Renal tumor quantification and classification in triple-phase contrast-enhanced abdominal CT

  • Authors:

  • Marius George Linguraru;Rabindra Gautam;James Peterson;Jianhua Yao;W. Marston Linehan;Ronald M. Summers

  • Affiliations:

  • Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD;Urologic Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD;Urologic Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD;Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD;Urologic Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD;Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD

  • Venue:
  • ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
  • Year:
  • 2009

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Abstract

It is estimated that a quarter of a million people in the USA are living with kidney cancer. In clinical practice, the response to treatment is monitored by manual measurements of tumor size, which are time consuming and show high intra- and inter-operator variability. We propose a computer-assisted radiology tool to assess renal tumors in contrast-enhanced CT for the management of tumor diagnoses and treatments. The algorithm employs anisotropic diffusion, a combination of fast-marching and geodesic level-sets, and a novel statistical refinement step to adapt to the shape of the lesions. It also quantifies the 3D size, volume and enhancement of the lesion and allows serial management of tumors. The comparison between manual and semi-automated quantifications shows disparity within the limits of inter-observer variability. The automated tumor classification shows great separation between cysts, von Hippel-Lindau syndrome (VHL) lesions and hereditary papillary renal carcinomas (HPRC) (p