Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy
Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy
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DOI：
10.1118/1.3352709
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发表时间：
2010-04-01
期刊：
影响因子：
3.8
通讯作者：
Dekker, A. L. A. J.
中科院分区：
文献类型：
--
作者：
Jayasurya, K.;Fung, G.;Dekker, A. L. A. J.
目的：经典的统计和机器学习模型，如支持向量机（svm）可用于预测癌症结果，但通常只有在所有输入变量都已知的情况下才能表现良好，这在医学领域是不可能的。贝叶斯网络（BN）模型具有自然的不确定性推理能力，可以更好地处理缺失数据。在这项研究中，作者假设BN模型可以像SVM一样准确地预测非小细胞肺癌（NSCLC）患者的两年生存率，但在数据缺失时预测生存率会更准确。方法：对来自马斯特里赫特的322例接受放疗的不能手术NSCLC患者进行BN和SVM模型的训练，并在来自根特、鲁汶和多伦多的35例、47例和33例患者的三个独立数据集中进行验证。只有37例、28例和24例患者拥有完整的数据集，数据集中出现了缺失变量。结果：BN模型结构和参数学习确定了肿瘤体积大小、性能状态和PET阳性淋巴结数量作为两年生存率的预后因素。当在根特、鲁汶和多伦多的完整验证集中进行验证时，BN模型的AUC分别为0.77、0.72和0.70。基于相同变量的SVM模型的总体性能较差（AUC分别为0.71、0.68和0.69），特别是在Ghent集合中，该集合缺失重要GTV大小数据的比例最高。当仅考虑具有完整数据集的患者时，BN和SVM模型的表现更为相似。结论：在本研究的局限性内，支持BN模型比SVM模型更擅长处理缺失数据的假设，因此更适合于医学领域。未来的工作必须专注于通过纳入更多的患者，更多的变量和更多的多样性来提高BN的性能。
Purpose: Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better. In this study, the authors hypothesize that a BN model can predict two-year survival in non-small cell lung cancer (NSCLC) patients as accurately as SVM, but will predict survival more accurately when data are missing.Methods: A BN and SVM model were trained on 322 inoperable NSCLC patients treated with radiotherapy from Maastricht and validated in three independent data sets of 35, 47, and 33 patients from Ghent, Leuven, and Toronto. Missing variables occurred in the data set with only 37, 28, and 24 patients having a complete data set.Results: The BN model structure and parameter learning identified gross tumor volume size, performance status, and number of positive lymph nodes on a PET as prognostic factors for two-year survival. When validated in the full validation set of Ghent, Leuven, and Toronto, the BN model had an AUC of 0.77, 0.72, and 0.70, respectively. A SVM model based on the same variables had an overall worse performance (AUC 0.71, 0.68, and 0.69) especially in the Ghent set, which had the highest percentage of missing the important GTV size data. When only patients with complete data sets were considered, the BN and SVM model performed more alike.Conclusions: Within the limitations of this study, the hypothesis is supported that BN models are better at handling missing data than SVM models and are therefore more suitable for the medical domain. Future works have to focus on improving the BN performance by including more patients, more variables, and more diversity.