Donghui Guo1, Dongsheng Gu2, Honghai Wang3, Jingwei Wei2, Zhenglu Wang3, Xiaohan Hao2, Qian Ji3, Shunqi Cao1, Zhuolun Song3, Jiabing Jiang1, Zhongyang Shen3, Jie Tian4, Hong Zheng5. 1. First Central Clinical College of Tianjin Medical University, Tianjin 300192, PR China. 2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, PR China. 3. Oriental Organ Transplant Center of Tianjin First Central Hospital, Tianjin 300192, PR China. 4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, PR China. Electronic address: tian@ieee.org. 5. Oriental Organ Transplant Center of Tianjin First Central Hospital, Tianjin 300192, PR China. Electronic address: zhenghong1965@tmu.edu.cn.
Abstract
OBJECTIVES: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation. METHODS: Our study consisted of a training dataset (n = 93) and a validation dataset (40) with clinically confirmed HCC after liver transplantation from October 2011 to December 2016. Radiomics features were extracted by delineating regions-of-interest (ROIs) around the lesion in four phases of CT images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association between radiomics signature and recurrence-free survival (RFS) was assessed. Preoperative clinical characteristics potentially associated with RFS were evaluated to develop a clinical model. A combined model incorporating clinical risk factors and radiomics signature was built. RESULTS: The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164). CONCLUSIONS: Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.
OBJECTIVES: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation. METHODS: Our study consisted of a training dataset (n = 93) and a validation dataset (40) with clinically confirmed HCC after liver transplantation from October 2011 to December 2016. Radiomics features were extracted by delineating regions-of-interest (ROIs) around the lesion in four phases of CT images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association between radiomics signature and recurrence-free survival (RFS) was assessed. Preoperative clinical characteristics potentially associated with RFS were evaluated to develop a clinical model. A combined model incorporating clinical risk factors and radiomics signature was built. RESULTS: The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164). CONCLUSIONS: Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.
Authors: Emily Harding-Theobald; Jeremy Louissaint; Bharat Maraj; Edward Cuaresma; Whitney Townsend; Mishal Mendiratta-Lala; Amit G Singal; Grace L Su; Anna S Lok; Neehar D Parikh Journal: Aliment Pharmacol Ther Date: 2021-08-12 Impact factor: 9.524
Authors: Lise Wei; Dawn Owen; Benjamin Rosen; Xinzhou Guo; Kyle Cuneo; Theodore S Lawrence; Randall Ten Haken; Issam El Naqa Journal: Phys Med Date: 2021-03-10 Impact factor: 2.685