding of the TSCI healthcare process is required. The Visual Systems Modelling Framework is applied in this system to generate new insights and promote discussion about TSCI healthcare processes. The second case study is in the context of investigating the role of neurorehabilitative physiotherapy in the recovery of stroke survivors. There are open questions within clinical literature on stroke rehabilitation regarding what physiotherapy regimens lead to better patient outcomes. In this application, the Visual Systems Modelling Framework is used to allow clinical researchers the ability to identify patterns in and explore the relationships between physiotherapy regimens and patient health, thereby developing an understanding which can improve the care provided in stroke rehabilitation. The third case study is in the same neurorehabilitative system as the second case study. In this application, the Visual Systems Modelling Framework is used to develop a model which predicts patient outcomes based on their received physiotherapy regimen. This assists clinical researchers seeking to identify what physiotherapy regimens are likely to improve patient outcomes. The final case study is in the context of planning an major clinical trial in stroke rehabilitation which uses an adaptive trial design. Adaptive clinical trials use prespecified rules to adjust the behaviour of the trial based on data as it is accrued by the trial. These rules can be used to address ethical concerns regarding giving patients experimental treatments which appear to be ineffective, enforce balancing of important prognostic factors across treatment arms, or any number of other desired features. However, such designs can lead to complex and counter-intuitive behaviour in the trial, requiring both careful planning and understanding of these behaviours. In this application, the Visual Systems Modelling Framework is used to model this clinical trial to assist planning and communicate the behaviours arising from the adaptive trial design. Through these four case studies, this thesis provides illustrations of how the Visual Systems Modelling Framework can be used across multiple systems, each with their own requirements and modelling purpose. This thesis demonstrates how visualisation can be integrated in the OR/MS systems modelling toolkit, and contributes to OR/MS literature by providing a method for applying visualisation as a systems modelling tool

A collaboratory for radiation therapy treatment planning optimization research

Intensity modulated radiation therapy treatment planning (IMRTP) is a challenging application of optimization technology. We present software tools to facilitate IMRTP research by computational scientists who may not have convenient access to radiotherapy treatment planning systems. The tools, developed within Matlab and CERR (computational environment for radiotherapy research), allow convenient access, visualization, programmable manipulation, and sharing of patient treatment planning data, as well as convenient generation of dosimetric data needed as input for treatment plan optimization research. CERR/Matlab also provides a common framework for storing, reviewing, sharing, and comparing optimized dose distributions from multiple researchers