The clinical and imaging data fusion model for single-period cerebral CTA collateral circulation assessment

Article type: Research Article

Authors: Ma, Yuqi^a | He, Jingliu^a | Tan, Duo^b | Han, Xu^c | Feng, Ruiqi^a | Xiong, Hailing^d | Peng, Xihua^a | Pu, Xun^a | Zhang, Lin^a | Li, Yongmei^{e; 1; *} | Chen, Shanxiong^{a; f; 1; *}

Affiliations: [a] College of Computer and Information Science, Southwest University, Chongqing, China | [b] The Second People’s Hospital of Guizhou Province, Guizhou, China | [c] School of Electrical and Information Engineering, Tianjin University, Tianjin, China | [d] College of Electronic and Information Engineering, Southwest University, Chongqing, China | [e] Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China | [f] Big Data & Intelligence Engineering School, Chongqing College of International Business and Economics, Chongqing, China

Correspondence: [*] Corresponding authors: Yongmei Li, MD, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 40016, China. E-mail: [email protected] and Shanxiong Chen, PhD, College of Computer and Information Science, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China. E-mail: [email protected].

Note: [1] These authors contributed equally to this work.

Abstract: BACKGROUND:The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms. However, in studies concerning collateral circulation assessment, the extracted imaging features are primarily composed of manually designed statistical features, which exhibit significant limitations in their representational capacity. Accurately assessing collateral circulation using image features in brain CTA images still presents challenges. METHODS:To tackle this issue, considering the scarcity of publicly accessible medical datasets, we combined clinical data with imaging data to establish a dataset named RadiomicsClinicCTA. Moreover, we devised two collateral circulation assessment models to exploit the synergistic potential of patients’ clinical information and imaging data for a more accurate assessment of collateral circulation: data-level fusion and feature-level fusion. To remove redundant features from the dataset, we employed Levene’s test and T-test methods for feature pre-screening. Subsequently, we performed feature dimensionality reduction using the LASSO and random forest algorithms and trained classification models with various machine learning algorithms on the data-level fusion dataset after feature engineering. RESULTS:Experimental results on the RadiomicsClinicCTA dataset demonstrate that the optimized data-level fusion model achieves an accuracy and AUC value exceeding 86%. Subsequently, we trained and assessed the performance of the feature-level fusion classification model. The results indicate the feature-level fusion classification model outperforms the optimized data-level fusion model. Comparative experiments show that the fused dataset better differentiates between good and bad side branch features relative to the pure radiomics dataset. CONCLUSIONS:Our study underscores the efficacy of integrating clinical and imaging data through fusion models, significantly enhancing the accuracy of collateral circulation assessment in stroke patients.

Keywords: Radiomics, computer-aided diagnosis, fusion model, collateral circulation assessment, machine learning

DOI: 10.3233/XST-240083

Journal: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 953-971, 2024