Imaging and cancer: a review

Imaging and cancer: a review

Multiple biomedical imaging techniques are used in all phases of cancer management. Imaging forms an essential part of cancer clinical protocols and is able to furnish morphological, structural, metabolic and functional information. Integration with other diagnostic tools such as in vitro tissue and fluids analysis assists in clinical decision-making. Hybrid imaging techniques are able to supply complementary information for improved staging and therapy planning. Image guided and targeted minimally invasive therapy has the promise to improve outcome and reduce collateral effects. Early detection of cancer through screening based on imaging is probably the major contributor to a reduction in mortality for certain cancers. Targeted imaging of receptors, gene therapy expression and cancer stem cells are research activities that will translate into clinical use in the next decade. Technological developments will increase imaging speed to match that of physiological processes. Targeted imaging and therapeutic agents will be developed in tandem through close collaboration between academia and biotechnology, information technology and pharmaceutical industries.

Generalized semiparametrically structured ordinal models

Semiparametrically structured models are defined as a class of models for which the predictors may contain parametric parts, additive parts of covariates with an unspecified functional form, and interactions which are described as varying coefficients. In the case of an ordinal response the complexity of the predictor is determined by different sorts of effects. Global effects and category-specific effects are distinguished; the latter allow the effect to vary across response categories. A general framework is developed in which global as well as category-specific effects may have unspecified functional form. The framework extends various existing methods of modeling ordinal responses. The Wilkinson-Rogers notation is extended to incorporate smooth model parts and varying coefficient terms, the latter being important for the smooth specification of category-specific effects.