title: Liver computed tomographic perfusion in the assessment of microvascular invasion in patients with small hepatocellular carcinoma.
abstract: OBJECTIVE
  Detecting microvascular invasion (mVI) in patients with hepatocellular carcinoma is a diagnostic challenge. The present study aimed to acquire a series of quantitative perfusion parameters from liver computed tomography (CT) with a 320-slice scanner in patients with small hepatocellular carcinoma (sHCC) and study its efficacy in identifying mVI.
  
  
  METHODS
  Fifty-six patients who underwent hepatic resection for sHCC (≤3 cm) were preoperatively examined with a 320-detector row CT scanner. Histopathological analyses of liver biopsies confirmed that 18 patients had sHCC with mVI and that 38 patients had sHCC without mVI. Hepatic artery flow, portal vein flow (PVF), and perfusion index were measured in both tumor and normal liver tissues. Nonparametric receiver operating characteristic curve analysis was performed to quantify the accuracy of the perfusion CT parameters.
  
  
  RESULTS
  The tumor PVF (PVFtumor), difference in PVF between tumor and liver tissue (ΔPVF), and the ΔPVF/liver PVF ratio (rPVF) were significantly higher in sHCC with mVI than in sHCC without mVI (P = 0.0094, P = 0.0018, and P = 0.0007, respectively; Wilcoxon signed rank test). The PVFtumor, ΔPVF, and rPVF correctly predicted mVI in 73.2% (sensitivity, 66.7%; specificity, 76.3%; cutoff, 103.8 mL per 100 mL/min), 76.8% (sensitivity, 66.7%; specificity, 81.6%; cutoff, -53.65 mL per 100 mL/min), and 83.9% (sensitivity, 77.8%; specificity, 86.8%; cutoff, -0.38) of a total of 56 patients with sHCC, respectively. Other parameters were not significantly different between the groups.
  
  
  CONCLUSIONS
  Liver CT perfusion provides a noninvasive, quantitative method that can predict mVI in patients with sHCC through measurement of 3 perfusion parameters: PVFtumor, ΔPVF, and rPVF.
authors.affiliation: From the *Department of Radiology, Zhongshan Hospital of Fudan University; †Department of Medical Imaging, Shanghai Medical College, Fudan University; ‡Shanghai Institute of Medical Imaging; §Department of Liver Surgery, Zhongshan Hospital of Fudan University; ∥Shanghai Institute of Liver Cancer; and ¶Department of Pathology, Zhongshan Hospital of Fudan University, Shanghai, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wu
authors.forename: Ming
authors.initials: M
authors.lastname: Tan
authors.forename: Meiling
authors.initials: M
authors.lastname: Zhou
authors.forename: Huichuan
authors.initials: H
authors.lastname: Sun
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
authors.forename: Lingli
authors.initials: L
authors.lastname: Chen
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.forename: Mengsu
authors.initials: M
authors.lastname: Zeng
mesh_terms: D006528:Carcinoma, Hepatocellular / Q000098:blood supply / Q000000981:diagnostic imaging* / Q000473:pathology*; D005260:Female; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging; D008113:Liver Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging* / Q000473:pathology*; D008297:Male; D055806:Microvessels / Q000000981:diagnostic imaging*; D008875:Middle Aged; D009361:Neoplasm Invasiveness / Q000000981:diagnostic imaging; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed