Canon Inc., Ohta-ku, Tokyo, Japan.
Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Kyoto, Japan.
PLoS One. 2018 Nov 16;13(11):e0207661. doi: 10.1371/journal.pone.0207661. eCollection 2018.
We aimed to describe the development of an inference model for computer-aided diagnosis of lung nodules that could provide valid reasoning for any inferences, thereby improving the interpretability and performance of the system. An automatic construction method was used that considered explanation adequacy and inference accuracy. In addition, we evaluated the usefulness of prior experts' (radiologists') knowledge while constructing the models. In total, 179 patients with lung nodules were included and divided into 79 and 100 cases for training and test data, respectively. F-measure and accuracy were used to assess explanation adequacy and inference accuracy, respectively. For F-measure, reasons were defined as proper subsets of Evidence that had a strong influence on the inference result. The inference models were automatically constructed using the Bayesian network and Markov chain Monte Carlo methods, selecting only those models that met the predefined criteria. During model constructions, we examined the effect of including radiologist's knowledge in the initial Bayesian network models. Performance of the best models in terms of F-measure, accuracy, and evaluation metric were as follows: 0.411, 72.0%, and 0.566, respectively, with prior knowledge, and 0.274, 65.0%, and 0.462, respectively, without prior knowledge. The best models with prior knowledge were then subjectively and independently evaluated by two radiologists using a 5-point scale, with 5, 3, and 1 representing beneficial, appropriate, and detrimental, respectively. The average scores by the two radiologists were 3.97 and 3.76 for the test data, indicating that the proposed computer-aided diagnosis system was acceptable to them. In conclusion, the proposed method incorporating radiologists' knowledge could help in eliminating radiologists' distrust of computer-aided diagnosis and improving its performance.
我们旨在描述一种用于肺结节计算机辅助诊断的推理模型的开发，该模型能够为任何推理提供有效的推理，从而提高系统的可解释性和性能。使用自动构建方法，同时考虑解释充分性和推理准确性。此外，我们在构建模型时评估了先前专家（放射科医生）知识的有用性。总共纳入了 179 名肺结节患者，分为 79 名和 100 名用于训练和测试数据。使用 F 度量和准确性分别评估解释充分性和推理准确性。对于 F 度量，原因被定义为对推理结果有强烈影响的证据的适当子集。使用贝叶斯网络和马尔可夫链蒙特卡罗方法自动构建推理模型，仅选择符合预定义标准的模型。在模型构建过程中，我们检查了在初始贝叶斯网络模型中包含放射科医生知识的效果。具有先验知识的最佳模型在 F 度量、准确性和评估指标方面的性能如下：分别为 0.411、72.0%和 0.566，而没有先验知识的最佳模型分别为 0.274、65.0%和 0.462。然后，两名放射科医生使用 5 分制对具有先验知识的最佳模型进行主观和独立评估，其中 5 分、3 分和 1 分分别表示有益、适当和有害。两名放射科医生对测试数据的平均评分分别为 3.97 和 3.76，表明他们对所提出的计算机辅助诊断系统是可以接受的。总之，纳入放射科医生知识的方法可以帮助消除放射科医生对计算机辅助诊断的不信任，并提高其性能。
