Citations

Abstract1q gain (+1q) is the most common high‐risk cytogenetic abnormality (HRCA) in patients with multiple myeloma (MM). However, its prognostic value remains unclear in the era of novel agents. Here, we retrospectively analyzed the impact of +1q on the outcomes of 934 patients newly diagnosed with MM. +1q was identified in 53.1% of patients and verified as an independent variate for inferior overall survival (OS) (hazard ratio, 1.400; 95% confidence interval, 1.097–1.787; p = .007). Concurrence of other HRCAs (particularly t(14;16) and del(17p)) further exacerbated the outcomes of patients with +1q, suggesting prognostic heterogeneity. Thus, a risk‐scoring algorithm based on four risk variates (t(14;16), hypercalcemia, ISS III, and high LDH) was developed to estimate the outcomes of patients with +1q. Of the patients, 376 evaluable patients with +1q were re‐stratified into low (31.6%), intermediate (61.7%), and high risk (6.7%) groups, with significantly different progression‐free survival and OS (p < .0001), in association with early relapse of the disease. The prognostic value of this model was validated in the CoMMpass cohort. While attaining undetectable MRD largely circumvented the adverse impact of +1q, it scarcely ameliorated the outcome of the patients with high risk, who likely represent a subset of patients with extremely poor survival. Hence, patients with +1q are a heterogeneous group of high‐risk patients, therefore underlining the necessity for their re‐stratification. The proposed simple risk‐scoring model can estimate the outcomes of patients with +1q, which may help guide risk‐adapted treatment for such patients.

Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one’s life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the left atrium fat tissue pattern using Radon descriptor-based machine learning. This study developed a bridge between the Radon transform framework and machine learning to distinguish two distinct patterns. Motivated by a Radon descriptor-based machine learning approach, the patches of eight patients from CT images of the heart were used and categorized into “epicardial fat tissue” and “nonfat tissue” groups. The 10 feature vectors are extracted from each big patch using Radon descriptors and then fed into a traditional machine learning model. The results show that the proposed methodology discriminates between fat tissues and nonfat tissues clearly. KNN has shown the best performance with 96.77% specificity, 98.28% sensitivity, and 97.50% accuracy. To our knowledge, this study is the first attempt to provide a Radon transform-based machine learning method to distinguish between fat tissue and nonfat tissue on the left atrium. Our proposed research method could be potentially used in advanced interventions.