PURPOSE: The purpose of this study is to evaluate the usefulness of a novel computerized method to select automatically the similar chest radiograph for image subtraction in the patients who have no previous chest radiographs and to assist the radiologists' interpretation by presenting the "similar subtraction image" from different patients. MATERIALS AND METHODS: Institutional review board approval was obtained, and the requirement for informed patient consent was waived. A large database of approximately 15,000 normal chest radiographs was used for searching similar images of different patients. One hundred images of candidates were selected according to two clinical parameters and similarity of the lung field in the target image. We used the correlation value of chest region in the 100 images for searching the most similar image. The similar subtraction images were obtained by subtracting the similar image selected from the target image. Thirty cases with lung nodules and 30 cases without lung nodules were used for an observer performance test. Four attending radiologists and four radiology residents participated in this observer performance test. RESULTS: The AUC for all radiologists increased significantly from 0.925 to 0.974 with the CAD (P=.004). When the computer output images were available, the average AUC for the residents was more improved (0.960 vs. 0.890) than for the attending radiologists (0.987 vs. 0.960). CONCLUSION: The novel computerized method for lung nodule detection using similar subtraction images from different patients would be useful to detect lung nodules on digital chest radiographs, especially for less experienced readers.
PURPOSE: The purpose of this study is to evaluate the usefulness of a novel computerized method to select automatically the similar chest radiograph for image subtraction in the patients who have no previous chest radiographs and to assist the radiologists' interpretation by presenting the "similar subtraction image" from different patients. MATERIALS AND METHODS: Institutional review board approval was obtained, and the requirement for informed patient consent was waived. A large database of approximately 15,000 normal chest radiographs was used for searching similar images of different patients. One hundred images of candidates were selected according to two clinical parameters and similarity of the lung field in the target image. We used the correlation value of chest region in the 100 images for searching the most similar image. The similar subtraction images were obtained by subtracting the similar image selected from the target image. Thirty cases with lung nodules and 30 cases without lung nodules were used for an observer performance test. Four attending radiologists and four radiology residents participated in this observer performance test. RESULTS: The AUC for all radiologists increased significantly from 0.925 to 0.974 with the CAD (P=.004). When the computer output images were available, the average AUC for the residents was more improved (0.960 vs. 0.890) than for the attending radiologists (0.987 vs. 0.960). CONCLUSION: The novel computerized method for lung nodule detection using similar subtraction images from different patients would be useful to detect lung nodules on digital chest radiographs, especially for less experienced readers.
Authors: Thaworn Dendumrongsup; Andrew A Plumb; Steve Halligan; Thomas R Fanshawe; Douglas G Altman; Susan Mallett Journal: PLoS One Date: 2014-12-26 Impact factor: 3.240