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Prediction of Abdominal Aortic Aneurysm Growth by Automatic Segmentation and Radiomics Feature Quantification


An accurate assessment of abdominal aortic aneurysm (AAA) progression is essential to its clinical management. Currently, the maximum diameter of AAA at diagnosis is considered as the primary indicator of rupture risk. However, it is not optimal as rupture can happen at any size. Several patient-specific factors may also influence AAA rupture risk. Given the clinical variability in aneurysm progression, additional prognostic markers are desirable to enhance patient-specific risk stratification. Radiomics is an image processing technique that extracts quantitative and high-dimensional features from medical images. While it has emerged as a novel approach for solving diagnosis in oncology, its application in cardiovascular diseases is still limited.

This study set out with an aim to determine the feasibility of radiomics in identifying AAA with a fast growth rate (>0.3cm/year) using CT images. An automatic AAA segmentation algorithm was developed in our pipeline. Based on the radiomics features of an 84 CT dataset, supervised classification models were implemented with two feature selection algorithms and two classifiers in a machine-learning framework. An AUC of 0.80 was achieved and the predictive power was proved through comparisons to the maximum diameter and conventional risk factors. Further multivariate analysis suggested that a radiomics-based classification model could be used as an independent, yet strong predictor for fast AAA growth rate.

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