A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair.
Authors
Affiliations (6)
Affiliations (6)
- National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China; Department of Ultrasound, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
- Department of Vascular Surgery, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Department of Vascular Surgery, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China; Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Electronic address: [email protected].
- National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China. Electronic address: [email protected].
- National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China. Electronic address: [email protected].
Abstract
This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to predict the prognosis of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) patients. In this retrospective study, 164 AAA patients underwent EVAR and were categorized into shrinkage (good prognosis) or stable (poor prognosis) groups based on post-EVAR sac regression. From preoperative AAA and perivascular adipose tissue (PVAT) image, radiomics features (RFs) were extracted for model creation. Patients were split into 80 % training and 20 % test sets. A support vector machine model was constructed for prediction. Accuracy is evaluated via the area under the receiver operating characteristic curve (AUC). Demographics and comorbidities showed no significant differences between shrinkage and stable groups. The model containing 5 AAA RFs (which are original_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glrlm_GrayLevelNonUniformityNormalized, log-sigma-3-0-mm-3D_glrlm_RunPercentage, log-sigma-4-0-mm-3D_glrlm_ShortRunLowGrayLevelEmphasis, wavelet-LLH_glcm_SumEntropy) had AUCs of 0.86 (training) and 0.77 (test). The model containing 7 PVAT RFs (which are log-sigma-3-0-mm-3D_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glcm_Correlation, wavelet-LHL_firstorder_Energy, wavelet-LHL_firstorder_TotalEnergy, wavelet-LHH_firstorder_Mean, wavelet-LHH_glcm_Idmn, wavelet-LHH_glszm_GrayLevelNonUniformityNormalized) had AUCs of 0.76 (training) and 0.78 (test). Combining AAA and PVAT RFs yielded the highest accuracy: AUCs of 0.93 (training) and 0.87 (test). Radiomics-based CTA model predicts aneurysm sac regression post-EVAR in AAA patients. PVAT RFs from preoperative CTA images were closely related to AAA prognosis after EVAR, enhancing accuracy when combined with AAA RFs. This preliminary study explores a predictive model designed to assist clinicians in optimizing therapeutic strategies during clinical decision-making processes.