Bone Scintigraphy in Cardiac Transthyretin-Related Amyloidosis: A Novel Time-Saving Tool for Semiquantitative Analysis, with Good Potential for Predicting Different Etiologies

Abstract

(1) Background: The visual and semiquantitative analysis of Technetium-99metastable-3,3-diphospono-1,2-propanodicarboxylic acid (99mTc-DPD) bone scintigraphy is promising for diagnosing cardiac amyloidosis but time-consuming. We validated a faster method, the geometric mean (GM) method with a semi-automated workflow, for heart–whole body (WB) ratio (H/WBr), heart retention (Hr), and WB retention (WBr) calculations compared to the classic method (CM) established in the literature. The capability of semiquantitative scintigraphy indexes to differentiate the etiology in transthyretin-related cardiac amyloidosis (cATTR) patients was investigated. (2) Methods: H/WBr, Hr, and WBr were calculated by extracting counts for WB, kidneys, bladder, and heart on early and late planar image scans and applying background, scan-time, and decay corrections, using CM and GM both on a referring workstation and on a semi-automated workflow in external software. The comparison between CM and GM was assessed with Pearson’s correlation, Lin’s Concordance Correlation Coefficient (CCC), and Bland–Altman analysis. H/WBr, Hr, and WBr and several clinical variables were used to implement LASSO, Random Forest (RF), and Neural Network (NN) models to predict mutated and wild-type ATTR etiologies. ROC curves and AUC were calculated. (3) Results: Hr, WBr, and H/WBr using CM and GM were highly correlated. Bland–Altman analysis between CM and GM showed biases of 0.12% [CI:0.04%;0.19%] for H/WBr, 0.07% [CI: 0.01%; 0.13%] for Hr, and -0.50% [CI: −1.22%; 0.22%] for WBr. LASSO and NN models had good performance in predicting etiologies with AUC values of 87.3% and 73.6%, respectively. The RF model showed a poorer AUC of 55.8%. (4) Conclusions: The GM in the assisted workflow was validated against the CM. LASSO and NN approaches allowed a good prediction performance to be obtained for patient etiology.

Keywords

• geometric mean method
• non-invasive imaging
• etiology
• predictive model
• cardiac amyloidosis