<h3>Purpose/Objective(s)</h3> Cardiovascular (CV) disease is among the leading causes of death for patients with prostate cancer. One novel strategy for automated screening for CV disease is the use of opportunistic imaging biomarkers derived from CT simulation scans. This study aims to investigate association of abdominal aorto-iliac calcification burden as measured qualitatively by physicians and quantitatively using a deep learning approach, and occurrence of major adverse CV events (MACE) among prostate cancer patients undergoing SBRT. <h3>Materials/Methods</h3> A retrospective study was conducted of 146 patients undergoing SBRT for localized prostate cancer (median age 68.9 years [interquartile range, IQR, 63.6-74.0]; 23.2% low risk, 35.1% intermediate risk, 11.6% high risk) between June 2007 and August 2018. MACE was collected from review of the medical chart, defined as a composite event of stroke, myocardial infarction, hospitalization for new onset heart failure, and CV death. Two physicians who were blinded to outcomes (WCC, JDB) independently reviewed non-contrast CT simulation scans to assess aorto-iliac calcification burden by a pre-defined 5-point scale (weighted Kappa: 0.72). Next, the abdominal aorta and common iliac arteries were contoured for 50 scans which served as a training dataset. A multi-stage UNet was trained to delineate aorto-iliac contours, achieving a mean dice score of 0.87±0.08 and Hausdorff distance of 1.9±0.6mm. Calcification burden was quantified using a per-voxel threshold of >130 Houndsfield units. <h3>Results</h3> 20 patients (13.7%) experienced MACE during a cohort-wide median clinical follow up of 5.9 years (IQR 4.7-6.4), and 5 patients (3.4%) died of CV disease and 10 patients (6.8%) of other causes (1 prostate cancer related). 49 patients (33.5%) had moderate to severe CaBS of 3 or higher. CaBS was significantly positively correlated with MACE in a dose dependent manner (P<0.0001, Cochran-Armitage test for trend), increasing from 6.2% for patients with mild/absent CaBS, to 20.6%, 37.5%, and 57.1%, for CaBS of 3, 4 and 5, respectively. Quantitative calcification burden (CaQ)derived from auto-segmentation was correlated with CaBS (Pearson r=0.79, P<0.0001) and achieved a similar area under the curve (AUC 0.74 vs 0.73 for CaBS) for MACE, resulting in a sensitivity of 78%, specificity of 63%, negative predictive value of 94.8%, and positive predictive value of 24.6%, using a cut-off of 1650 mm^3. Finally, both CaBS and CaQ remained significantly associated with MACE after adjusting for age on Cox regression (P=0.04, CaQ: HR 1.15 per 1000mm^3, 95% CI 1.005-1.33). <h3>Conclusion</h3> Abdominal aorto-iliac calcification burden was strongly associated with cardiovascular risk among patients undergoing SBRT for prostate cancer. A deep learning approach to quantify calcification burden on non-contrast pelvic CT simulation scans identified patients at elevated cardiovascular risk and may facilitate automated screening.