A Local Rule-Based Attribute Weighting Scheme for a Case-Based Reasoning System for Radiotherapy Treatment Planning

Jagannathan, Rupa; Petrović, Sanja

doi:10.1007/978-3-642-32986-9_14

Cited by 4 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different similarity measures were proposed, including a fuzzy similarity measure that used fuzzy sets to represent the contribution of each individual attribute to the similarity between two cases along with a non-linear similarity measure, in which the similarity between two values of an attribute of two cases contributed in a non-linear way to the aggregate similarity between two cases. The similarity measure that used local weights assigned to case attributes that depended on the values of other case attributes was described in [20]. The research presented in this paper builds upon this work and investigates the extent to which the adaptation can improve the plan proposed by the retrieval phase of the CBR system.…”

Section: Case-based Reasoning In Cancer Treatmentmentioning

confidence: 99%

“…A weighted nearest neighbour (wNN) similarity measure was defined with local weights identified for each of the attributes depending on the values of other attributes [20]. Figure 2.…”

Section: Overview Of the Cbr System For Radiotherapy Treatment Planningmentioning

confidence: 99%

“…The weights of the attributes are different in the retrieval of a case to suggest the beam number (suggested in phase 1 of the CBR system) from the retrieval for beam angles (suggested in phase 2). An algorithm was developed that learns from the data-appropriate rules to assign a local weight to each attribute based on the values of the new case attributes [20].…”

Section: Overview Of the Cbr System For Radiotherapy Treatment Planningmentioning

confidence: 99%

See 2 more Smart Citations

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

Petrović

Khussainova

Jagannathan

2016

Artificial Intelligence in Medicine

Self Cite

View full text Add to dashboard Cite

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 2-3 hours 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 adaptationguided 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.

show abstract

Section: Case-based Reasoning In Cancer Treatmentmentioning

confidence: 99%

“…A weighted nearest neighbour (wNN) similarity measure was defined with local weights identified for each of the attributes depending on the values of other attributes [20]. Figure 2.…”

Section: Overview Of the Cbr System For Radiotherapy Treatment Planningmentioning

confidence: 99%

Section: Overview Of the Cbr System For Radiotherapy Treatment Planningmentioning

confidence: 99%

See 1 more Smart Citation

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

Petrović

Khussainova

Jagannathan

2016

Artificial Intelligence in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

Predicting Road Accidents Based on Current and Historical Spatio-temporal Traffic Flow Data

Jagannathan

Petrović

Powell

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Determining the Number of Beams in 3D Conformal Radiotherapy: A Classification Approach

Dias

Jaganathan

Petrović

2013

Procedia Technology

View full text Add to dashboard Cite

A Local Rule-Based Attribute Weighting Scheme for a Case-Based Reasoning System for Radiotherapy Treatment Planning

Cited by 4 publications

References 17 publications

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

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

Predicting Road Accidents Based on Current and Historical Spatio-temporal Traffic Flow Data

Determining the Number of Beams in 3D Conformal Radiotherapy: A Classification Approach

Contact Info

Product

Resources

About