PubMed · 7550625
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E E Fess. Guidelines for evaluating assessment instruments.. https://doi.org/10.1016/s0894-1130(12)80312-7
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RATIONALE AND OBJECTIVES: The authors investigated the use of fractal texture characterization to improve the accuracy of solitary pulmonary nodule computer-aided diagnosis (CAD) systems. METHODS: Thirty chest radiographs were acquired from patients who had no pulmonary nodules. Thirty regions were selected that were considered remotely suspicious-looking for nodules. Artificial nodules of multiple shapes, sizes, and orientations were added at subtle levels of contrast to 30 non-suspicious-looking regions of the radiographs. Fractal dimensions of the 60 "nodule candidates" were calculated to quantify the texture of each region. Four radiologists also interpreted the images. RESULTS: The fractal dimension of each possible nodule provided statistically significant (P < .05) differentiation between regions that contained an artificial nodule and those that did not. The area under the receiver operating characteristic curve for the fractal analysis was significantly better (P < .05) than that for the radiologists. CONCLUSION: Fractal texture characterization provides useful information for the classification of potential solitary pulmonary nodules with CAD algorithms.
PURPOSE: To test the accuracy of a combined radiologist-computer system in the diagnosis with magnetic resonance (MR) imaging of cancer of the prostate gland. MATERIALS AND METHODS: The combined system was developed and tested by four specialists in prostate MR imaging and five radiologists expert in body MR imaging. Each group read MR images obtained in 100 proved cases of prostate cancer. The images were obtained from two sources, and all were obtained with an endorectal surface coil. Prostate MR specialists ranked imaging features of cases to develop a checklist for image interpretation. Features with greatest diagnostic value were incorporated in the combined system. Accuracy measures were derived from the area index of the receiver operating characteristic curve for the combined system and compared with those of radiologists working alone. RESULTS: Body MR radiologists had a mean baseline accuracy of 0.67; mean accuracy of their combined system was 0.80. The prostate MR specialists, when they rated the features in each case, had a mean accuracy of 0.81; the accuracy of their combined system was 0.87. CONCLUSIONS: A combined radiologist-computer system substantially improved accuracy of body MR radiologists in the diagnosis of prostate cancer. High levels of accuracy were also achieved by the system with prostate MR specialists.
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