Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data.

BACKGROUND: For the statistical analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data, compartment models are a commonly used tool. By these models, the observed uptake of contrast agent in some tissue over time is linked to physiologic properties like capillary permeability and blood flow. Up to now, models of different complexity have been used, and it is still unclear which model should be used in which situation. In previous studies, it has been found that for DCE-MRI data, the number of compartments differs for different types of tissue, and that in cancerous tissue, it might actually differ over a region of voxels of one DCE-MR image. OBJECTIVES: To find the appropriate number of compartments and estimate the parameters of a regression model for each voxel in an DCE-MR image. With that, tumors in an DCE-MR image can be located, and for example therapy success can be assessed. METHODS: The observed uptake of contrast agent in a voxel of an image of some tissue is described by a concentration time curve. This curve can be modeled using a nonlinear regression model. We present a boosting approach with nonlinear regression as base procedure, which allows us to estimate the number of compartments and the related parameters for each voxel of an DCE-MR image. In addition, a spatially regularized version of this approach is proposed. RESULTS: With the proposed approach, the number of compartments - and with that the complexity of the model - per voxel is not fixed but data-driven, which allows us to fit models of adequate complexity to the concentration time curves of all voxels. The parameters of the model remain nevertheless interpretable because of the underlying compartment model. CONCLUSIONS: The proposed boosting approaches outperform all competing methods considered in this paper regarding the correct localization of tumors in DCE-MR images as well as the spatial homogeneity of the estimated number of compartments across the image, and the definition of the tumor edge.

[Value and acceptance of risk assessment for Alzheimer's disease]. / Wert und Akzeptanz einer Alzheimer-Risikodiagnostik.

BACKGROUND: It is quite common that people suffering from cognitive impairment only visit a doctor when the symptoms have already reached an advanced stage. This is often due to a fear of Alzheimer's disease or a dread of exhausting diagnostic procedures and exposure of personal details; however, an early diagnosis and therapy increases the chance of preserving the quality of life for a longer period of time. OBJECTIVES: Evaluation of a risk assessment for Alzheimer's disease by magnetic resonance imaging (MRI) with respect to the acceptance and value by participants. METHODS: In this prospective preventive study 106 subjects between the age of 39 and 89 years (median age 68 years) with general risk factors were included and underwent a risk assessment for Alzheimer's disease by standard MRI of the brain using a 1 T open MRI with subsequent hippocampal volumetry. Participants were stratified into two distinct subgroups according to the individual hippocampal atrophy status, one with elevated and the other with reduced risk. All participants were thoroughly interviewed regarding anxieties and mental well-being before and after the risk assessment. RESULTS: As expected, participants with a reduced risk had a significant improvement in well-being and a reduction of fears and worries after the examination. Neither a significant deterioration of the mental situation nor an increase of fears and worries was found for participants with an elevated risk. Of the participants 90% stated that MRI-based risk stratification generated positive perspectives for the future. The assessment revealed a high acceptance by most of the participants (94%). CONCLUSION: An MRI-based risk assessment is beneficial to the patient's quality of life and as a low threshold approach may induce more individuals with concerns to take advantage of an early diagnosis of Alzheimer's disease.

Discussion of "the evolution of boosting algorithms" and "extending statistical boosting".

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the papers "The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling" and "Extending Statistical Boosting - An Overview of Recent Methodological Developments", written by Andreas Mayr and co-authors. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Mayr et al. papers. In subsequent issues the discussion can continue through letters to the editor.

Variable Selection in Generalized Functional Linear Models.

Modern research data, where a large number of functional predictors is collected on few subjects are becoming increasingly common. In this paper we propose a variable selection technique, when the predictors are functional and the response is scalar. Our approach is based on adopting a generalized functional linear model framework and using a penalized likelihood method that simultaneously controls the sparsity of the model and the smoothness of the corresponding coefficient functions by adequate penalization. The methodology is characterized by high predictive accuracy, and yields interpretable models, while retaining computational efficiency. The proposed method is investigated numerically in finite samples, and applied to a diffusion tensor imaging tractography data set and a chemometric data set.