Approaches to knowledge-light adaptation in case-based reasoning for radiotherapy treatment planning

In radiotherapy, ionised radiation beams are used to destroy cancerous cells. A radiotherapy treatment plan needs to be created to deliver a sufficient radiation dose to cancerous cells while sparing nearby organs at risk and healthy tissue. The development of such a treatment plan is a time consuming trial and error process which can take from a few hours up to a few days. This thesis builds on the previously developed Case-Based Reasoning (CBR) system for radiotherapy treatment planning for brain cancer that was developed in collaboration with Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The original CBR system focused on the retrieval stage of CBR, where the most similar case was retrieved for the new patient case. The results obtained were promising but adaptation needed to be performed for them to be suitable for the new patient. Testing of the CBR system by medical physicists has revealed that some of the retrieved radiation beams were not suitable for the tumour position of the new cases and thus could not be used. To avoid this the clustering of cases by their tumour positions was implemented to only retrieve cases with similar tumour positions. The revised CBR system should now retrieve treatment plans with better suited beams. Adaptation requires a lot of domain knowledge which is often difficult to acquire. In this research we present adaptation approaches which are knowledge-light, i.e. they utilise knowledge available in the case base without requiring interaction with medical experts. Adaptation methods based on machine learning algorithms, in particular neural networks, the naive Bayes classifier, and support vector machines, were developed. Also, an adaptation-guided retrieval approach is presented, in which the case is retrieved only if it can be adapted. In addition, a pair of similar cases are retrieved with it, which guide the adaptation process. The developed knowledge-light adaptation methods have improved the results of the original CBR system. In addition, the proposed adaptation methods are general and could be used in domains where the available amount of knowledge is limited

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

Approaches to knowledge-light adaptation in case-based reasoning for radiotherapy treatment planning

In radiotherapy, ionised radiation beams are used to destroy cancerous cells. A radiotherapy treatment plan needs to be created to deliver a sufficient radiation dose to cancerous cells while sparing nearby organs at risk and healthy tissue. The development of such a treatment plan is a time consuming trial and error process which can take from a few hours up to a few days. This thesis builds on the previously developed Case-Based Reasoning (CBR) system for radiotherapy treatment planning for brain cancer that was developed in collaboration with Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The original CBR system focused on the retrieval stage of CBR, where the most similar case was retrieved for the new patient case. The results obtained were promising but adaptation needed to be performed for them to be suitable for the new patient. Testing of the CBR system by medical physicists has revealed that some of the retrieved radiation beams were not suitable for the tumour position of the new cases and thus could not be used. To avoid this the clustering of cases by their tumour positions was implemented to only retrieve cases with similar tumour positions. The revised CBR system should now retrieve treatment plans with better suited beams. Adaptation requires a lot of domain knowledge which is often difficult to acquire. In this research we present adaptation approaches which are knowledge-light, i.e. they utilise knowledge available in the case base without requiring interaction with medical experts. Adaptation methods based on machine learning algorithms, in particular neural networks, the naive Bayes classifier, and support vector machines, were developed. Also, an adaptation-guided retrieval approach is presented, in which the case is retrieved only if it can be adapted. In addition, a pair of similar cases are retrieved with it, which guide the adaptation process. The developed knowledge-light adaptation methods have improved the results of the original CBR system. In addition, the proposed adaptation methods are general and could be used in domains where the available amount of knowledge is limited