Clinical characteristics, imaging phenotypes and events free survival in Takayasu arteritis patients with hypertension.

BACKGROUND: Hypertension occurred in 30-80% of Takayasu arteritis (TAK) patients around the world and the occurrence of hypertension might worsen the disease prognosis. This study aimed to investigate the clinical characteristics and imaging phenotypes, as well as their associations with events free survival (EFS) in Chinese TAK patients with hypertension. METHODS: This current research was based on a prospectively ongoing observational cohort-the East China Takayasu Arteritis (ECTA) cohort, centered in Zhongshan Hospital, Fudan University. Totally, 204 TAK patients with hypertension were enrolled between January 2013 and December 2019. Clinical characteristics and imaging phenotypes of each case were evaluated and their associations with the EFS by the end of August 30, 2020, were analyzed. RESULTS: Severe hypertension accounted for 46.1% of the entire population. Three specific imaging phenotypes were identified: Cluster 1: involvement of the abdominal aorta and/or renal artery (27.5%); Cluster 2: involvement of the ascending aorta, thoracic aorta, the aortic arch, and/or its branches (18.6%); and Cluster 3: combined involvement of Cluster 1 and 2 (53.9%). Clinical characteristics, especially hypertensive severity, differed greatly among the three imaging clusters. In all, 187 patients were followed up for a median of 46 (9-102) months; 72 events were observed in 60 patients (1-3 per person). The overall blood pressure control rate was 50.8%, and the EFS was 67.9% by the end of the follow-up. Multivariate Cox regression indicated that controlled blood pressure (HR = 2.13, 95% CI 1.32-3.74), Cluster 1 (HR = 0.69, 95% CI 0.48-0.92) and Cluster 3 (HR = 0.72, 95% CI 0.43-0.94) imaging phenotype was associated with the EFS. Kaplan-Meier curves showed that patients with controlled blood pressure showed better EFS (p = 0.043). Furthermore, using cases with Cluster 1 imaging phenotype and controlled blood pressure as reference, better EFS was observed in patients with Cluster 2 phenotype and controlled blood pressure (HR = 2.21, 95%CI 1.47-4.32), while the case with Cluster 1 phenotype plus uncontrolled blood pressure (HR = 0.64, 95%CI 0.52-0.89) and those with Cluster 3 phenotype and uncontrolled blood pressure (HR = 0.83, 95%CI 0.76-0.92) suffered worse EFS. CONCLUSION: Blood pressure control status and imaging phenotypes showed significant effects on the EFS for TAK patients with hypertension.

Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics.

In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.

Synchronous Breast Cancer: Phenotypic Similarities on MRI.

BACKGROUND: Previous studies have shown discrepancies between index and synchronous breast cancer in histology and molecular phenotype. It is yet unknown whether this observation also applies to the MRI phenotype. PURPOSE: To investigate whether the appearance of breast cancer on MRI (i.e. phenotype) is different from that of additional breast cancer (i.e. synchronous cancer), and whether such a difference, if it exists, is associated with prognosis. STUDY TYPE: Retrospective. POPULATION: In all, 464 consecutive patients with early-stage ER+/HER2- breast cancer were included; 34/464 (7.3%) had 44 synchronous cancers in total (34 ipsilateral, 10 contralateral). SEQUENCE: 1.5T, contrast-enhanced T1 -weighted. ASSESSMENT: We assessed imaging phenotype using 50 quantitative features from each cancer and applied principal component analysis (PCA) to identify independent properties. The degree of phenotype difference was assessed. An association between phenotype differences and prognosis in terms of the Nottingham Prognostic Index (NPI) and PREDICT score were analyzed. STATISTICAL TESTS: PCA; Wilcoxon rank sum test; Benjamini-Hochberg to control the false discovery rate. RESULTS: PCA identified eight components in patients with ipsilateral synchronous cancer. Six out of eight were significantly different between index and synchronous cancer. These components represented features describing texture (three components, P < 0.001, P < 0.001, P = 0.004), size (P < 0.001), smoothness (P < 0.001), and kinetics (P = 0.004). Phenotype differences in terms of the six components were split in tertiles. Larger phenotype differences in size, kinetics, and texture were associated with significantly worse prognosis in terms of NPI (P = 0.019, P = 0.045, P = 0.014), but not for the PREDICT score (P = 0.109, P = 0.479, P = 0.109). PCA identified six components in patients with contralateral synchronous cancer. None were significantly different from the index cancer (P = 0.178, P = 0.178, P = 0.178, P = 0.326, P = 0.739, P = 0.423). DATA CONCLUSION: The MRI phenotype of ER+/HER2- breast cancer was different from that of ipsilateral synchronous cancer and a large phenotype difference was associated with worse prognosis. No significant difference was found for synchronous contralateral cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;51:1858-1867.

Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease.

BACKGROUND: The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. OBJECTIVE: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. METHODS: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. RESULTS: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. CONCLUSION: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.

Moreton Lecture: Imaging in the Age of Precision Medicine.

The term "precision medicine" (also known as "personalized medicine") is broadly defined as the tailoring of medical treatment to the individual characteristics of each patient. This process entails classifying patients into subpopulations that differ in their susceptibility to a particular disease, in the biology and/or prognosis of those diseases they may develop, or in their response to a specific treatment. Subpopulations are defined through systematic analysis and classification of patients' genotypic and phenotypic characteristics. Image findings are surrogates for phenotype manifestation of disease, and radiology reports are written descriptions of imaging phenotypes. Imaging phenotypes are often presented as classification, grading, or scoring systems that help assign patients to subpopulations for selecting treatment or assessing prognosis. The "spot sign score" that reflects the severity of bleeding in intracerebral hemorrhage is an example that has been used as an inclusion criterion in clinical trials. The term "radiogenomics" is used to describe the study of linkage between a patient's genotype and imaging phenotype. When a patient's genotype is known, it often suggests a surveillance role for imaging to determine clinical occurrence, location, extent, and severity of the associated disease; for example, use of breast imaging for enhanced surveillance in women known to harbor the BRCA1 and BRCA2 genes. Imaging is poised to play major roles in the age of precision medicine. The imaging community needs to learn new terminology and think in terms of how imaging phenotypes and imaging surveillance of patients with known genetic mutations can contribute to the concept.

Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.

We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1,071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI).