Machine Learning of Echocardiographic Left Ventricular Myocardial Radiomics for Identification of Transthyretin Amyloid Cardiomyopathy.

@article{elzieny2026machine,
  title = {{Machine Learning of Echocardiographic Left Ventricular Myocardial Radiomics for Identification of Transthyretin Amyloid Cardiomyopathy.}},
  author = {Ali A Elzieny and Rabah Alreshq and Ikram Ullah and Denise Fine and Sergio Teruya and Stephen Helmke and Cesia Gallegos and Edward J Miller and Jeremy A Slivnick and Mathew S Maurer and Baojun Li and Frederick L Ruberg},
  journal = {JACC. Advances},
  year = {2026},
  month = oct,
  doi = {10.1016/j.jacadv.2026.103336},
  url = {https://europepmc.org/article/MED/42868962},
  note = {CC BY-NC-ND},
}

TY  - JOUR
TI  - Machine Learning of Echocardiographic Left Ventricular Myocardial Radiomics for Identification of Transthyretin Amyloid Cardiomyopathy.
AU  - Ali A Elzieny
AU  - Rabah Alreshq
AU  - Ikram Ullah
AU  - Denise Fine
AU  - Sergio Teruya
AU  - Stephen Helmke
AU  - Cesia Gallegos
AU  - Edward J Miller
AU  - Jeremy A Slivnick
AU  - Mathew S Maurer
AU  - Baojun Li
AU  - Frederick L Ruberg
PY  - 2026
DA  - 2026/10/13
JO  - JACC. Advances
DO  - 10.1016/j.jacadv.2026.103336
UR  - https://europepmc.org/article/MED/42868962
AB  - Background  Transthyretin amyloid cardiomyopathy (ATTR-CM) is an important cause of heart failure (HF) with increased left ventricular (LV) wall thickness that can be challenging to distinguish from other LV hypertrophic processes. Echocardiographic myocardial radiomic texture analysis may identify imperceptible amyloid-induced structural changes.Objectives  The study aims to develop and validate an echocardiography-based machine learning model using myocardial radiomics to identify ATTR-CM.Methods  In this retrospective and prospective multicenter study, 854 echocardiographic scans were analyzed. Conventional and novel radiomic features were extracted from parasternal short-axis views at basal, mid, and apical LV levels, with incorporated age and sex from echocardiogram metadata. A machine learning pipeline was developed using a balanced training set (150 ATTR-CM, 150 nonamyloid HF). Performance was evaluated on an independent internal validation set (50 ATTR-CM, 399 nonamyloid HF) and an external validation set (10 ATTR-CM, 10 immunoglobulin light-chain-amyloid, 10 hypertrophic cardiomyopathy, 75 nonamyloid HF).Results  In internal validation, the model achieved 86% sensitivity (95% CI: 73.8%-93%), 92% specificity (95% CI: 88.9%-94.3%), 57.3% positive predictive value (95% CI: 46.1%-67.9%), 98.1% negative predictive value (NPV) (95% CI: 96.2%-99.1%), and receiver operating characteristic-area under the curve 0.938 (95% CI: 0.891-0.985). Performance was consistent in the external validation (100% sensitivity, 78.9% specificity, 33.3% positive predictive value, 100% NPV, and receiver operating characteristic-area under the curve of 0.917). The model correctly classified 77.3% of nonamyloid HF, 70% of AL-amyloid cases, and 100% of hypertrophic cardiomyopathy cases as non-ATTR in subgroup analysis.Conclusions  We developed an echocardiographic radiomics-based tool to identify ATTR-CM with excellent discrimination and very high NPV in a cohort that approximates clinical experience. These preliminary findings require validation in larger, more diverse cohorts.
SN  - 2772-963X
ER  -