Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning
作者：
Highlights：
• This paper is concerned with a case-based reasoning (CBR) system for radiotherapy treatment planning for brain cancer patients, which has been developed in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK.
• The developed CBR system generates the parameters of a treatment plan by capturing the subjective and intuitive knowledge of medical physicists.
• In this research we focus on the adaptation stage of the CBR system in which the solution (treatment plan) of the retrieved case is adapted to meet the needs of the new case (patient) by considering differences between the retrieved and new cases.
• We investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation.
• Results obtained by three developed adaptation approaches including adaptation based on Neural Networks and naïve Bayes classifier, and adaptation-guided retrieval are presented.
摘要
Highlights•This paper is concerned with a case-based reasoning (CBR) system for radiotherapy treatment planning for brain cancer patients, which has been developed in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK.•The developed CBR system generates the parameters of a treatment plan by capturing the subjective and intuitive knowledge of medical physicists.•In this research we focus on the adaptation stage of the CBR system in which the solution (treatment plan) of the retrieved case is adapted to meet the needs of the new case (patient) by considering differences between the retrieved and new cases.•We investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation.•Results obtained by three developed adaptation approaches including adaptation based on Neural Networks and naïve Bayes classifier, and adaptation-guided retrieval are presented.
论文关键词：Case-based reasoning,Adaptation-guided retrieval,Machine-learning tools,Radiotherapy treatment planning
论文评审过程：Received 30 January 2015, Revised 17 December 2015, Accepted 20 January 2016, Available online 9 February 2016, Version of Record 13 April 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.01.006
