Quantification of Abdominal Aortic Calcification on CT: Clinical Validation for Assessment Of Cardiovascular Risk in Oncology

Non-cancer mortality (NCM) accounts for a substantial proportion of deaths in cancer survivors, with cardiovascular disease (CVD) being the leading cause. Abdominal aortic calcification (AAC) is a strong predictor of cardiovascular and all-cause mortality but remains underused due to the burden of manual scoring. Automated pipelines could enable opportunistic CVD risk screening from routine oncology CTs. We benchmarked three widely available AAC tools against manual reference standards in a multi-institutional prostate cancer cohort. We retrospectively analysed staging CTs from 99 men in the control arm of the STAMPEDE trial. Manual AAC was quantified using adaptive thresholding and Agatston scoring. Automated AAC scoring was performed using OSCAR (institutional agreement), Comp2Comp (open-source), and DAFS (commercial licence). Agreement with manual reference was assessed using Pearson correlation (r), intraclass correlation coefficients (ICC), Bland-Altman analyses, and categorical risk concordance (Cohen's κ). Manual scoring was highly reproducible (ICC = 0.99) but required > 12 min per scan. Automated pipelines reduced processing to < 5 min. OSCAR achieved the strongest agreement with manual AAC (r = 0.92, κ = 0.93), followed by DAFS (r = 0.88, κ = 0.91) and Comp2Comp (r = 0.75, κ = 0.74). Volumetric measures were reproducible across all tools (r ≥ 0.89). Failure occurred in < 10% of scans, mainly at slice thickness < 1 mm. OSCAR and DAFS were stable across patient and scan factors, whereas Comp2Comp was more sensitive to acquisition parameters. Automated AAC quantification is accurate, reproducible, and significantly faster than manual scoring. These findings support its role in cardiovascular screening for cancer and cancer treatment-related risk in oncology using routine CT scans.