Functional generalized canonical correlation analysis for studying multiple longitudinal variables.

In this paper, we introduce functional generalized canonical correlation analysis, a new framework for exploring associations between multiple random processes observed jointly. The framework is based on the multiblock regularized generalized canonical correlation analysis framework. It is robust to sparsely and irregularly observed data, making it applicable in many settings.

Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge.

Purpose: The 2021 edition of the Artificial Intelligence Data Challenge was organized by the French Society of Radiology together with the Centre National d'Études Spatiales and CentraleSupélec with the aim to implement generative adversarial networks (GANs) techniques to provide 1000 magnetic resonance imaging (MRI) cases of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC), a rare and aggressive subtype of HCC, generated from a limited number of real cases from multiple French centers.

Materials And Methods: A dedicated platform was used by the seven inclusion centers to securely upload their anonymized MRI examinations including all three cross-sectional images (one late arterial and one portal-venous phase T1-weighted images and one fat-saturated T2-weighted image) in compliance with general data protection regulation. The quality of the database was checked by experts and manual delineation of the lesions was performed by the expert radiologists involved in each center.

Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview.

Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence.

Multi-omics analysis to decipher the molecular link between chronic exposure to pollution and human skin dysfunction.

Environmental pollution is composed of several factors, namely particulate matter (PM, PM), ozone and Ultra Violet (UV) rays among others and first and the most exposed tissue to these substances is the skin epidermis. It has been established that several skin disorders such as eczema, acne, lentigines and wrinkles are aggravated by exposure to atmospheric pollution. While pollutants can interact with skin surface, contamination of deep skin by ultrafine particles or Polycyclic aromatic hydrocarbons (PAH) might be explained by their presence in blood and hair cortex.

Changes of the human skin microbiota upon chronic exposure to polycyclic aromatic hydrocarbon pollutants.

Background: Polycyclic aromatic hydrocarbons (PAHs) are of environmental and public health concerns and contribute to adverse skin attributes such as premature skin aging and pigmentary disorder. However, little information is available on the potential roles of chronic urban PAH pollutant exposure on the cutaneous microbiota. Given the roles of the skin microbiota have on healthy and undesirable skin phenotypes and the relationships between PAHs and skin properties, we hypothesize that exposure of PAHs may be associated with changes in the cutaneous microbiota.

Multiway generalized canonical correlation analysis.

Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA).

Hepatobiliary MR contrast agent uptake as a predictive biomarker of aggressive features on pathology and reduced recurrence-free survival in resectable hepatocellular carcinoma: comparison with dual-tracer 18F-FDG and 18F-FCH PET/CT.

Objectives: To compare the performance of the quantitative analysis of the hepatobiliary phase (HBP) tumor enhancement in gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI and of dual-tracer 18F-FDG and 18F-fluorocholine (FCH) PET/CT for the prediction of tumor aggressiveness and recurrence-free survival (RFS) in resectable hepatocellular carcinoma (HCC).

Methods: This retrospective, IRB approved study included 32 patients with 35 surgically proven HCCs. All patients underwent Gd-BOPTA-enhanced MRI including delayed HBP images, 18F-FDG PET/CT, and (for 29/32 patients) 18F-FCH PET/CT during the 2 months prior to surgery.

Multiphase Liver MRI for Identifying the Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma.

Background The recently described "macrotrabecular-massive" (MTM) histologic subtype of hepatocellular carcinoma (HCC) (MTM-HCC) represents an aggressive form of HCC and is associated with poor survival. Purpose To investigate whether preoperative MRI can help identify MTM-HCCs in patients with HCC. Materials and Methods This retrospective study included patients with HCC treated with surgical resection between January 2008 and February 2018 and who underwent preoperative multiphase contrast material-enhanced MRI.

Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study.

Background: One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load.

Methods: We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918).

A constrained singular value decomposition method that integrates sparsity and orthogonality.

We propose a new sparsification method for the singular value decomposition-called the constrained singular value decomposition (CSVD)-that can incorporate multiple constraints such as sparsification and orthogonality for the left and right singular vectors. The CSVD can combine different constraints because it implements each constraint as a projection onto a convex set, and because it integrates these constraints as projections onto the intersection of multiple convex sets. We show that, with appropriate sparsification constants, the algorithm is guaranteed to converge to a stable point.

Hepatocellular carcinoma: CT texture analysis as a predictor of survival after surgical resection.

Objectives: To determine whether image texture parameters analysed on pre-operative contrast-enhanced computed tomography (CT) can predict overall survival and recurrence-free survival in patients with hepatocellular carcinoma (HCC) treated by surgical resection.

Methods: We retrospectively included all patients operated for HCC who had liver contrast-enhanced CT within 3 months prior to treatment in our centre between 2010 and 2015. The following texture parameters were evaluated on late-arterial and portal-venous phases: mean grey-level, standard deviation, kurtosis, skewness and entropy.

Can dual-energy CT replace perfusion CT for the functional evaluation of advanced hepatocellular carcinoma?

Objectives: To determine the degree of relationship between iodine concentrations derived from dual-energy CT (DECT) and perfusion CT parameters in patients with advanced HCC under treatment.

Methods: In this single-centre IRB approved study, 16 patients with advanced HCC treated with sorafenib or radioembolization who underwent concurrent dynamic perfusion CT and multiphase DECT using a single source, fast kV switching DECT scanner were included. Written informed consent was obtained for all patients.

A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia.

The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasizes the need for novel statistical methods of data analysis able to unify these views.

Modeling a linkage between blood transcriptional expression and activity in brain regions to infer the phenotype of schizophrenia patients.

Unlabelled: Hundreds of genetic loci participate to schizophrenia liability. It is also known that impaired cerebral connectivity is directly related to the cognitive and affective disturbances in schizophrenia. How genetic susceptibility and brain neural networks interact to specify a pathological phenotype in schizophrenia remains elusive.

Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods.

A new framework for sequential multiblock component methods is presented. This framework relies on a new version of regularized generalized canonical correlation analysis (RGCCA) where various scheme functions and shrinkage constants are considered. Two types of between block connections are considered: blocks are either fully connected or connected to the superblock (concatenation of all blocks).

Compressed-sensing-based content-driven hierarchical reconstruction: Theory and application to C-arm cone-beam tomography.

Purpose: This paper addresses the reconstruction of x-ray cone-beam computed tomography (CBCT) for interventional C-arm systems. Subsampling of CBCT is a significant issue with C-arms due to their slow rotation and to the low frame rate of their flat panel x-ray detectors. The aim of this work is to propose a novel method able to handle the subsampling artifacts generally observed with analytical reconstruction, through a content-driven hierarchical reconstruction based on compressed sensing.

Variable selection for generalized canonical correlation analysis.

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to 3 or more sets of variables. RGCCA is a component-based approach which aims to study the relationships between several sets of variables. The quality and interpretability of the RGCCA components are likely to be affected by the usefulness and relevance of the variables in each block.

Compressed sensing dynamic reconstruction in rotational angiography.

This work tackles three-dimensional reconstruction of tomographic acquisitions in C-arm-based rotational angiography. The relatively slow rotation speed of C-arm systems involves motion artifacts that limit the use of three-dimensional imaging in interventional procedures. The main contribution of this paper is a reconstruction algorithm that deals with the temporal variations due to intra-arterial injections.

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares.

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions.

Graph constrained discriminant analysis: a new method for the integration of a graph into a classification process.

Integrating gene regulatory networks (GRNs) into the classification process of DNA microarrays is an important issue in bioinformatics, both because this information has a true biological interest and because it helps in the interpretation of the final classifier. We present a method called graph-constrained discriminant analysis (gCDA), which aims to integrate the information contained in one or several GRNs into a classification procedure. We show that when the integrated graph includes erroneous information, gCDA's performance is only slightly worse, thus showing robustness to misspecifications in the given GRNs.

Compressed sensing based 3D tomographic reconstruction for rotational angiography.

In this paper, we address three-dimensional tomographic reconstruction of rotational angiography acquisitions. In clinical routine, angular subsampling commonly occurs, due to the technical limitations of C-arm systems or possible improper injection. Standard methods such as filtered backprojection yield a reconstruction that is deteriorated by sampling artifacts, which potentially hampers medical interpretation.

Over-optimism in bioinformatics: an illustration.

Motivation: In statistical bioinformatics research, different optimization mechanisms potentially lead to 'over-optimism' in published papers. So far, however, a systematic critical study concerning the various sources underlying this over-optimism is lacking.

Results: We present an empirical study on over-optimism using high-dimensional classification as example.