Abstract
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.
Highlights
Advances in imaging modalities have made it possible to acquire large amounts of medical image data for radiologists to assess, increasing their workload
Shiraishi et al proposed a system that calculated the possibility of the presence of a malignant lung nodule from two clinical parameters and 75 imaging features, using linear distinct analysis in chest radiographs [8]; they showed that radiologists’ performance significantly improved with the use of CADx
We have proposed a method of automatic model construction for CADx of lung nodules that had high explanation adequacy and high inference accuracy
Summary
Advances in imaging modalities have made it possible to acquire large amounts of medical image data for radiologists to assess, increasing their workload. Shiraishi et al proposed a system that calculated the possibility of the presence of a malignant lung nodule from two clinical parameters and 75 imaging features, using linear distinct analysis in chest radiographs [8]; they showed that radiologists’ performance significantly improved with the use of CADx. In addition, Chen et al proposed a CADx system that estimated nodule type based on 15 image features, using an ensemble model of artificial neural network with chest computed tomography (CT) [9]. Chen et al proposed a CADx system that estimated nodule type based on 15 image features, using an ensemble model of artificial neural network with chest computed tomography (CT) [9] Their system showed performance comparable to that of senior radiologists while classifying the nodule type. We believe that the barriers to its use could disappear, or at least diminish, if the CAD system could provide justifications for its suggestions
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