Computed tomography-derived myocardial radiomics for detection of transthyretin amyloidosis in patients with severe aortic stenosis.

Abstract

Background: We explored the value of myocardial radiomics by computed tomography angiography (CTA) for detection of transthyretin amyloidosis cardiomyopathy (ATTR-CM).

Methods: The study included 589 patients with aortic stenosis and CTA datasets. Radiomics were extracted from LV myocardium. Arm 1 (n = 400) served for method optimisation and removal of redundant features. In Arm 2 (n = 30), we identified radiomics associated with extracellular volume by CT (ECV_CT); in Arm 3 (n = 159), radiomics were compared in patients with/without positive bone scintigraphy scan (training cohort, n = 84; validation cohort, n = 75) to build a radiomic signature for ATTR-CM.

Results: In Arm 1, unsupervised clustering of patients based on radiomics was associated with significant differences in patients' clinical profile among clusters. In Arm 2, we constructed a radiomic-based ECV (correlation with ECV_CT: rho = .78, p = 1.2 x 10^-6) with excellent diagnostic accuracy for high ECV_CT (AUC = .925, 95%CI: .825-1.000, p = .0002). In Arm 3, a radiomic score (AmyloidRS) had good performance for ATTR-CM detection in the training (c-index .88, 95%CI: .80-.95) and validation cohort (c-index .84, 95%CI: .69-.98). When combined with clinical features, AmyloidRS maximised diagnostic accuracy for ATTR (kappa: .894, balanced accuracy .984).

Conclusions: We present a radiomic method for myocardial tissue characterisation in patients with severe aortic stenosis which enables ATTR-CM detection from standard CTA scans.