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Morphomic Signatures Derived from Computed Tomography Predict Hepatocellular Carcinoma Occurrence in Cirrhotic Patients

  • Kung-Hao Liang
  • Peng Zhang
  • Chih-Lang Lin
  • Stewart C. Wang
  • Tsung-Hui Hu
  • Chau-Ting Yeh
  • Grace L. SuEmail author
Original Article
  • 25 Downloads

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.

Keywords

Bone mineral density Fascia eccentricity Prognosis Retrospective–prospective design 

Notes

Acknowledgment

The authors would like to thank Brian Derstine, Brian Ross, Dr. Gigin Lin, Dr. Ching-Yi Hsieh, Wan-Ru Liang, Yi-Ting Liao, Yi-Wen Li, Chung-Yin Wu, Fang-Yi He, Hui-Chin Chen, Ya-Ming Cheng, Yu-Jean Chen, and Chien-Chih Wang of the liver research center for the excellent technical and administrative assistance.

Funding

PZ was partially supported by funding from the US National Institutes of Health (K01 DK106296). GLS was partially funded by U01 CA230669.

Compliance with Ethical Standards

Conflict of interest

SCW and the University of Michigan have a patent on the Morphomics Technique. SCW has equity interest in Applied Morphomics. No commercial funding was utilized for this project.

Supplementary material

10620_2019_5915_MOESM1_ESM.docx (145 kb)
Supplementary material 1 (DOCX 144 kb)

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.Department of Medical ResearchTaipei Veterans General HospitalTaipeiTaiwan
  2. 2.Liver Research CenterChang Gung Memorial HospitalLinkouTaiwan
  3. 3.Department of SurgeryUniversity of Michigan Medical SchoolAnn ArborUSA
  4. 4.Morphomic Analysis GroupUniversity of Michigan Medical SchoolAnn ArborUSA
  5. 5.Liver Research UnitKeelung Chang Gung Memorial HospitalKeelungTaiwan
  6. 6.Division of Hepatogastroenterology, Department of Internal MedicineKaohsiung Chang Gung Memorial HospitalKaohsiungTaiwan
  7. 7.Molecular Medicine Research CenterChang Gung UniversityTaoyüanTaiwan
  8. 8.Division of GastroenterologyUniversity of Michigan Medical SchoolAnn ArborUSA
  9. 9.VA Ann Arbor Healthcare SystemAnn ArborUSA
  10. 10.Institute of Food Safety and Health Risk AssessmentNational Yang-Ming UniversityTaipeiTaiwan
  11. 11.Institute of Biomedical InformaticsNational Yang-Ming UniversityTaipeiTaiwan

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