Rationale and Objectives
The aim of this study was to develop a pulmonary nodule viewing system to visualize and retrieve data from the Lung Image Database Consortium.
Materials and Methods
The pulmonary nodule viewing system, developed using Microsoft C++ and the .NET 2.0 Framework, is composed of a clinical information integrator, a nodule viewer, a search engine, and a data model.
Results
A pulmonary nodule viewing system using Lung Image Database Consortium data for computer-aided diagnosis research and training purpose was developed.
Conclusions
The pulmonary nodule viewing system can be used to build a pulmonary nodule database for computer-aided diagnosis research and medical education. It can also be used to view and retrieve large data sets efficiently.
In the continuing battle against lung cancer, computed tomographic (CT) scanning has been found to increase the detection rate of pulmonary nodules . Much work has been done to develop computer-aided detection and diagnosis (CAD) systems for pulmonary nodules on CT imaging . Training and testing systems have also been considered to educate residents and fellows in lung cancer image interpretation . Reference databases with large numbers of cases containing representative types of nodules are vital for the development of robust CAD systems and training systems. However, these databases are very expensive to create. At present, there are only a limited number of public available databases to support research in CAD. The National Cancer Institute’s Lung Image Database Consortium (LIDC) is one of these. The development of the LIDC has led to a large amount of research based on the image sets that are provided to users. Meyer et al investigated the effects of radiologist agreement on the development of a “ground truth” and the subsequent impact on CAD performance. Reeves et al compared volumes computed using their research system with those derived from the LIDC archive. LIDC data have also been used to train and validate CAD algorithms , as well as to develop a pulmonary nodule image retrieval system .
However, there are still some barriers that prevent LIDC data from being used efficiently. First, the LIDC data lack a unified data model. At present, the data are composed of Digital Imaging and Communications in Medicine (DICOM) images, annotation files, a nodule size report, and a diagnosis report. The annotation files are in extensible markup language (XML) format. The nodule size report and the diagnosis data report are Excel spreadsheets (Microsoft Corporation, Redmond, WA). As a result, it is difficult to access this valuable resource efficiently, because of the lack of a unified data view.
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Materials and methods
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Table 1
Summary of Subjective Assessment Ratings and Associated Descriptive Terms of Nodule Characteristics Provided by Lung Image Database Consortium Radiologists
Characteristic Description Ratings and Terms Calcification Calcification appearance in the nodule
Internal structure Internal composition of the nodule (soft tissue, fluid, fat, air)
Subtlety Difficulty in detection (refers to the contrast between the lung and its surroundings)
Lobulation Whether a lobular shape is apparent from the margin or not
Margin How well defined the margins of the nodule are
Sphericity The three-dimensional shape of the nodule in terms of its roundness
Malignancy Likelihood of malignancy of the nodule
Texture Internal texture of the nodule
Spiculation Degree to which the nodule has spicules, spikelike structures, along its border
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Data Model
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Clinical Information Integrator
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Nodule Viewer
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Search Engine
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Results
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Discussion
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