Renal tumor quantification and classification in contrast-enhanced abdominal CT

Authors:
Marius George Linguraru;Jianhua Yao;Rabindra Gautam;James Peterson;Zhixi Li;W. Marston Linehan;Ronald M. Summers
Affiliations:
Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA;Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA;Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA;Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA
Venue:
Pattern Recognition
Year:
2009

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Abstract

Kidney cancer occurs in both hereditary (inherited) and sporadic (non-inherited) form. It is estimated that almost a quarter of a million people in the USA are living with kidney cancer and their number increases, with 51,000 diagnosed with the disease every year. In clinical practice, the response to treatment is monitored by manual measurements of tumor size, which are 2D, do not reflect the 3D geometry and enhancement of tumors, 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 responses to new treatments. The algorithm employs anisotropic diffusion (for smoothing), a combination of fast-marching and geodesic level-sets (for segmentation), 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 over time. Tumors are robustly segmented and the comparison between manual and semi-automated quantifications shows disparity within the limits of inter-observer variability. The analysis of lesion enhancement for tumor classification shows great separation between cysts, von Hippel-Lindau syndrome lesions, and hereditary papillary renal carcinomas (HPRC) with p-values inferior to 0.004. The results on temporal evaluation of tumors from serial scans illustrate the potential of the method to become an important tool for disease monitoring, drug trials, and non-invasive clinical surveillance.