Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms

Abstract

Delian Kong,1,* Junrong Li,1,* Yingying Lv,1,* Man Wang,2,* Shenghua Li,1 Baoxin Qian,3 Yusheng Yu2 1Department of Neurology, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, Jiangsu, 211100, People’s Republic of China; 2Department of Radiology, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, Jiangsu, 211100, People’s Republic of China; 3Huiying Medical Technology (Beijing); Huiying Medical Technology Co., Ltd, Beijing City, 100192, People’s Republic of China*These authors contributed equally to this workCorrespondence: Delian Kong; Yusheng Yu, Email [email protected]; [email protected]: To develop a radiomics nomogram model based on time-of-flight magnetic resonance angiography (TOF-MRA) images for preoperative prediction of true microaneurysms.Methods: 118 patients with Intracranial Aneurysm Sac (40 positive and 78 negative) were enrolled and allocated to training and validation groups (8:2 ratio). Findings of clinical characteristics and MRA features were analyzed. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator (LASSO) regression algorithm in the training group. The radiomics nomogram model was constructed by combining clinical risk factors and radiomics signature. In order to compare the classification performance of clinical models, radiomics model and radiomics nomogram model, AUC was used to evaluate them. The performance of the radiomics nomogram model was evaluated by calibration curve and decision curve analysis.Results: Eleven features were selected to develop radiomics model with AUC of 0.875 (95% CI 0.78– 0.97), sensitivity of 0.84, and specificity of 0.68. The radiomics model achieved a better diagnostic performance than the clinic model (AUC = 0.75, 95% CI: 0.53– 0.97) and even radiologists. The radiomics nomogram model, which combines radiomics signature and clinical risk factors, is effective too (AUC = 0.913, 95% CI: 0.87– 0.96). Furthermore, the decision curve analysis demonstrated significantly better net benefit in the radiomics nomogram model.Conclusion: Radiomics features derived from TOF-MRA can reliably be used to build a radiomics nomogram model for effectively differentiating between pseudo microaneurysms and true microaneurysms, and it can provide an objective basis for the selection of clinical treatment plans.Keywords: machine learning, radiomics, microaneurysms, nomogram

Keywords

• machine learning
• radiomics
• micro-aneurysms
• nomogram