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A radiomics signature to identify malignant and benign liver tumors on plain CT images.
Journal of X-Ray Science and Technology ( IF 3 ) Pub Date : 2020-06-18 , DOI: 10.3233/xst-200675
Jin Yin 1 , Jia-Jun Qiu 2 , Wei Qian 3 , Lin Ji 4 , Dan Yang 4 , Jing-Wen Jiang 2 , Jun-Ren Wang 2 , Lan Lan 2
Affiliation  

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.

中文翻译:

在普通 CT 图像上识别恶性和良性肝脏肿瘤的放射组学特征。

背景:在常规检查中,基于普通计算机断层扫描(CT)图像可能难以在视觉上识别良恶性肝脏肿瘤。RCAD(基于放射组学的计算机辅助诊断)已被证明是有帮助的,并在临床使用中提供了可解释性。目的:这项工作旨在开发基于 CT 的放射组学特征并研究其与恶性/良性肝脏肿瘤的相关性。方法:回顾性分析168例肝细胞癌(恶性)和117例肝血管瘤(良性)患者。从普通 CT 图像中提取纹理特征并用作候选特征。从候选特征中开发出放射组学特征。我们进行逻辑回归分析并使用多元回归系数(称为 R)来评估已发展的放射组学特征与恶性/良性肝脏肿瘤之间的相关性。最后,我们建立了一个逻辑回归模型来分类良性和恶性肝脏肿瘤。结果:从 1223 个候选特征中选择了 13 个特征来构成放射组学特征。逻辑回归分析实现了 R = 0.6745,远大于 Rα = 0.3703(显着性水平下 R 的临界值 α = 0.001)。逻辑回归模型的平均 AUC 为 0.87。结论:发展的放射组学特征与恶性/良性肝脏肿瘤具有统计学显着相关性(p < 0.001)。
更新日期:2020-06-30
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