The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

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Brain regions activated with complex sequential finger movements were localized by measuring regional cerebral blood flow (rCBF) with positron emission tomography. Whereas the total number and frequency of finger movements were kept constant, the complexity of auditory cued sequential finger movements of the right hand varied, with sequence length as the independent variable. In four conditions of differing complexity, the bilateral primary sensorimotor area, left ventral premotor cortex, posterior supplementary motor area, right superior part of the cerebellum, and left putamen were consistently and equally activated. This finding suggests an executive role in running sequences, regardless of their length. The right dorsal premotor cortex (Brodmann area 6) and the right precuneus (Brodmann area 7) showed a linear increase of rCBF as sequence complexity increased. This finding is consistent with the hypothesis that these areas function in the storage of motor sequences in spatial working memory and the production of ongoing sequential movement with reference to that of buffered memory. A similar increase in the cerebellar vermis and the left thalamus likewise suggests a role of these subcortical structures in complexity of sequential finger movements. Conversely, the left inferior parietal lobule showed a decrease of rCBF as complexity increased. Because short-term phonological storage is localized to this area, we suggest that the visuospatial working memory system may suppress other systems not in use. Our findings suggest that complex sequential finger movements recruit a discrete set of brain areas, in addition to areas underlying the execution of simple movement sequences.