Kung-Hao Liang1,2,3,4, Peng Zhang5,6, Chih-Lang Lin2,7, Stewart C Wang5,6, Tsung-Hui Hu8, Chau-Ting Yeh2,9, Grace L Su10,11,12. 1. Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan. 2. Liver Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan. 3. Institute of Food Safety and Health Risk Assessment, National Yang-Ming University, Taipei, Taiwan. 4. Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. 5. Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA. 6. Morphomic Analysis Group, University of Michigan Medical School, Ann Arbor, MI, USA. 7. Liver Research Unit, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan. 8. Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan. 9. Molecular Medicine Research Center, Chang Gung University, Taoyüan, Taiwan. 10. Morphomic Analysis Group, University of Michigan Medical School, Ann Arbor, MI, USA. gsu@umich.edu. 11. Division of Gastroenterology, University of Michigan Medical School, Ann Arbor, MI, USA. gsu@umich.edu. 12. VA Ann Arbor Healthcare System, 2215 Fuller Road, Ann Arbor, MI, 48105, USA. gsu@umich.edu.
Abstract
BACKGROUND AND AIMS: Computed tomography (CT) provides scans of the human body from which digitized features can be extracted. The aim of this study was to examine the role of these digital biomarkers for predicting subsequent occurrence of hepatocellular carcinoma (HCC) in cirrhotic patients. METHODS: A cohort of 269 patients with cirrhosis were recruited and prospectively followed for the occurrence of HCC in Taiwan. CT scans were retrospectively retrieved and computationally processed using analytic morphomics. A predictive score was constructed using Cox regression and the generalized iterative modeling method, maximizing the log likelihood of the time to HCC development. An independent cohort of 274 patients from University of Michigan was utilized to examine the predictive validity of this score in a Western population. RESULTS: Of the 27 digitized features at the 12th thoracic vertebral level, six features were significantly associated with HCC occurrence. Two digitized features (fascia eccentricity and the bone mineral density) were able to stratify patients into high- and low-risk groups with distinct cumulative incidence of HCC in both the training and validation cohorts (P = 0.015 and 0.044, respectively). When the two digitized features were tested in the Michigan cohort, only bone mineral density remained an effective predictor. CONCLUSION: Digitized features derived from the CT were effective in predicting subsequent occurrence of HCC in cirrhosis patients. The bone mineral density measured on CT was an effective predictor for patients in both Taiwan and USA.
BACKGROUND AND AIMS: Computed tomography (CT) provides scans of the human body from which digitized features can be extracted. The aim of this study was to examine the role of these digital biomarkers for predicting subsequent occurrence of hepatocellular carcinoma (HCC) in cirrhotic patients. METHODS: A cohort of 269 patients with cirrhosis were recruited and prospectively followed for the occurrence of HCC in Taiwan. CT scans were retrospectively retrieved and computationally processed using analytic morphomics. A predictive score was constructed using Cox regression and the generalized iterative modeling method, maximizing the log likelihood of the time to HCC development. An independent cohort of 274 patients from University of Michigan was utilized to examine the predictive validity of this score in a Western population. RESULTS: Of the 27 digitized features at the 12th thoracic vertebral level, six features were significantly associated with HCC occurrence. Two digitized features (fascia eccentricity and the bone mineral density) were able to stratify patients into high- and low-risk groups with distinct cumulative incidence of HCC in both the training and validation cohorts (P = 0.015 and 0.044, respectively). When the two digitized features were tested in the Michigan cohort, only bone mineral density remained an effective predictor. CONCLUSION: Digitized features derived from the CT were effective in predicting subsequent occurrence of HCC in cirrhosis patients. The bone mineral density measured on CT was an effective predictor for patients in both Taiwan and USA.
Entities:
Keywords:
Bone mineral density; Fascia eccentricity; Prognosis; Retrospective–prospective design
Authors: Calista M Harbaugh; Peng Zhang; Brianna Henderson; Brian A Derstine; Sven A Holcombe; Stewart C Wang; Carla Kohoyda-Inglis; Peter F Ehrlich Journal: J Pediatr Surg Date: 2017-01-29 Impact factor: 2.545
Authors: Bamidele Otemuyiwa; Brian A Derstine; Peng Zhang; Sandra L Wong; Michael S Sabel; Bruce G Redman; Stewart C Wang; Ajjai S Alva; Matthew S Davenport Journal: Acad Radiol Date: 2017-03-22 Impact factor: 3.173
Authors: Monica A Konerman; Aashesh Verma; Betty Zhao; Amit G Singal; Anna S Lok; Neehar D Parikh Journal: Liver Transpl Date: 2019-03 Impact factor: 5.799
Authors: Calista M Harbaugh; Peng Zhang; Brianna Henderson; Brian A Derstine; Sven A Holcombe; Stewart C Wang; Carla Kohoyda-Inglis; Peter F Ehrlich Journal: J Pediatr Surg Date: 2018-02-10 Impact factor: 2.545
Authors: Christopher Lee; Erica Raymond; Brian A Derstine; Joshua M Glazer; Rebecca Goulson; Avinash Rajasekaran; Jill Cherry-Bukowiec; Grace L Su; Stewart C Wang Journal: JPEN J Parenter Enteral Nutr Date: 2018-05-22 Impact factor: 4.016
Authors: Ryan W Stidham; Akbar K Waljee; Nicholas M Day; Carrie L Bergmans; Katelin M Zahn; Peter D R Higgins; Stewart C Wang; Grace L Su Journal: Inflamm Bowel Dis Date: 2015-06 Impact factor: 5.325
Authors: Neehar D Parikh; Peng Zhang; Amit G Singal; Brian A Derstine; Venkat Krishnamurthy; Pranab Barman; Akbar K Waljee; Grace L Su Journal: Cancer Res Treat Date: 2017-06-01 Impact factor: 4.679