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Reproducibility of Epicardial Adipose Tissue Radiomics in Non-contrast Computed Tomography
Session:
Comunicações Orais - Sessão 24 - Tomografia Computorizada Cardíaca
Speaker:
Fábio Sousa Nunes
Congress:
CPC 2023
Topic:
B. Imaging
Theme:
03. Imaging
Subtheme:
03.2 Computed Tomography
Session Type:
Comunicações Orais
FP Number:
---
Authors:
Fábio Sousa Nunes; Carolina Santos; Wilson Ferreira; Mónica Carvalho; João Pedrosa; Miguel Coimbra; Nuno Ferreira; Ricardo Ladeiras Lopes; Rita Faria; Luís Vouga; Jennifer Mancio; Ricardo Fontes Carvalho
Abstract
<p><strong>Introduction</strong>: Many factors can negatively impact radiomic features reproducibility, and, consequently, their diagnostic & prognostic accuracy. Although several deep learning solutions for automatic pericardial segmentation already exist, the impact of contouring variability on epicardial adipose tissue (EAT) radiomic features values is not known.<br /> <strong>Methods</strong>: We segmented the pericardium in 192 non-contrast CT scans manually by a trained operator and using the semi-automatic pericardial segmentation Syngo.via Frontier Cardiac Risk Assessment Research Prototype (Siemens Healthinees, Erlangen, Germany). The same operator repeated the segmentation in 20 random cases (intra-observer), which were also segmented by another operator with same level of training (inter-observer). Intraclass coefficient correlation (ICC) was used to measure the variability between EAT radiomic features extracted after segmentation by all the methods.<br /> <strong>Results</strong>: Manual segmentation rendered 961 (93%) and 699 (67%) features with a very good intra-observer and inter-observer ICC (>0.80), respectively. The inter-observer variability between manual vs. semi-automatic segmentation was not different: there were 692 (67%) features with ICC>0.80. Very good intra- & inter-observer ICC were found in 696 features obtained by manual segmentation (A) and 672 with manual vs. semi-automatic method (B). Full data analysis will provide a detailed description of the most reliable features per each feature family.<br /> <strong>Conclusions</strong>: We observed very good intra-and inter-observer ICC and similar results between the manual and semi-automatic segmentation methods. This study supports current recommendation for semi-automatic segmentation of large dataset and will yield a better understanding the most stable EAT radiomic features with a potential for clinical translation.<br /> </p>
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