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Kinetic Textural Biomarker for Predicting Survival of Patients with Advanced Hepatocellular Carcinoma After Antiangiogenic Therapy by Use of Baseline First-Pass Perfusion CT

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Book cover Abdominal Imaging. Computational and Clinical Applications (ABD-MICCAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8676))

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Abstract

Previous texture analysis studies of liver CT images have shown the potential to achieve hepatic malignancy or predict overall survival (OS). However, to date, most studies have mainly focused on assessing texture features of the non-contrast CT or portal-phase image in the dynamic contrast-enhanced CT sequence. The aim of this study was to quantify texture features of physiologically-based kinetic parametric images, and to develop prognostic kinetic textural biomarkers for 1-year survival (1YS) and OS in patients with advanced hepatocellular carcinoma (HCC) following antiangiogenic therapy in comparison among five different tracer kinetic models. Mean, standard deviation, coefficient of variation, skewness, and kurtosis of the pixel distribution histogram within HCC were derived from baseline first-pass perfusion CT parameters. Results suggest that texture analysis of kinetic parametric images can provide better chances of finding effective prognostic biomarkers for the prediction of survival than a mean value analysis alone.

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Authors and Affiliations

  1. 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., Suite 400C, Boston, MA, 02114, USA

    Sang Ho Lee & Hiroyuki Yoshida

  2. Division of Abdominal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA

    Koichi Hayano & Dushyant V. Sahani

  3. Division of Hematology and Oncology, Department of Medicine, Massachusetts General Hospital Cancer Center, Harvard Medical School, and Dana-Farber/Partners Cancer Care, 55 Fruit St., Boston, MA, 02114, USA

    Andrew X. Zhu

Authors
  1. Sang Ho Lee
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  2. Koichi Hayano
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  3. Dushyant V. Sahani
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  4. Andrew X. Zhu
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  5. Hiroyuki Yoshida
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Correspondence to Sang Ho Lee .

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Editors and Affiliations

  1. 3D Imaging Research ,Dept of Radiology, Harvard Medical School , Massachusetts General Hospital, Boston, Massachusetts, USA

    Hiroyuki Yoshida

  2. 3D Imaging Research, Dept of Radiology, Harvard Medical School , MA General Hospital, Boston, Massachusetts, USA

    Janne J. Näppi

  3. Department of Radiology, Harvard Medical School , MA General Hospital, Boston, Massachusetts, USA

    Sanjay Saini

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Lee, S.H., Hayano, K., Sahani, D.V., Zhu, A.X., Yoshida, H. (2014). Kinetic Textural Biomarker for Predicting Survival of Patients with Advanced Hepatocellular Carcinoma After Antiangiogenic Therapy by Use of Baseline First-Pass Perfusion CT. In: Yoshida, H., Näppi, J., Saini, S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science(), vol 8676. Springer, Cham. https://doi.org/10.1007/978-3-319-13692-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-13692-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13691-2

  • Online ISBN: 978-3-319-13692-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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