A case-based reasoning system for radiotherapy treatment planning for brain cancer

In this thesis, a novel case-based reasoning (CBR) approach to radiotherapy treatment planning for brain cancer patients is presented. In radiotherapy, tumour cells are destroyed using ionizing radiation. For each patient, a treatment plan is generated that describes how the radiation should be applied in order to deliver a tumouricidal radiation dose while avoiding irradiation of healthy tissue and organs at risk in the vicinity of the tumour. The traditional, manual trial and error approach is a time-consuming process that depends on the experience and intuitive knowledge of medical physicists. CBR is an artificial intelligence methodology, which attempts to solve new problems based on the solutions of previously solved similar problems. In this research work, CBR is used to generate the parameters of a treatment plan by capturing the subjective and intuitive knowledge of expert medical physicists stored intrinsically in the treatment plans of similar patients treated in the past. This work focusses on the retrieval stage of the CBR system, in which given a new patient case, the most similar case in the archived case base is retrieved along with its treatment plan. A number of research issues that arise from using CBR for radiotherapy treatment planning for brain cancer are addressed. Different approaches to similarity calculation between cases are investigated and compared, in particular, the weighted nearest neighbour similarity measure and a novel non-linear, fuzzy similarity measure designed for our CBR system. A local case attribute weighting scheme has been developed that uses rules to assign attribute weights based on the values of the attributes in the new case and is compared to global attribute weighting, where the attribute weights remain constant for all target cases. A multi-phase case retrieval approach is introduced in which each phase considers one part of the solution. In addition, a framework developed for the imputation of missing values in the case base is described. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The performance of the developed methodologies was tested using brain cancer patient cases obtained from the City Hospital. The results obtained show that the success rate of the retrieval mechanism provides a good starting point for adaptation, the next phase in development for the CBR system. The developed automated CBR system will assist medical physicists in quickly generating treatment plans and can also serve as a teaching and training aid for junior, inexperienced medical physicists. In addition, the developed methods are generic in nature and can be adapted to be used in other CBR or intelligent decision support systems for other complex, real world, problem domains that highly depend on subjective and intuitive knowledge

Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning

Objective: Radiotherapy treatment planning aims at delivering a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour-surrounding area. It is a time-consuming trial-and-error process that requires the expertise of a group of medical experts including oncologists and medical physicists and can take from 2 to 3 h to a few days. Our objective is to improve the performance of our previously built case-based reasoning (CBR) system for brain tumour radiotherapy treatment planning. In this system, a treatment plan for a new patient is retrieved from a case base containing patient cases treated in the past and their treatment plans. However, this system does not perform any adaptation, which is needed to account for any difference between the new and retrieved cases. Generally, the adaptation phase is considered to be intrinsically knowledge-intensive and domain-dependent. Therefore, an adaptation often requires a large amount of domain-specific knowledge, which can be difficult to acquire and often is not readily available. In this study, we investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation. Methodology: We developed two adaptation approaches: adaptation based on machine-learning tools and adaptation-guided retrieval. They were used to adapt the beam number and beam angles suggested in the retrieved case. Two machine-learning tools, neural networks and naive Bayes classifier, were used in the adaptation to learn how the difference in attribute values between the retrieved and new cases affects the output of these two cases. The adaptation-guided retrieval takes into consideration not only the similarity between the new and retrieved cases, but also how to adapt the retrieved case. Results: The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. All experiments were performed using real-world brain cancer patient cases treated with three-dimensional (3D)-conformal radiotherapy. Neural networks-based adaptation improved the success rate of the CBR system with no adaptation by 12%. However, naive Bayes classifier did not improve the current retrieval results as it did not consider the interplay among attributes. The adaptation-guided retrieval of the case for beam number improved the success rate of the CBR system by 29%. However, it did not demonstrate good performance for the beam angle adaptation. Its success rate was 29% versus 39% when no adaptation was performed. Conclusions: The obtained empirical results demonstrate that the proposed adaptation methods improve the performance of the existing CBR system in recommending the number of beams to use. However, we also conclude that to be effective, the proposed adaptation of beam angles requires a large number of relevant cases in the case base

A case-based reasoning system for radiotherapy treatment planning for brain cancer

In this thesis, a novel case-based reasoning (CBR) approach to radiotherapy treatment planning for brain cancer patients is presented. In radiotherapy, tumour cells are destroyed using ionizing radiation. For each patient, a treatment plan is generated that describes how the radiation should be applied in order to deliver a tumouricidal radiation dose while avoiding irradiation of healthy tissue and organs at risk in the vicinity of the tumour. The traditional, manual trial and error approach is a time-consuming process that depends on the experience and intuitive knowledge of medical physicists. CBR is an artificial intelligence methodology, which attempts to solve new problems based on the solutions of previously solved similar problems. In this research work, CBR is used to generate the parameters of a treatment plan by capturing the subjective and intuitive knowledge of expert medical physicists stored intrinsically in the treatment plans of similar patients treated in the past. This work focusses on the retrieval stage of the CBR system, in which given a new patient case, the most similar case in the archived case base is retrieved along with its treatment plan. A number of research issues that arise from using CBR for radiotherapy treatment planning for brain cancer are addressed. Different approaches to similarity calculation between cases are investigated and compared, in particular, the weighted nearest neighbour similarity measure and a novel non-linear, fuzzy similarity measure designed for our CBR system. A local case attribute weighting scheme has been developed that uses rules to assign attribute weights based on the values of the attributes in the new case and is compared to global attribute weighting, where the attribute weights remain constant for all target cases. A multi-phase case retrieval approach is introduced in which each phase considers one part of the solution. In addition, a framework developed for the imputation of missing values in the case base is described. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The performance of the developed methodologies was tested using brain cancer patient cases obtained from the City Hospital. The results obtained show that the success rate of the retrieval mechanism provides a good starting point for adaptation, the next phase in development for the CBR system. The developed automated CBR system will assist medical physicists in quickly generating treatment plans and can also serve as a teaching and training aid for junior, inexperienced medical physicists. In addition, the developed methods are generic in nature and can be adapted to be used in other CBR or intelligent decision support systems for other complex, real world, problem domains that highly depend on subjective and intuitive knowledge

Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing

Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts

Motion monitoring during prostate radiation therapy treatment: clinical considerations, and patient preferences and perspectives

Amy Brown investigated motion monitoring in radiation therapy, a primary treatment for prostrate cancer. She investigated clinical and patient-reported outcomes with reduced margins afforded by motion monitoring; patient perceptions and preferences for motion monitoring, and identified future preference research areas. This research has led to practice change at Townsville Cancer Centre

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Mammography Techniques and Review

Mammography remains at the backbone of medical tools to examine the human breast. The early detection of breast cancer typically uses adjunct tests to mammogram such as ultrasound, positron emission mammography, electrical impedance, Computer-aided detection systems and others. In the present digital era it is even more important to use the best new techniques and systems available to improve the correct diagnosis and to prevent mortality from breast cancer. The first part of this book deals with the electrical impedance mammographic scheme, ultrasound axillary imaging, position emission mammography and digital mammogram enhancement. A detailed consideration of CBR CAD System and the availability of mammographs in Brazil forms the second part of this book. With the up-to-date papers from world experts, this book will be invaluable to anyone who studies the field of mammography