We show that H-1 NMR based metabonomics of serum allows the diagnosis of early stage I/II epithelial ovarian cancer (EOC) required for successful treatment. Because patient specimens are highly precious, we conducted ...
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We show that H-1 NMR based metabonomics of serum allows the diagnosis of early stage I/II epithelial ovarian cancer (EOC) required for successful treatment. Because patient specimens are highly precious, we conducted an exploratory study using a microflow probe requiring only 20,mu L, of serum. By use of logistic regression on principal components (PCs) of the NMR profiles, we built a 4-variable model for early stage EOC prediction (training set: 69 EOC specimens, 84 healthy controls;test set: 40 EOC, 44 controls) with operating characteristics estimated for the test set at 80% specificity [95% confidence interval (CI): 65-90%], 63% sensitivity (95% CI: 46-77%), and an area under the Receiver Operator Characteristic Curve (AUC) of 0.796. Independent validation (SO EOC, 50 controls) of the model yielded 95% specificity (95% CI: 86-99.5%), 68% sensitivity (95% CI: 53-80%) and an AUC of 0.949. A test on cancer type specificity showed that women diseased with renal cell carcinoma were not incorrectly diagnosed with EOC, indicating that metabonomics bears significant potential for cancer type-specific diagnosis. Our model can potentially be applied for women at high risk for EOC, and our study promises to contribute to developing a screening protocol for the general population.
predictive statistical models are used in the area of adaptive user interfaces to model user behavior and to infer user information from interaction events in an implicit and non-intrusive way. This information consti...
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predictive statistical models are used in the area of adaptive user interfaces to model user behavior and to infer user information from interaction events in an implicit and non-intrusive way. This information constitutes the basis for tailoring the user interface to the needs of the individual user. Consequently, the user analysis process should model the user with information, which can be used in various systems to recognize user activities, intentions and roles to accomplish an adequate adaptation to the given user and his current task. In this paper we present the improved prediction algorithm KO*/19, which is able to recognize, beside interaction predictions, behavioral patterns for recognizing user activities. By means of this extension, the evaluation shows that the KO*/19Algorithm improves the Mean Prediction Rank more than 19％ compared to other well-established prediction algorithms.
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