Wenyu Gao1,2, Wentao Wang3,4, Danjun Song5,6, Chun Yang3, Kai Zhu5, Mengsu Zeng3,4, Sheng-Xiang Rao7,8, Manning Wang9,10. 1. Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. 2. Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China. 3. Department of Radiology, Cancer Center, Shanghai Medical Imaging Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, 200032, China. 4. Shanghai Institute of Medical Imaging, Shanghai, China. 5. Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China. 6. Department of Interventional Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China. 7. Department of Radiology, Cancer Center, Shanghai Medical Imaging Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Rd., Shanghai, 200032, China. raoxray@163.com. 8. Shanghai Institute of Medical Imaging, Shanghai, China. raoxray@163.com. 9. Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. mnwang@fudan.edu.cn. 10. Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China. mnwang@fudan.edu.cn.
Abstract
PURPOSE: Hepatocellular carcinoma (HCC) is the most common liver cancer worldwide, and early recurrence of HCC after curative hepatic resection is indicative of poor prognoses. We aim to develop a predictive model for postoperative early recurrence of HCC based on deep and radiomics features from multi-phasic magnetic resonance imaging (MRI). MATERIALS AND METHODS: A total of 472 HCC patients were included and divided into the training (n = 378) and validation (n = 94) cohorts in the retrospective study. We separately extracted radiomics features and deep features from eight phases of gadoxetic acid-enhanced MRI and utilized the least absolute shrinkage and selection operator logistic regression algorithm for feature selection and model construction. We integrated the selected two types of features into a combined model and established a radiomics model as well as a deep learning (DL) model for comparison. RESULTS: In the training and validation cohorts, the combined model demonstrated better performance for stratifying patients at high risk of early recurrence (AUC of 0.911 and 0.840, accuracy of 0.779 and 0.777, sensitivity of 0.927 and 0.769, specificity 0.720 and 0.779) than the radiomics model (AUC of 0.740 and 0.780) and the DL model (AUC of 0.887 and 0.813). CONCLUSION: The combined model integrating deep and radiomics features from multi-phasic MRI is efficient for noninvasively stratifying patients at high risk of early HCC recurrence after resection.
PURPOSE: Hepatocellular carcinoma (HCC) is the most common liver cancer worldwide, and early recurrence of HCC after curative hepatic resection is indicative of poor prognoses. We aim to develop a predictive model for postoperative early recurrence of HCC based on deep and radiomics features from multi-phasic magnetic resonance imaging (MRI). MATERIALS AND METHODS: A total of 472 HCC patients were included and divided into the training (n = 378) and validation (n = 94) cohorts in the retrospective study. We separately extracted radiomics features and deep features from eight phases of gadoxetic acid-enhanced MRI and utilized the least absolute shrinkage and selection operator logistic regression algorithm for feature selection and model construction. We integrated the selected two types of features into a combined model and established a radiomics model as well as a deep learning (DL) model for comparison. RESULTS: In the training and validation cohorts, the combined model demonstrated better performance for stratifying patients at high risk of early recurrence (AUC of 0.911 and 0.840, accuracy of 0.779 and 0.777, sensitivity of 0.927 and 0.769, specificity 0.720 and 0.779) than the radiomics model (AUC of 0.740 and 0.780) and the DL model (AUC of 0.887 and 0.813). CONCLUSION: The combined model integrating deep and radiomics features from multi-phasic MRI is efficient for noninvasively stratifying patients at high risk of early HCC recurrence after resection.
Authors: Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies Journal: Magn Reson Imaging Date: 2012-08-13 Impact factor: 2.546