Shuai Zhang1, Guizhi Xu2, Chongfeng Duan1, Xiaoming Zhou1, Xin Wang1, Haiyang Yu1, Lan Yu1, Zhiming Li1, Yuanxiang Gao1, Ruirui Zhao3, Linlin Jiao4, Gang Wang1. 1. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. 2. Department of Radiology, Zhucheng People Hospital, Zhucheng Shandong, China. 3. Operating Room, The Affiliated Hospital of Qingdao University, Qingdao, China. 4. Interventional department, The Affiliated Hospital of Qingdao University, Qingdao, China.
Abstract
PURPOSE: To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). METHOD: A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. RESULTS: The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. CONCLUSIONS: Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.
PURPOSE: To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). METHOD: A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. RESULTS: The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. CONCLUSIONS: Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.
Authors: Stephen R Bowen; William T C Yuh; Daniel S Hippe; Wei Wu; Savannah C Partridge; Saba Elias; Guang Jia; Zhibin Huang; George A Sandison; Dennis Nelson; Michael V Knopp; Simon S Lo; Paul E Kinahan; Nina A Mayr Journal: J Magn Reson Imaging Date: 2017-10-16 Impact factor: 4.813
Authors: Koichi Hayano; Fang Tian; Avinash R Kambadakone; Sam S Yoon; Dan G Duda; Balaji Ganeshan; Dushyant V Sahani Journal: J Comput Assist Tomogr Date: 2015 Jul-Aug Impact factor: 1.826