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Unraveling the complexity of hypertrophic cardiomyopathy: a machine learning-based radiomics model to predict phenotype and cardiovascular events
Session:
Comunicações Orais - Sessão 13 - Miocardiopatias
Speaker:
Inês Miranda
Congress:
CPC 2024
Topic:
F. Valvular, Myocardial, Pericardial, Pulmonary, Congenital Heart Disease
Theme:
17. Myocardial Disease
Subtheme:
17.3 Myocardial Disease – Diagnostic Methods
Session Type:
Comunicações Orais
FP Number:
---
Authors:
Inês Pereira De Miranda; Miguel Marques Antunes; Vera Ferreira; Mara Sarmento; Filipa Gerardo; Mariana Passos; Carolina Pereira Mateus; Joana Lima Lopes; Sílvia Aguiar Rosa; João Bicho Augusto
Abstract
<p style="text-align:justify"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:14px"><span style="color:#000000"><strong>Background: </strong>Late gadolinium enhancement (LGE) imaging from cardiovascular magnetic resonance (CMR) plays a crucial role in the diagnosis and risk stratification of patients with hypertrophic cardiomyopathy (HCM). However, acquisition time can be long and some patients have contraindication to paramagnetic contrast. Simple, fast and widely available non-contrast single-shot fast spin echo black-blood (SS-FSE-BB) sequences can provide insights into the ultrastructure of the myocardium in HCM.</span></span></span></p> <p style="text-align:justify"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:14px"><span style="color:#000000"><strong>Objective: </strong>To assess radiomic models based on left ventricle (LV) images from SS-FSE-BB sequences and (1) compare with other imaging characteristics of HCM, particularly LGE, and (2) predict the risk of cardiovascular events in HCM patients.</span></span></span></p> <p style="text-align:justify"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:14px"><span style="color:#000000"><strong>Methods</strong>: We conducted a single-center study on 52 HCM patients who underwent CMR. SS-FSE-BB is widely used and acquired as a transaxial stack in beginning of the scan (without contrast), with <60s acquisition time. We excluded patients with poor imaging quality or missing this CMR sequence. The LV was segmented using a semi-automated tool with minimal input from the user. A total of 107 radiomics features were extracted using the PyRadiomics v3.1.0 library which included first-order, textural, shape and size. The primary endpoint was a composite of major adverse cardiac and cerebrovascular events (MACCE) defined as all-cause death, admission for acute/decompensated heart failure, malignant arrhythmia, cardiac syncope, myocardial infarction, ischemic stroke and/or complete heart block. We first conducted correlation analyses with visualization of matrix, to exclude redundant/correlated features. Then we implemented a Machine Learning Random Forest Classifier (ML-RFC) model to explore the HCM phenotype and cardiovascular events.</span></span></span></p> <p style="text-align:justify"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:14px"><span style="color:#000000"><strong>Results: </strong>A total of 46 CMR scans were suitable for analysis (age 60±15years, 43.5% female). 56.5% of patients had septal HCM, 15.2% had apical HCM and 8.7% had mixed disease. LV obstruction at rest was present in 14 (30.4%) patients, 40 (87%) had LGE, and MACCE occurred in 31 (67.4%) patients. The ML-RFC models for both LGE and MACCE are presented in the Figure. Longer LV least axis length (a shape feature that reflects the smallest axis length) was the only radiomics feature significantly associated with LGE (higher in patients with LGE, median 67.9 [IQR 57.8-67.9] vs 60.5 [57.7-63.2], p=0.0106). Patients who experienced MACCE displayed myocardial LV texture features of heterogeneity, with higher Dependence Non-Uniformity (728 [590-1029] vs 566 [482-711], p=0.0191]) than patients without MACCE; other factors were not significant. </span></span></span></p> <p style="text-align:justify"><span style="font-family:Arial,Helvetica,sans-serif"><span style="font-size:14px"><span style="color:#000000"><strong>Conclusion: </strong>Radiomic features in simple, fast and widely available SS-FSE-BB sequences provide relevant information in HCM patients in less than a minute of CMR scan, without the need for paramagnetic contrast. This tool could simplify current CMR protocols and improve risk stratification in HCM.</span></span></span></p>
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