Hongxiang Li1, Jing Zhang2, Zeyu Zheng3, Yihao Guo4, Maodong Chen5, Caiqin Xie6, Zhongping Zhang7, Yingjie Mei8, Yanqiu Feng9, Yikai Xu10. 1. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, PR China. Electronic address: 1161522390@qq.com. 2. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, PR China. Electronic address: zjlzy12@163.com. 3. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, PR China. Electronic address: zeyu4350@163.com. 4. School of Biomedical Engineering, Southern Medical University, Guangzhou, PR China. Electronic address: guoyh0708@163.com. 5. School of Biomedical Engineering, Southern Medical University, Guangzhou, PR China. Electronic address: 729403823@qq.com. 6. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, PR China. Electronic address: 505245715@qq.com. 7. Philips Intergrated Solution Center, Guangzhou, PR China. Electronic address: aaron.zhang@philips.com. 8. Philips Intergrated Solution Center, Guangzhou, PR China. Electronic address: Yingjie.Mei@philips.com. 9. School of Biomedical Engineering, Southern Medical University, Guangzhou, PR China. Electronic address: foree@163.com. 10. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, PR China. Electronic address: yikaixu917@gmail.com.
Abstract
PURPOSE: To evaluate the value of intravoxel incoherent motion (IVIM) histogram analysis based on whole tumor volume in predicting microvascular invasion (MVI) of single hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The study enrolled 41 patients with pathologically proven HCCs who underwent IVIM diffusion-weighted imaging with nine b values and contrast-enhanced magnetic resonance imaging (MRI). Histogram parameters including mean; skewness; kurtosis; and percentiles (5th, 10th, 25th, 50th, 75th, 90th, 95th) were derived from apparent diffusion coefficient (ADC), perfusion fraction (f), true diffusion coefficient (D), and pseudo diffusion coefficient (D*). Quantitative histogram parameters and clinical data were compared between HCCs with and without MVI. For significant parameters, receiver operating characteristic (ROC) curves were further plotted to compare the diagnosis performance for identifying MVI. RESULTS: The mean, 5th, 10th, 25th, 50th, and 75th percentiles of D, and the 5th, 10th, and 25th percentiles of ADC between HCCs with and without MVI were statistically significant (all P＜0.05). The histogram parameters of D* and f showed no statistically significant differences between HCCs with and without MVI (all P＞0.05). The areas under the ROC curves (AUCs) were 0.707-0.874 for D and 0.668-0.720 for ADC. The largest AUC of D (5th percentile) showed significantly higher accuracy than that of ADC or tumor size (P = 0.009-0.046). With a cut-off of 0.403 × 10-3 mm²/s, the 5th percentile of D value provided a sensitivity of 81% and a specificity of 85% in the prediction of MVI. CONCLUSIONS: Histogram analysis of IVIM based on whole tumor volume can be useful for predicting MVI. The 5th percentile of D was most useful value to predict MVI of HCC.
PURPOSE: To evaluate the value of intravoxel incoherent motion (IVIM) histogram analysis based on whole tumor volume in predicting microvascular invasion (MVI) of single hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The study enrolled 41 patients with pathologically proven HCCs who underwent IVIM diffusion-weighted imaging with nine b values and contrast-enhanced magnetic resonance imaging (MRI). Histogram parameters including mean; skewness; kurtosis; and percentiles (5th, 10th, 25th, 50th, 75th, 90th, 95th) were derived from apparent diffusion coefficient (ADC), perfusion fraction (f), true diffusion coefficient (D), and pseudo diffusion coefficient (D*). Quantitative histogram parameters and clinical data were compared between HCCs with and without MVI. For significant parameters, receiver operating characteristic (ROC) curves were further plotted to compare the diagnosis performance for identifying MVI. RESULTS: The mean, 5th, 10th, 25th, 50th, and 75th percentiles of D, and the 5th, 10th, and 25th percentiles of ADC between HCCs with and without MVI were statistically significant (all P＜0.05). The histogram parameters of D* and f showed no statistically significant differences between HCCs with and without MVI (all P＞0.05). The areas under the ROC curves (AUCs) were 0.707-0.874 for D and 0.668-0.720 for ADC. The largest AUC of D (5th percentile) showed significantly higher accuracy than that of ADC or tumor size (P = 0.009-0.046). With a cut-off of 0.403 × 10-3 mm²/s, the 5th percentile of D value provided a sensitivity of 81% and a specificity of 85% in the prediction of MVI. CONCLUSIONS: Histogram analysis of IVIM based on whole tumor volume can be useful for predicting MVI. The 5th percentile of D was most useful value to predict MVI of HCC.
Authors: Likun Cao; Jie Chen; Ting Duan; Min Wang; Hanyu Jiang; Yi Wei; Chunchao Xia; Xiaoyue Zhou; Xu Yan; Bin Song Journal: Quant Imaging Med Surg Date: 2019-04
Authors: Alexey Surov; Maciej Pech; Jazan Omari; Frank Fischbach; Robert Damm; Katharina Fischbach; Maciej Powerski; Borna Relja; Andreas Wienke Journal: Liver Cancer Date: 2021-01-27 Impact factor: 11.740