Home| Current Issue| Ahead of Print| Archive| About TSR| Contact| Türkçe


About DIR
Editorial Board
Past Editors

 
Most Read Papers
Most Downloaded Papers
Most Cited Papers
DIR selected Reviews

March / April 2013, Volume 19, Issue 2, Page(s) 097-105
Quantitative morphometric analysis of hepatocellular carcinoma: development of a programmed algorithm and preliminary application
Felix Y. Yap1, James T. Bui2, M. Grace Knuttinen1, Natasha M. Walzer2, Scott J. Cotler2, Charles A. Owens1, Jamie L. Berkes2, Ron C. Gaba1
1Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
2Department of Hepatology, University of Illinois at Chicago, Chicago, Illinois, USA
DOI: 10.4261/1305-3825.DIR.5973-12.1
PURPOSE
The quantitative relationship between tumor morphology and malignant potential has not been explored in liver tumors. We designed a computer algorithm to analyze shape features of hepatocellular carcinoma (HCC) and tested feasibility of morphologic analysis.

MATERIALS AND METHODS
Cross-sectional images from 118 patients diagnosed with HCC between 2007 and 2010 were extracted at the widest index tumor diameter. The tumor margins were outlined, and point coordinates were input into a MATLAB (MathWorks Inc., Natick, Massachusetts, USA) algorithm. Twelve shape descriptors were calculated per tumor: the compactness, the mean radial distance (MRD), the RD standard deviation (RDSD), the RD area ratio (RDAR), the zero crossings, entropy, the mean Feret diameter (MFD), the Feret ratio, the convex hull area (CHA) and perimeter (CHP) ratios, the elliptic compactness (EC), and the elliptic irregularity (EI). The parameters were correlated with the levels of alpha-fetoprotein (AFP) as an indicator of tumor aggressiveness.

RESULTS
The quantitative morphometric analysis was technically successful in all cases. The mean parameters were as follows: compactness 0.88±0.086, MRD 0.83±0.056, RDSD 0.087±0.037, RDAR 0.045±0.023, zero crossings 6±2.2, entropy 1.43±0.16, MFD 4.40±3.14 cm, Feret ratio 0.78±0.089, CHA 0.98±0.027, CHP 0.98±0.030, EC 0.95±0.043, and EI 0.95±0.023. MFD and RDAR provided the widest value range for the best shape discrimination. The larger tumors were less compact, more concave, and less ellipsoid than the smaller tumors (P < 0.0001). AFP-producing tumors displayed greater morphologic irregularity based on several parameters, including compactness, MRD, RDSD, RDAR, entropy, and EI (P < 0.05 for all).

CONCLUSION
Computerized HCC image analysis using shape descriptors is technically feasible. Aggressively growing tumors have wider diameters and more irregular margins. Future studies will determine further clinical applications for this morphologic analysis.