Reproducibility and Repeatability of Coronary Computed Tomography Angiography (CCTA) Image Segmentation in Detecting Atherosclerosis: A Radiomics Study

Abstract

Atherosclerosis is known as the leading factor in heart disease with the highest mortality rate among the Malaysian population. Usually, the gold standard for diagnosing atherosclerosis is by using the coronary computed tomography angiography (CCTA) technique to look for plaque within the coronary artery. However, qualitative diagnosis for noncalcified atherosclerosis is vulnerable to false-positive diagnoses, as well as inconsistent reporting between observers. In this study, we assess the reproducibility and repeatability of segmenting atherosclerotic lesions manually and semiautomatically in CCTA images to identify the most appropriate CCTA image segmentation method for radiomics analysis to quantitatively extract the atherosclerotic lesion. Thirty (30) CCTA images were taken retrospectively from the radiology image database of Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, Malaysia. We extract 11,700 radiomics features which include the first-order, second-order and shape features from 180 times of image segmentation. The interest vessels were segmentized manually and semiautomatically using LIFEx (Version 7.0.15, Institut Curie, Orsay, France) software by two independent radiology experts, focusing on three main coronary blood vessels. As a result, manual segmentation with a soft-tissuewindowing setting yielded higher repeatability as compared to semiautomatic segmentation with a significant intraclass correlation coefficient (intra-CC) 0.961 for thefirst-order and shape features; intra-CC of 0.924 for thesecond-order features with p < 0.001. Meanwhile, the semiautomatic segmentation has higher reproducibility as compared to manual segmentation with significant interclass correlation coefficient (inter-CC) of 0.920 (first-order features) and a good interclass correlation coefficient of 0.839 for the second-order features with p < 0.001. The first-order, shape order and second-order features for both manual and semiautomatic segmentation have an excellent percentage of reproducibility and repeatability (intra-CC > 0.9). In conclusion, semi-automated segmentation is recommended for inter-observer study while manual segmentation with soft tissue-windowing can be used for single observer study.

Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images

“…The studies [47][48][49][50] proposed a method for detecting CAD markers from the CCTA images. They applied image segmentation and pixel enhancement techniques to enhance the CCTA image quality.…”

“…It improves blood vessel visibility and recognition in angiograms and other vascular imaging modalities. Similarly, the Sato filter enhances tubular or vessel-like features in medical images [47]. It uses Hessian matrix eigenvalues to represent local second-order intensity fluctuations in an image.…”

Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer

“…Meanwhile, reproducibility is used to assess the consistency of results when different segmentation techniques are employed on the same imaging data [ 14 ]. As the output varied in terms of consistency, there has been a rise in research investigating the repeatability and reproducibility of radiomics characteristics [ 15 ]. The test-retest method serves as a crucial measure of feature repeatability, derived from images of the same patient that were obtained within a relatively brief time frame [ 16 ].…”

Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

“…The component of radiomic represents high quantitative image features of tumor phenotypes that characterize the volumes of interest. The feature extraction contains information from input images and represents data in lower dimensional space [ 7 , 8 , 9 ]. This involves a complex mathematical algorithm which describes phenotypes of tumors that are unrecognized and might not be detectable by human observation.…”