With the increasingly using of LIDC data in lung cancer research, education, and clinical medicine areas, the needs for effectively accessing and visualizing the CT scans and annotations collected by LIDC is more and more underscored. In this paper, we introduce the data integration and visualization software for the LIDC data exploration. A XML-based data model is provided for storing the LIDC data to combine different kinds of information into a uniform format. The data integration component is developed based on technologies of Java Excel API and JAXP. A nodule viewer is developed to visualize DICOM images and the annotations (commonly known as nodules) using DCMTK. The software offers a solution to the challenge of lack of a uniform data format for the LIDC data and nodule viewer. This software has the potential application in the areas of lung cancer research, education and patient care

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Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17 %, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education.