Computer-aided renal cancer quantification and classification from contrast-enhanced CT via histograms of curvature-related features.
In clinical practice, renal cancer diagnosis is performed by manual quantifications of tumor size and enhancement, which are time consuming and show high variability. We propose a computer-assisted clinical tool to assess and classify renal tumors in contrast-enhanced CT for the management and classification of kidney tumors. The quantification of lesions used level-sets and a statistical refinement step to adapt to the shape of the lesions. Intra-patient and inter-phase registration facilitated the study of lesion enhancement. From the segmented lesions, the histograms of curvature-related features were used to classify the lesion types via random sampling. The clinical tool allows the accurate quantification and classification of cysts and cancer from clinical data. Cancer types are further classified into four categories. Computer-assisted image analysis shows great potential for tumor diagnosis and monitoring.
Linguraru
Marius George
MG
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA. lingurarum@mail.nih.gov
Wang
Shijun
S
Shah
Furhawn
F
Gautam
Rabindra
R
Peterson
James
J
Linehan
W
W
Summers
Ronald M
RM
D003287:Contrast Media / Q000008:administration & dosage*; D006801:Humans; D007680:Kidney Neoplasms / Q000145:classification* / Q000473:pathology*; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*
D003287:Contrast Media