2,898 research outputs found

    A novel case-based reasoning approach to radiotherapy dose planning

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    In this thesis, novel Case-Based Reasoning (CBR) methods were developed to be included in CBRDP (Case-Based Reasoning Dose Planner) -an adaptive decision support system for radiotherapy dose planning. CBR is an artificial intelligence methodology which solves new problems by retrieving solutions to previously solved similar problems stored in a case base. The focus of this research is on dose planning for prostate cancer patients. The records of patients successfully treated in the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK, were stored in a case base and were exploited using case-based reasoning for future decision making. After each successful run of the system, a group based Simulated Annealing (SA) algorithm automatically searches for an optimal/near optimal combination of feature weights to be used in the future retrieval process of CBR. A number of research issues associated with the prostate cancer dose planning problem and the use of CBR are addressed including: (a) trade-off between the benefit of delivering a higher dose of radiation to cancer cells and the risk to damage surrounding organs, (b) deciding when and how much to violate the limitations of dose limits imposed to surrounding organs, (c) fusion of knowledge and experience gained over time in treating patients similar to the new one, (d) incorporation of the 5 years Progression Free Probability and success rate in the decision making process and (e) hybridisation of CBR with a novel group based simulated annealing algorithm to update knowledge/experience gained in treating patients over time. The efficiency of the proposed system was validated using real data sets collected from the Nottingham University Hospitals. Experiments based on a leave-one-out strategy demonstrated that for most of the patients, the dose plans generated by our approach are coherent with the dose plans prescribed by an experienced oncologist or even better. This system may play a vital role to assist the oncologist in making a better decision in less time; it incorporates the success rate of previously treated similar patients in the dose planning for a new patient and it can also be used in teaching and training processes. In addition, the developed method is generic in nature and can be used to solve similar non-linear real world complex problems

    A novel case-based reasoning approach to radiotherapy dose planning

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    In this thesis, novel Case-Based Reasoning (CBR) methods were developed to be included in CBRDP (Case-Based Reasoning Dose Planner) -an adaptive decision support system for radiotherapy dose planning. CBR is an artificial intelligence methodology which solves new problems by retrieving solutions to previously solved similar problems stored in a case base. The focus of this research is on dose planning for prostate cancer patients. The records of patients successfully treated in the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK, were stored in a case base and were exploited using case-based reasoning for future decision making. After each successful run of the system, a group based Simulated Annealing (SA) algorithm automatically searches for an optimal/near optimal combination of feature weights to be used in the future retrieval process of CBR. A number of research issues associated with the prostate cancer dose planning problem and the use of CBR are addressed including: (a) trade-off between the benefit of delivering a higher dose of radiation to cancer cells and the risk to damage surrounding organs, (b) deciding when and how much to violate the limitations of dose limits imposed to surrounding organs, (c) fusion of knowledge and experience gained over time in treating patients similar to the new one, (d) incorporation of the 5 years Progression Free Probability and success rate in the decision making process and (e) hybridisation of CBR with a novel group based simulated annealing algorithm to update knowledge/experience gained in treating patients over time. The efficiency of the proposed system was validated using real data sets collected from the Nottingham University Hospitals. Experiments based on a leave-one-out strategy demonstrated that for most of the patients, the dose plans generated by our approach are coherent with the dose plans prescribed by an experienced oncologist or even better. This system may play a vital role to assist the oncologist in making a better decision in less time; it incorporates the success rate of previously treated similar patients in the dose planning for a new patient and it can also be used in teaching and training processes. In addition, the developed method is generic in nature and can be used to solve similar non-linear real world complex problems

    A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

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    Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

    A novel combination of Cased-Based Reasoning and Multi Criteria Decision Making approach to radiotherapy dose planning

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    In this thesis, a set of novel approaches has been developed by integration of Cased-Based Reasoning (CBR) and Multi-Criteria Decision Making (MCDM) techniques. Its purpose is to design a support system to assist oncologists with decision making about the dose planning for radiotherapy treatment with a focus on radiotherapy for prostate cancer. CBR, an artificial intelligence approach, is a general paradigm to reasoning from past experiences. It retrieves previous cases similar to a new case and exploits the successful past solutions to provide a suggested solution for the new case. The case pool used in this research is a dataset consisting of features and details related to successfully treated patients in Nottingham University Hospital. In a typical run of prostate cancer radiotherapy simple CBR, a new case is selected and thereafter based on the features available at our data set the most similar case to the new case is obtained and its solution is prescribed to the new case. However, there are a number of deficiencies associated with this approach. Firstly, in a real-life scenario, the medical team considers multiple factors rather than just the similarity between two cases and not always the most similar case provides with the most appropriate solution. Thus, in this thesis, the cases with high similarity to a new case have been evaluated with the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach takes into account multiple criteria besides similarity to prescribe a final solution. Moreover, the obtained dose plans were optimised through a Goal Programming mathematical model to improve the results. By incorporating oncologists’ experiences about violating the conventionally available dose limits a system was devised to manage the trade-off between treatment risk for sensitive organs and necessary actions to effectively eradicate cancer cells. Additionally, the success rate of the treatment, the 2-years cancer free possibility, has a vital role in the efficiency of the prescribed solutions. To consider the success rate, as well as uncertainty involved in human judgment about the values of different features of radiotherapy Data Envelopment Analysis (DEA) based on grey numbers, was used to assess the efficiency of different treatment plans on an input and output based approach. In order to deal with limitations involved in DEA regarding the number of inputs and outputs, we presented an approach for Factor Analysis based on Principal Components to utilize the grey numbers. Finally, to improve the CBR base of the system, we applied Grey Relational Analysis and Gaussian distant based CBR along with features weight selection through Genetic Algorithm to better handle the non-linearity exists within the problem features and the high number of features. Finally, the efficiency of each system has been validated through leave-one-out strategy and the real dataset. The results demonstrated the efficiency of the proposed approaches and capability of the system to assist the medical planning team. Furthermore, the integrated approaches developed within this thesis can be also applied to solve other real-life problems in various domains other than healthcare such as supply chain management, manufacturing, business success prediction and performance evaluation

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

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    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

    Intensity modulated radiation therapy and arc therapy: validation and evolution as applied to tumours of the head and neck, abdominal and pelvic regions

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    Intensiteitsgemoduleerde radiotherapie (IMRT) laat een betere controle over de dosisdistributie (DD) toe dan meer conventionele bestralingstechnieken. Zo is het met IMRT mogelijk om concave DDs te bereiken en om de risico-organen conformeel uit te sparen. IMRT werd in het UZG klinisch toegepast voor een hele waaier van tumorlocalisaties. De toepassing van IMRT voor de bestraling van hoofd- en halstumoren (HHT) vormt het onderwerp van het eerste deel van deze thesis. De planningsstrategie voor herbestralingen en bestraling van HHT, uitgaande van de keel en de mondholte wordt beschreven, evenals de eerste klinische resultaten hiervan. IMRT voor tumoren van de neus(bij)holten leidt tot minstens even goede lokale controle (LC) en overleving als conventionele bestralingstechnieken, en dit zonder stralingsgeïnduceerde blindheid. IMRT leidt dus tot een gunstiger toxiciteitprofiel maar heeft nog geen bewijs kunnen leveren van een gunstig effect op LC of overleving. De meeste hervallen van HHT worden gezien in het gebied dat tot een hoge dosis bestraald werd, wat erop wijst dat deze “hoge dosis” niet volstaat om alle clonogene tumorcellen uit te schakelen. We startten een studie op, om de mogelijkheid van dosisescalatie op geleide van biologische beeldvorming uit te testen. Naast de toepassing en klinische validatie van IMRT bestond het werk in het kader van deze thesis ook uit de ontwikkeling en het klinisch opstarten van intensiteitgemoduleerde arc therapie (IMAT). IMAT is een rotationele vorm van IMRT (d.w.z. de gantry draait rond tijdens de bestraling), waarbij de modulatie van de intensiteit bereikt wordt door overlappende arcs. IMAT heeft enkele duidelijke voordelen ten opzichte van IMRT in bepaalde situaties. Als het doelvolume concaaf rond een risico-orgaan ligt met een grote diameter, biedt IMAT eigenlijk een oneindig aantal bundelrichtingen aan. Een planningsstrategie voor IMAT werd ontwikkeld, en type-oplossingen voor totaal abdominale bestraling en rectumbestraling werden onderzocht en klinisch toegepast
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