Quantitative morphometric analysis of hepatocellular carcinoma: development of a programmed algorithm and preliminary application

PURPOSEThe 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 METHODSCross-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. RESULTSThe 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). CONCLUSIONComputerized 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

Imaging surveillance and multidisciplinary review improves curative therapy access and survival in HCC patients

Introduction. Imaging surveillance and multidisciplinary conference (MDC) review can potentially improve survival in patients with hepatocellular carcinoma (HCC) by increasing access to liver transplantation. Geographic disparities in donor organ availability may reduce this benefit. This study evaluated the impact of HCC surveillance on use of curative therapies and survival in a region with long transplant waiting times.Material and methods. 167 HCC patients were retrospectively studied. Subjects had an established HCC diagnosis or were diagnosed during hepatology follow-up. Collected data included patient demographics, HCC surveillance and MDC review status, portal hypertension complications, laboratory and radiologic parameters, tumor size, therapeutic interventions, tumor progression, and mortality. The primary outcome measures were use of curative treatments and survival. A Cox-regression model was constructed utilizing factors associated with survival in univariate analysis.Results. 58% of subjects underwent surveillance and MDC review of HCC. These patients were more likely to have received treatment with ablation or resection (16 vs. 3%, P = 0.006) and transplantation (23 vs. 4%, P = 0.001), and were less likely to develop tumor progression (45 vs. 68%, P = 0.005) or metastases (0 vs. 19%, P < 0.001). In multivariate analysis, surveillance and MDC review (P = 0.034, HR 0.520, 95% CI 0.284–0.952), tumor meeting Milan criteria (P < 0.001, HR 0.329, 95% CI 0.178–0.607), curative therapy application (P = 0.048, HR 0.130, 95% CI 0.017–0.979), and transplantation (P = 0.004, HR 0.236, 95% CI 0.088–0.632) were associated with survival.Conclusion. In conclusion, imaging surveillance and MDC review is associated with detection of early stage HCC, increased access to curative therapies and transplantation, and prolonged survival