Interobserver reproducibility of The Paris System for Reporting Urinary Cytology.

BACKGROUND: The Paris System for Reporting Urinary Cytology represents a significant improvement in classification of urinary specimens. The system acknowledges the difficulty in cytologically diagnosing low-grade urothelial carcinomas and has developed categories to deal with this issue. The system uses six categories: unsatisfactory, negative for high-grade urothelial carcinoma (NHGUC), atypical urothelial cells, suspicious for high-grade urothelial carcinoma, high-grade urothelial carcinoma, other malignancies and a seventh subcategory (low-grade urothelial neoplasm). METHODS: Three hundred and fifty-seven urine specimens were independently reviewed by four cytopathologists unaware of the previous diagnoses. Each cytopathologist rendered a diagnosis according to the Paris System categories. Agreement was assessed using absolute agreement and weighted chance-corrected agreement (kappa). Disagreements were classified as low impact and high impact based on the potential impact of a misclassification on clinical management. RESULTS: The average absolute agreement was 65% with an average expected agreement of 44%. The average chance-corrected agreement (kappa) was 0.32. Nine hundred and ninety-nine of 1902 comparisons between rater pairs were in agreement, but 12% of comparisons differed by two or more categories for the category NHGUC. Approximately 15% of the disagreements were classified as high clinical impact. CONCLUSIONS: Our findings indicated that the scheme recommended by the Paris System shows adequate precision for the category NHGUC, but the other categories demonstrated unacceptable interobserver variability. This low level of diagnostic precision may negatively impact the applicability of the Paris System for widespread clinical application.

Quantifying factors for the success of stratified medicine.

Co-developing a drug with a diagnostic to create a stratified medicine - a therapy that is targeted to a specific patient population on the basis of a clinical characteristic such as a biomarker that predicts treatment response - presents challenges for product developers, regulators, payers and physicians. With the aim of developing a shared framework and tools for addressing these challenges, here we present an analysis using data from case studies in oncology and Alzheimer's disease, coupled with integrated computational modelling of clinical outcomes and developer economic value, to quantify the effects of decisions related to key issues such as the design of clinical trials. This illustrates how such analyses can aid the coordination of diagnostic and drug development, and the selection of optimal development and commercialization strategies. It also illustrates the impact of the interplay of these factors on the economic feasibility of stratified medicine, which has important implications for public policy makers.

Investigating the neural basis for functional and effective connectivity. Application to fMRI.

Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We have constructed neurobiologically realistic models for visual and auditory object processing with multiple interconnected brain regions that perform delayed match-to-sample (DMS) tasks. We used these models to investigate how neurobiological parameters affect the interregional functional connectivity between functional magnetic resonance imaging (fMRI) time-series. Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trial-to-trial interactions with non-specific neurons processing noise stimuli. We find that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during the DMS task than during a control task. These results were less clear when the integrated synaptic activity was haemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations.