Cardiac amyloidosis: a texture issue. a proof of concept study on quantification of granular sparkling in patients with transthyretin-related cardiac amyloidosis using radiomics and granulometry
Authors: Mori S , Montobbio N , Sormani M P , Campi C , Cameli M , Cappelli F , Mazzoni C , Mandoli G E , Vianello P F , Zanoletti M , Ginetti F R , Porto I , Canepa M 
Published: October 2024

Abstract

Background
Granular sparkling is a well-known echocardiographic feature found in patients with transthyretin-related cardiac amyloidosis (ATTR-CA). However, there is no objective technique for quantifying this feature, which therefore remains a qualitative, elusive and ultimately unreliable imaging characteristic. Recent evidence suggests that radiomics and, in particular, ultrasonomics could play an important role in the non-invasive tissue characterization of cardiomyopathies through the study of specific myocardial texture features.

Purpose
The aim of this study is to statistically and geometrically characterize granular sparkling as a volume-independent texture property of the myocardium in patients with ATTR-CA.

Methods
We retrospectively collected echocardiogram video-clips in parasternal long axis (PLAX) and 4-chamber (4CH) views of 229 patients with ATTR-CA, and 224 age- and gender-matched hypertensive patients without any known cardiac disease. From each video-clip, one end-diastole frame was extracted and annotated by an expert to identify a region-of-interest (ROI) within the interventricular septum. Left ventricle chamber masks were also extracted, and used as a brightness reference to enforce invariance w.r.t. the settings of the specific ultrasound system (Fig. 1). Because many established radiomic textural features are heavily volume-confounded, we analyzed the ROI texture by extracting a subset of volume-invariant radiomic features (i.e. ROI-independent), as well as morphological granulometry features. We then fitted a logistic regression classifier to discriminate between ATTR-CA and controls based on the computed textural features.

Results
94 radiomic features were identified, of which 73 were volume-invariant. The texture-based classifier predicted the diagnosis with a cross-validated accuracy of 91%, a specificity of 90% and a sensitivity of 92% by utilizing 73 volume-invariant radiomic features on both PLAX and 4CH views (Fig. 2). The addition of granulometry and the further addition of 11 volume-confounded features and 9 shape features to the model did not significantly enhance its discriminative performance (Fig. 2). The top 10 volume-invariant discriminative features were largely consistent in PLAX and 4CH views (Fig. 2), with variables descriptive of greater heterogeneity (such as glszm_ZoneEntropy and glszm_RunEnNtropy) and a higher spatial rate of change (such as ngtdm_Complexity) in the texture of ATTR-CA frames as compared to control frames.

Conclusions
Our results confirm the ability of radiomics in detecting subtle differences in the myocardial texture of echocardiographic images between patients with ATTR-CA and hypertensive controls. High levels of accuracy are obtained with a purely texture-based, volume-independent set of features. These findings suggest that the diagnosis of ATTR-CA could be simplified and made earlier using a statistical model based on the extraction of radiomic features.Figure 1Figure 2