JACC. Advances · 2025
Echocardiographic Tissue Characterization Using Radiomics in Patients With Transthyretin-Related Cardiac Amyloidosis.
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Mori Sara, Montobbio Noemi, Sormani Maria Pia, Campi Cristina, Mazzoni Carlotta, Argirò Alessia, Mandoli Giulia Elena, Ginetti Francesca Rubina, Zanoletti Margherita, Vianello Pier Filippo, Rella Valeria, Crotti Lia, Piana Michele, Cameli Matteo, Cappelli Francesco, Porto Italo, Badano Luigi Paolo, Canepa Marco
Аннотация
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.
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.