Moderate inter-observer agreement was found when observers were compared pairwise. False-negative errors seem to be the major problem in the interpretations of bone scan images, whilst the specificities for the observers were high.
The aim of this multicenter study was to investigate whether a computer-assisted diagnosis (CAD) system could improve performance and reduce interobserver variation in bone-scan interpretations of the presence or absence of bone metastases. Methods: The whole-body bone scans (anterior and posterior views) of 59 patients with breast or prostate cancer who had undergone scintigraphy for suspected bone metastatic disease were studied. The patients were selected to reflect the spectrum of pathology found in everyday clinical work. Thirty-five physicians working at 18 of the 30 nuclear medicine departments in Sweden agreed to participate. The physicians were asked to classify each case for the presence or absence of bone metastasis, without (baseline) and with the aid of the CAD system (1 y later), using a 4-point scale. The final clinical assessments, based on follow-up scans and other clinical data including the results of laboratory tests and available diagnostic images (such as MRI, CT, and radiographs from a mean follow-up period of 4.8 y), were used as the gold standard. Each physician's classification was pairwise compared with the classifications made by all the other physicians, resulting in 595 pairs of comparisons, both at baseline and after using the CAD system. Results: The physicians increased their sensitivity from 78% without to 88% with the aid of the CAD system (P , 0.001). The specificity did not change significantly with CAD. Percentage agreement and k-values between paired physicians on average increased from 64% to 70% and from 0.48 to 0.55, respectively, with the CAD system. Conclusion: A CAD system improved physicians' sensitivity in detecting metastases and reduced interobserver variation in planar whole-body bone scans. The CAD system appears to have significant potential in assisting physicians in their clinical routine.
The purpose of this study was to develop a completely automated method for the interpretation of ventilation-perfusion (V-P) lung scintigrams used in the diagnosis of pulmonary embolism. An artificial neural network was trained for the diagnosis of pulmonary embolism using 18 automatically obtained features from each set of V-P scintigrams. The techniques used to process the images included their alignment to templates, the construction of quotient images based on the ventilation and perfusion images, and the calculation of measures describing V-P mismatches in the quotient images. The templates represented lungs of normal size and shape without any pathological changes. Images that could not be properly aligned to the templates were detected and excluded automatically. After exclusion of those V-P scintigrams not properly aligned to the templates, 478 V-P scintigrams remained in a training group of consecutive patients with suspected pulmonary embolism, and a further 87 V-P scintigrams formed a separate test group comprising patients who had undergone pulmonary angiography. The performance of the neural network, measured as the area under the receiver operating characteristic curve, was 0.87 (95% confidence limits 0.82-0.92) in the training group and 0.79 (0.69-0.88) in the test group. It is concluded that a completely automated method can be used for the interpretation of V-P scintigrams. The performance of this method is similar to others previously presented, whereby features were extracted manually.
The yields of the reactions NUCLEUS 0', YP xn) 24Na have been measured for eleven elements with 13 -< Z-< 29 at maximum bremsstrahlung energies 100 MeV =< E7 max < 1000 MeV. An exponential decrease with increasing Z of the mean cross section calculated from the yield data has been obtained. This Z-dependence fits well to the systematics of spallation product cross-sections.
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