A radiomics signature to identify malignant and benign liver tumors on plain CT images

Article type: Research Article

Authors: Yin, Jin^b | Qiu, Jia-Jun^a | Qian, Wei^c | Ji, Lin^{d; *} | Yang, Dan^d | Jiang, Jing-Wen^a | Wang, Jun-Ren^a | Lan, Lan^a

Affiliations: [a] West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China | [b] School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China | [c] Department of Electric and Computer Engineering, University of Texas El Paso, El Paso, TX, USA | [d] Department of Radiology, West China Hospital, Sichuan University, Chengdu, China

Correspondence: [*] Corresponding author: Lin Ji, Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, 610000, China. E-mail: jilin2@sina.com.

Abstract: BACKGROUND:In regular examinations, it may be difficult to visually identify benign and malignant liver tumors based on plain computed tomography (CT) images. RCAD (radiomics-based computer-aided diagnosis) has proven to be helpful and provide interpretability in clinical use. OBJECTIVE:This work aims to develop a CT-based radiomics signature and investigate its correlation with malignant/benign liver tumors. METHODS:We retrospectively analyzed 168 patients of hepatocellular carcinoma (malignant) and 117 patients of hepatic hemangioma (benign). Texture features were extracted from plain CT images and used as candidate features. A radiomics signature was developed from the candidate features. We performed logistic regression analysis and used a multiple-regression coefficient (termed as R) to assess the correlation between the developed radiomics signature and malignant/benign liver tumors. Finally, we built a logistic regression model to classify benign and malignant liver tumors. RESULTS:Thirteen features were chosen from 1223 candidate features to constitute the radiomics signature. The logistic regression analysis achieved an R = 0.6745, which was much larger than Rα = 0.3703 (the critical value of R at significant level α = 0.001). The logistic regression model achieved an average AUC of 0.87. CONCLUSIONS:The developed radiomics signature was statistically significantly correlated with malignant/benign liver tumors (p < 0.001). It has potential to help enhance physicians’ diagnostic abilities and play an important role in RCADs.

Keywords: Radiomics signature, texture analysis, liver tumor, logistic regression model, classification between malignant and benign tumors

DOI: 10.3233/XST-200675

Journal: Journal of X-Ray Science and Technology, vol. 28, no. 4, pp. 683-694, 2020