ArticleBMC cardiovascular disorders2026
Validation of aortic valve calcification quantification on contrast-enhanced computed tomography against ex vivo gravimetric analysis: comparison of fixed Hounsfield unit thresholds and deep learning segmentation.
Article in BMC cardiovascular disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAccurate quantification of aortic valve calcification (AVC) on contrast-enhanced computed tomography angiography (CTA) is pivotal for planning surgical and transcatheter aortic valve replacement. The optimal Hounsfield unit (HU) threshold for calcification detection on contrast-enhanced images remains unresolved, and every prior validation study has relied on non-contrast Agatston scoring-itself an imaging estimate-as the reference standard. This study validated two widely used fixed HU thresholds (450 HU and 850 HU) and a self-configuring nnU-Net deep learning model against ex vivo gravimetric calcium weight as an absolute physical ground truth.
methodsFour hundred patients were included in a retrospective cohort study with a pre-specified temporal validation split: 300 with CT-confirmed AVC and 100 with normal aortic valves. Fifty chronologically later AVC patients who underwent elective open surgical aortic valve replacement (SAVR) within seven days of clinically indicated pre-operative contrast-enhanced CTA formed the locked surgical validation cohort; their excised native leaflets underwent standardised high-temperature ashing (550 °C, 12 h) and analytical weighing (precision 0.1 mg) to obtain gravimetric calcium mass. The remaining 350 cases served exclusively for nnU-Net development (280 training / 70 internal validation). CT-derived calcium mass-equivalent estimates were quantified on the validation cohort and compared with gravimetric weight using Pearson and Spearman correlation and Bland-Altman analysis.
resultsThe nnU-Net achieved the strongest observed correlation with gravimetric weight (Pearson r = 0.967; bias + 6.2 mg; RMSE 13.7 mg), significantly outperforming the 450 HU threshold for correlation (r = 0.864; bias + 36.2 mg; RMSE 42.1 mg; Steiger p < 0.001) and showing a non-significant trend toward stronger correlation than 850 HU (r = 0.929; bias + 17.5 mg; RMSE 23.9 mg; Steiger p = 0.085). Compared with 850 HU, nnU-Net provided lower bias and RMSE, although the difference in Pearson r did not reach statistical significance. The 450 HU method exhibited significant proportional bias (p = 0.024), whereas neither 850 HU nor nnU-Net did. The nnU-Net achieved a mean Dice coefficient of 0.873 and intersection-over-union of 0.812.
conclusionsAgainst physically weighed calcium, nnU-Net deep learning segmentation provided the most favourable overall performance profile on contrast-enhanced CTA, with the lowest bias and RMSE and the strongest observed correlation. The improvement in Pearson correlation over 850 HU represented a non-significant trend, whereas the error and agreement metrics favoured nnU-Net. Among fixed thresholds, 850 HU substantially outperformed 450 HU, offering direct physical-rather than surrogate imaging-evidence to support 850 HU as the preferred fixed threshold in standard contrast-enhanced protocols.
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