Echocardiographic Tissue Characterization Using Radiomics in Patients With Transthyretin-Related Cardiac Amyloidosis - Sara Mori, MD; Noemi Montobbio, MSc; Maria Pia. Sormani, MSc, PhD; Cristina Campi, MSc, PhD; Carlotta Mazzoni, MD; Alessia Argirò, MD, PhD; Giulia Elena Mandoli, MD; Francesca Rubina Ginetti, MD; Margherita Zanoletti, MD; Pier Filippo Vianello, MD; Valeria Rella, MD; Lia Crotti, MD, PhD; et al. (JACC: Advances)
Background: Transthyretin-related cardiac amyloidosis (ATTR-CA) is often diagnosed at an advanced stage. Emerging evidence suggests that radiomics applied to echocardiographic images (ie, ultrasonomics) can detect early myocardial texture changes in ATTR-CA. Objectives: This study aimed to develop a radiomic model for characterizing ATTR-infiltrated myocardium via echocardiography. Methods: Echocardiographic images in parasternal long-axis and apical 4-chamber views from ATTR-CA and control patients were collected across 4 Italian centers. A region of interest (ROI) within the interventricular septum was delineated. Ninety-four radiomic features were extracted and classified into 2 categories for analysis, based on whether they were ROI-dependent or independent. Five logistic regression models analyzed data from 3 centers (229 ATTR-CA, 224 controls) to assess diagnostic accuracy and area under the curve (AUC) of different sets of radiomic features, with external validation conducted on patients from a fourth center (32 ATTR-CA, 32 controls). Results: Models analyzing the entire ROI using both ROI-dependent and ROI-independent features demonstrated high cross-validated accuracies (93%-95%) and AUC values (0.97-0.99). Using a fixed-size 0.5 × 0.5 cm ROI, these values decreased to 85% and 0.91, respectively, highlighting previous models' dependence on ROI size. The fifth model used 73 ROI-independent features on the entire ROI and demonstrated significantly better accuracy and AUC (92% and 0.97, respectively, P < 0.001), confirmed in the external validation cohort (87% and 0.95, respectively). Removing the least informative features slightly improved the model, achieving 90% accuracy and 0.95 precision. Conclusions: This study showcases ultrasonomics potential to differentiate ATTR-CA and control patients by capturing disease-specific textural features independent of ROI dimensions.