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.

Keywords: CCTA; atherosclerosis; radiomics; repeatability; reproducibility.

Figure 1

Radiomics Process.

Figure 2

Overall research workflow.

Figure 3

The framework of the research protocol.

Figure 4

LIFEx software was used to perform manual and semiautomatic segmentation on RCA, LAD, and LCX. (a) Manual segmentation using pencil 2D technique with soft-tissue-windowing setting on RCA (b) The area of segmentation covered after manual segmentation with soft-tissue-windowing setting was performed on RCA.

Figure 5

Semiautomatic segmentation using circle 3D technique in LIFEx software in bone window setting on the coronary blood vessel.

Figure 6

Interclass Correlation Coefficients (inter-CC) between manual and semi-automatic segmentation for 1st order features including the shape features to compare its reproducibility.

Figure 7

Intraclass Correlation Coefficient (intra-CC) between manual and semi-automatic segmentation for 1st order Features including the Shape Features to compare its repeatability.

Figure 8

Interclass Correlation Coefficient (inter-CC) between manual and semi-automatic segmentation for 2nd order features to compare its reproducibility.

Figure 9

Intraclass Correlation Coefficient (inter-CC) between manual and semi-automatic segmentation for 2nd order features to compare its repeatability.

Figure 10

Repeatability and reproducibility of manual and semi-automatic segmentation for the first-, shape and second-order features. The (*) shows the highest repeatability and reproducibility mean score with a significant p-value (p < 0.001).