Prediction of incident cardiovascular events using machine learning and CMR radiomics.

OBJECTIVES: Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. METHODS: We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. RESULTS: AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. CONCLUSIONS: Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. KEY POINTS: • Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases

Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers.

BACKGROUND: Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions. PURPOSE: To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region. STUDY TYPE: Cross-sectional. POPULATION: A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4). FIELD STRENGTH/SEQUENCE: A 1.5 T/balanced steady-state free precession. ASSESSMENT: An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49). STATISTICAL TEST: Multiple testing correction with false discovery method (threshold = 5%). RESULTS: The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10-26 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = -1.52, P = 7.44 × 10-6 ). DATA CONCLUSION: This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 1

The interest of the Spanish network of investigators in back pain for rehabilitation physician

Background: The Spanish Back Pain Research Network (REIDE) brings together teams of researchers and clinicians who are interested in nonspecific neck and back pain (BP). Its objective is to improve the efficacy, safety, effectiveness, and efficiency of the clinical management of BP. Method: The Network welcomes clinicians and researchers interested in BP. The only requirement to become a member of REIDE is to take part in one of its research projects, and any member can propose a new one. The Network supports those projects that are of interest to two or more groups by assuming their administration and management, which allows the researchers to focus on their task. Its working method ensures methodological quality, a multidisciplinary approach, and the clinical relevance of those projects that are carried out. Results: 179 researchers from 11 areas in Spain are involved in REIDE, including experts in all of the relevant fields of BP research. Most Spanish studies on BP that have been published in international scientific journals come from the teams involved in REIDE, and it currently has 13 ongoing research projects. Conclusions: The Network can help to enhance research among rehabilitation specialists who are interested in BP, and can contribute to the development of research projects which are of interest to the specialty. © 2005 Sociedad Española de Rehabilitación y Medicina Física (SERMEF) y Elsevier España, S.L

Systems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and "swarm intelligence"

Our knowledge of the biology of solid cancer has greatly progressed during the last few years, and many excellent reviews dealing with the various aspects of this biology have appeared. In the present review, we attempt to bring together these subjects in a general systems biology narrative. It starts from the roles of what we term entropy of signaling and noise in the initial oncogenic events, to the first major transition of tumorigenesis: the independence of the tumor cell and the switch in its physiology, i.e. from subservience to the organism to its own independent Darwinian evolution. The development after independence involves a constant dynamic reprogramming of the cells and the emergence of a sort of collective intelligence leading to invasion and metastasis and seldom to the ultimate acquisition of immortality through inter-individual infection. At each step, the probability of success is minimal to infinitesimal, but the number of cells possibly involved and the time scale account for the relatively high occurrence of tumorigenesis and metastasis in multicellular organisms.JOURNAL ARTICLESCOPUS: ar.jinfo:eu-repo/semantics/publishe