Dong Wu1, Ming Tan, Meiling Zhou, Huichuan Sun, Yuan Ji, Lingli Chen, Gang Chen, Mengsu Zeng. 1. 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.
Abstract
OBJECTIVES: 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. MATERIALS AND 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.
OBJECTIVES: 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. MATERIALS AND 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: Michał Grąt; Jan Stypułkowski; Waldemar Patkowski; Emil Bik; Maciej Krasnodębski; Karolina M Wronka; Zbigniew Lewandowski; Michał Wasilewicz; Karolina Grąt; Łukasz Masior; Joanna Ligocka; Marek Krawczyk Journal: Sci Rep Date: 2017-01-06 Impact factor: 4.379
Authors: Martin Reinhardt; Philipp Brandmaier; Daniel Seider; Marina Kolesnik; Sjoerd Jenniskens; Roberto Blanco Sequeiros; Martin Eibisberger; Philip Voglreiter; Ronan Flanagan; Panchatcharam Mariappan; Harald Busse; Michael Moche Journal: Contemp Clin Trials Commun Date: 2017-08-18