Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity

Meta-analysis of predictive values is usually discouraged because these values are directly affected by disease prevalence, but sensitivity and specificity sometimes show substantial heterogeneity as well. We propose a bivariate random-effects logitnormal model for the meta-analysis of the positive predictive value (PPV) and negative predictive value (NPV) of diagnostic tests.

Relative sensitivity factors of inorganic cations in frozen-hydrated standards in secondary ion MS analysis

We describe the measurement, at 100 K, of the SIMS relative sensitivity factors (RSFs) of the main physiological cations Na+, K+, Mg2+, and Ca2+ in frozen-hydrated (F-H) ionic solutions. Freezing was performed by either plunge freezing or high-pressure freezing. We also report the measurement of the RSFs in flax fibers, which are a model for ions in the plant cell wall, and in F-H ionic samples, which are a model for ions in the vacuole. RSFs were determined under bombardment with neutral oxygen (FAB) for both the fibers and the F-H samples. We show that referencing to ice-characteristic secondary ions is of little value in determining RSFs and that referencing to K is preferable. The RSFs of Na relative to K and of Ca relative to Mg in F-H samples are similar to their respective values in fiber samples, whereas the RSFs of both Ca and Mg relative to K are lower in fibers than in F-H samples. Our data show that the physical factors important for the determination of the RSFs are not the same in F-H samples and in homogeneous matrixes. Our data show that it is possible to perform a SIMS relative quantification of the cations in frozen-hydrated samples with an accuracy on the order of 15%. Referencing to K permits the quantification of the ionic ratios, even when the absolute concentration of the referencing ion is unknown. This is essential for physiological studies of F-H biological samples.

Lipid-poor adenomas on unenhanced CT: does histogram analysis increase sensitivity compared with a mean attenuation threshold?

OBJECTIVE: The purpose of our study was to evaluate the efficacy of CT histogram analysis for further characterization of lipid-poor adenomas on unenhanced CT. MATERIALS AND METHODS: One hundred thirty-two adrenal nodules were identified in 104 patients with lung cancer who underwent PET/CT. Sixty-five nodules were classified as lipid-rich adenomas if they had an unenhanced CT attenuation of less than or equal to 10 H. Thirty-one masses were classified as lipid-poor adenomas if they had an unenhanced CT attenuation greater than 10 H and stability for more than 1 year. Thirty-six masses were classified as lung cancer metastases if they showed rapid growth in 1 year (n = 27) or were biopsy-proven (n = 9). Histogram analysis was performed for all lesions to provide the mean attenuation value and percentage of negative pixels. RESULTS: All lipid-rich adenomas had more than 10% negative pixels; 51.6% of lipid-poor adenomas had more than 10% negative pixels and would have been classified as indeterminate nodules on the basis of mean attenuation alone. None of the metastases had more than 10% negative pixels. Using an unenhanced CT mean attenuation threshold of less than 10 H yielded a sensitivity of 68% and specificity of 100% for the diagnosis of an adenoma. Using an unenhanced CT threshold of more than 10% negative pixels yielded a sensitivity of 84% and specificity of 100% for the diagnosis of an adenoma. CONCLUSION: CT histogram analysis is superior to mean CT attenuation analysis for the evaluation of adrenal nodules and may help decrease referrals for additional imaging or biopsy.