We validate a method of calcium scoring on CT coronary angiography (CTCA) and propose an algorithm for the assessment of patients with stable chest pain. 503 consecutive patients undergoing coronary artery calcium score (CACS) and CTCA were included. A 0.1 cm² region of interest was used to determine the mean contrast density on CTCA images either in the left main stem (LM) or right coronary artery. Axial 3 mm CTCA images were scored for calcium using conventional software with a modified threshold: mean LM contrast density (HU) + 2SD. A conversion factor (CF) for predicting CACS from raw CTCA scores (rCTCAS) was determined using a multivariable regression model adjusted for model over-optimism (1,000 bootstrap samples). Accuracy of this method was determined using weighted kappa for NICE recommended CACS groupings (0, 1–400, >400) and Bland–Altman analysis for absolute score. With the CF applied: CACS = (1.183 × rCTCAS) + (0.002 × rCTCAS × threshold), there was excellent agreement between methods for absolute score (mean difference 5.44 [95 % limits of agreement −207.0 to 217.8]). The method discriminated between high (>400) and low risk (<400) calcium scores with a sensitivity and specificity of 85 and 99 %, and a PPV and NPV of 92 and 98 %, respectively, and led to a significant reduction in radiation exposure (6.9 [5.1–10.2] vs. 5.2 [6.3–8.7] mSv; p < 0.0001). Our proposed method allows a comprehensive assessment of coronary artery pathology through the use of an individualised, semi-automated approach. If incorporated into stable chest pain guidelines the need for further functional testing or invasive angiography could be determined from CTCA alone, supporting a change to the current guidelines.