Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning
Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning
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DOI：
10.1016/j.artmed.2016.01.006
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发表时间：
2016-03-01
影响因子：
7.5
通讯作者：
Jagannathan, Rupa
中科院分区：
文献类型：
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作者：
Petrovic, Sanja;Khussainova, Gulmira;Jagannathan, Rupa
目的：放射治疗计划旨在向癌性肿瘤细胞提供足够的放射剂量，同时保护肿瘤周围区域的健康器官。这是一个耗时的试错过程，需要包括肿瘤学家和医学物理学家在内的一组医学专家的专业知识，可能需要 2 至 3 小时到几天的时间。我们的目标是提高我们之前构建的用于脑肿瘤放射治疗计划的基于案例推理 (CBR) 系统的性能。在该系统中，从包含过去治疗的患者病例及其治疗计划的病例库中检索新患者的治疗计划。然而，该系统不执行任何适应，这是考虑新案例和检索到案例之间的任何差异所需要的。一般来说，适应阶段被认为本质上是知识密集型和领域相关的。因此，适应通常需要大量特定领域的知识，而这些知识可能很难获得并且通常不容易获得。在这项研究中，我们研究了不需要太多领域知识的适应方法，称为知识轻适应。方法论：我们开发了两种适应方法：基于机器学习工具的适应和适应引导检索。它们用于调整检索案例中建议的光束数量和光束角度。在适应过程中使用了两种机器学习工具：神经网络和朴素贝叶斯分类器，以了解检索到的案例和新案例之间的属性值差异如何影响这两种案例的输出。适应引导检索不仅考虑了新病例和检索到的病例之间的相似性，还考虑了如何适应检索到的病例。 结果：该研究是与英国诺丁汉大学医院 NHS Trust 城市医院校区的医学物理学家合作进行的。所有实验均使用真实世界中接受三维 (3D) 适形放射治疗的脑癌患者病例进行。基于神经网络的自适应将无自适应的 CBR 系统的成功率提高了 12%。然而，朴素贝叶斯分类器并没有改善当前的检索结果，因为它没有考虑属性之间的相互作用。自适应引导的波束数案例检索将 CBR 系统的成功率提高了 29%。然而，它并没有表现出良好的波束角度自适应性能。其成功率为 29%，而未执行自适应时为 39%。结论：获得的实证结果表明，所提出的自适应方法提高了现有 CBR 系统在推荐要使用的波束数量方面的性能。然而，我们还得出结论，为了有效，所提出的波束角度调整需要案例库中的大量相关案例。 (C) 2016 Elsevier B.V. 保留所有权利。
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. (C) 2016 Elsevier B.V. All rights reserved.