Coronary artery calcification (CAC) could assist in the discovery of new risk elements for coronary artery disorder. CAC evaluation, on the other hand, is difficult due to the wide range of CAC in the populations. As a reason, evaluating and analysing data among research have become complicated. In the Research of Inherited Risk Factors for Coronary Atherosclerosis, we used CAC information to test the effects of different analytical methodologies on the correlation with recognized cardiovascular risk elements in asymptomatic patients. Cardiac computed tomography (CT) is also seeing an increase in examinations, and machine learning (ML) could assist with the growing amount of extracted data. Furthermore, there are other sectors in cardiac CT where machine learning could be crucial, including coronary calcium scoring, perfusion, and CT angiography. The establishment of risk evaluation algorithms based on information from CAC utilizing machine learning could assist in the categorization of patients undergoing cardiovascular into distinct risk groups and effectively adapt their treatments to their unique situations. Our findings imply that for forecasting CVD occurrences in asymptomatic people, age-sex segmentation by CAC percentile rank is as effective as absolute CAC scoring. Longitudinal population-based investigations are currently underway and would offer further definitive findings. While machine learning is a strong technology with a lot of possibilities, its implementations in the domain of cardiac CAC are generally in the early stages of development and are not currently commonly accessible in medical practise because of the requirement for substantial verification. Enhanced machine learning will, however, have a significant effect on cardiovascular and coronary artery calcification in the upcoming years.

e18549 Background: Ewing sarcoma (ES) is the second most common malignant bone tumor in children and young adults. Although usually localized, subclinical metastatic disease is often present. Treatment includes multi-agent chemotherapy with surgery or radiation for local control. Though overall survival (OS) has improved to 70% for localized disease, mortality from recurrent or metastatic disease remains high. Although, ES is most common among Non-Hispanic White adolescent males, prior population-based ES studies utilizing Surveillance, Epidemiology, and End Results (SEER) have shown increased mortality among White Hispanics, Blacks, and those of low socioeconomic status (SES). Florida is not part of SEER but is home to a unique population with high proportions of immigrants and Hispanics of distinct backgrounds including Cubans, Puerto Ricans, and South Americans that contrasts to the Mexican Hispanic majority in other US states. As there are no prior population-based studies examining ES in Florida, the role of racial/ethnic disparities on outcomes among this diverse patient population remains unknown. By utilizing the Florida Cancer Data System (FCDS) for ES patients, this study will assess overall racial/ethnic disparities, evaluate the impact of racial/ethnic disparities on OS, and compare ES incidence rates in Florida with national SEER-21 rates to assess risk. Methods: This retrospective secondary analysis examined all patients diagnosed with ES (2005-2018) in Florida (n = 411) and SEER-21 (n = 1,513). Age-adjusted incidence (AAI) with 95% confidence interval (95%CI) was analyzed for the entire group and OS was estimated using Kaplan-Meier survival analysis. Univariable and multivariable analyses using Cox regression models were performed for variables of interest—race/ethnicity, age, sex, year of diagnosis, site of disease, staging, SES, and insurance type. Adjusted hazard ratio (aHR) with 95%CI were calculated. Results: Per the FCDS, there was a higher incidence of ES in Hispanic males (AAI 2.6; 95%CI: 2.0-3.2 per 1,000,000; n = 84) as compared to the SEER (1.2; 1.1-1.4; n = 250). Among non-metastatic ES, Hispanics had an increased risk for cancer-related mortality compared to Non-Hispanic Whites (aHR 2.9; 95%CI: 1.46-5.74; p =0.002), after adjusting for all other variables. In the full model (all cases regardless of stage at diagnosis), older age and distant metastasis were statistically significant factors for poor OS while SES and insurance status were not. Conclusions: Hispanics in Florida are of interest for ES with a higher-than-expected incidence compared to the US and remarkably worse survival for non-metastatic disease compared to Non-Hispanic Whites. Further in-depth studies are needed to examine why this disparity exists, but it is potentially multifactorial. This is the first known study for ES health disparities in Florida and provides updated national data for comparison.