Enhanced Radiomics for Prediction of Rupture Status in Cerebral Aneurysms.

BACKGROUND

Radiomics is a powerful tool for automatic extraction of morphological features, but when applied to cerebral aneurysms, it is inferior to established descriptors in classifying rupture status. We sought a strategy to recover neck orientation and parent vessel caliber to enhance Radiomics performance and facilitate its adoption for aneurysm risk stratification.

METHODS

We analyzed 135 sidewall (32 ruptured) and 216 bifurcation (90 ruptured) aneurysms from three-dimensional rotational catheter angiography datasets. Clinical three-dimensional rotational catheter angiography defined in arbitrary orientation underwent affine transformations enabling aneurysm neck alignment to XY plane before analysis in PyRadiomics, facilitating automatic extraction of aneurysm height and width, previously not possible with random alignment. Additionally, parent vessel size was estimated from aneurysm location and incorporated into enhanced Radiomics (height, width, height/width, size ratio). Rupture status classification was compared across methodologies for 31 automatically computed conventional Radiomics, enhanced Radiomics, and established size/shape descriptors using univariate, multivariate, and area under the curve (AUC) statistics.

RESULTS

Enhanced Radiomics-derived height/width and size ratio were significantly higher in both ruptured subsets. Using multivariate analysis in sidewall lesions, enhanced Radiomics (AUC = 0.85) matched established features (AUC = 0.86) and outperformed conventional Radiomics (AUC = 0.82); in bifurcation lesions, enhanced Radiomics (AUC = 0.78) outperformed both established features (AUC = 0.76) and conventional Radiomics (AUC = 0.74).

CONCLUSIONS

Enhanced Radiomics incorporating neck orientation and parent vessel estimate is an efficient operator-independent methodology that offers superior rupture status classification for both sidewall and bifurcation aneurysms and should be considered a strong candidate for larger-scale multicenter and multimodality validation.