比较乳腺动态增强MRI定量和半定量血流动力学参数鉴别乳腺良恶性病变
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摘要
目的比较乳腺动态增强MRI定量和半定量血流动力学参数鉴别诊断乳腺病变良恶性的效能。方法采用杂合动态增强MR序列对59例患者共66个乳腺病变进行扫描,获得半定量参数和定量参数。半定量参数为时间-信号强度曲线(TIC)、初始增强曲线下面积(IAUGC)、最大增强斜率(MaxSlope)、对比增强比率(CER)及正向增强积分(PEI);定量参数为前向容积转移常数(K~(trans))、反向容积转移常数(K_(ep))和每单位体积组织的血管外细胞外间隙容积(V_e)。以非参数检验比较良恶性病变间各参数的差异,并绘制ROC曲线,分析其诊断效能。结果 66个乳腺病变中,恶性31个(恶性组),良性35个(良性组),2组间K~(trans)、K_(ep)、TIC、IAUGC、MaxSlope差异均有统计学意义(P均<0.05),V_e、PEI、CER差异无统计学意义(P均>0.05);K~(trans)、K_(ep)、TIC、IAUGC、MaxSlope的AUC均>0.7。半定量参数联合诊断乳腺病变良恶性的AUC较单个参数均有显著提高(P均<0.05);定量参数联合后的AUC较K~(trans)无显著提高(P=0.134),较K_(ep)和V_e有显著提高(P均<0.001)。半定量联合与定量参数联合诊断诊断乳腺病变良恶性的AUC差异无统计学意义(P=0.614)。结论 K~(trans)、K_(ep)、TIC、IAUGC及MaxSlope对鉴别诊断乳腺良恶性病变具有较高效能;多参数联合,乳腺动态增强MRI半定量和定量参数诊断效能相似。
Objective To compare the diagnostic performance of quantitative and semi-quantitative hemodynamic parameters of breast dynamic contrast-enhanced MRI(DCE-MRI) in differential diagnosis of benign and malignant breast lesions. Methods Fifty-nine patients(66 lesions) with breast lesions underwent hybrid DCE MRI with high temporal and spatial resolution, and semi-quantitative and quantitative hemodynamic parameters were obtained. The semi-quantitative parameters included time-intensity curve(TIC), initial area under the gadolinium curve(IAUGC), maximum slope of increase(MaxSlope), contrast enhancement rate(CER) and positive enhancement integral(PEI), while quantitative parameters included volume transfer constant(K~(trans)), rate constant(K_(ep)) and extravascular extracellular volume fraction(V_e). The differences of all the parameters between benign and malignant breast lesions were compared by using non-parametric tests. ROC was used to analyze the diagnostic performance. Results There were 31 malignant lesions(malignant group) and 35 benign ones(benign group). There were significant differences of K~(trans), K_(ep), TIC, IAUGC and MaxSlope between the two groups(all P<0.05), while no significant difference in V_e, PEI nor CER(all P>0.05). AUC of ROC curve of K~(trans), K_(ep), TIC, IAUGC and MaxSlope were all >0.7. AUC of semi-quantitative parameter combination in diagnosis of benign and malignant breast lesions were significantly higher than those of single parameter(all P<0.05), AUC of quantitative parameter in combination was not significantly higher than those of K~(trans)(P=0.134), but significantly higher than those of K_(ep) and V_e(all P<0 001). There was no significant difference in AUC between combined semi-quantitative and quantitative parameters in diagnosis of benign and malignant breast lesions(P=0.614). Conclusion K~(trans), K_(ep), TIC, IAUGC and MaxSlope have good diagnostic performance in differentiating benign and malignant breast lesions. The combined semi-quantitative and quantitative hemodynamic DCE-MRI parameters have similar diagnostic performance.
Objective To compare the diagnostic performance of quantitative and semi-quantitative hemodynamic parameters of breast dynamic contrast-enhanced MRI(DCE-MRI) in differential diagnosis of benign and malignant breast lesions. Methods Fifty-nine patients(66 lesions) with breast lesions underwent hybrid DCE MRI with high temporal and spatial resolution, and semi-quantitative and quantitative hemodynamic parameters were obtained. The semi-quantitative parameters included time-intensity curve(TIC), initial area under the gadolinium curve(IAUGC), maximum slope of increase(MaxSlope), contrast enhancement rate(CER) and positive enhancement integral(PEI), while quantitative parameters included volume transfer constant(K~(trans)), rate constant(K_(ep)) and extravascular extracellular volume fraction(V_e). The differences of all the parameters between benign and malignant breast lesions were compared by using non-parametric tests. ROC was used to analyze the diagnostic performance. Results There were 31 malignant lesions(malignant group) and 35 benign ones(benign group). There were significant differences of K~(trans), K_(ep), TIC, IAUGC and MaxSlope between the two groups(all P<0.05), while no significant difference in V_e, PEI nor CER(all P>0.05). AUC of ROC curve of K~(trans), K_(ep), TIC, IAUGC and MaxSlope were all >0.7. AUC of semi-quantitative parameter combination in diagnosis of benign and malignant breast lesions were significantly higher than those of single parameter(all P<0.05), AUC of quantitative parameter in combination was not significantly higher than those of K~(trans)(P=0.134), but significantly higher than those of K_(ep) and V_e(all P<0 001). There was no significant difference in AUC between combined semi-quantitative and quantitative parameters in diagnosis of benign and malignant breast lesions(P=0.614). Conclusion K~(trans), K_(ep), TIC, IAUGC and MaxSlope have good diagnostic performance in differentiating benign and malignant breast lesions. The combined semi-quantitative and quantitative hemodynamic DCE-MRI parameters have similar diagnostic performance.
引文
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[6] Georgiou L, Sharma N, Broadbent DA, et al. Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI. Magn Reson Med, 2018,79(1):317-326.
[7] Li L, Wang K, Sun X, et al. Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer. Med Sci Monit, 2015,21:376-382.
[8] 盛美红,汤卫霞,陆益花,等.MRI动态增强扫描早期强化比值联合周围血管管径鉴别诊断乳腺良恶性病变的价值.中华放射学杂志,2016,50(5):324-328.
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[10] Koo HR, Cho N, Song IC, et al. Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers. J Magn Reson Imaging, 2012,36(1):145-151.
[11] Sardanelli F, Boetes C, Borisch B, et al. Magnetic resonance imaging of the breast: Recommendations from the EUSOMA working group. Eur J Cancer, 2010,46(8):1296-1316.
[12] El Khouli RH, Macura KJ, Jacobs MA, et al. Dynamiccontrast-enhanced MRI of the breast: Quantitative method for kinetic curve type assessment. AJR Am J Roentgenol, 2009,193(4):W295-W300.
[13] Turnbull LW. Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR Biomed, 2009,22(1):28-39.
[14] Cheng ZL, Wu Z, Shi GZ, et al. Discrimination between benign and malignant breastlesions using volumetric quantitative dynamic contrast-enhanced MR imaging. Eur Radiol, 2018,28(3):982-991.
[15] Rahbar H, Partridge SC. Multiparametric imaging of breast cancer. Magn Reson Imaging Clin N Am, 2016,24(1):223-238.
[16] 南聪慧,张伟,王慧颖,等.动态增强MRI定量评估乳腺良恶性病变的效能.中国医学影像技术,2018,34(4):558-562.
[17] Kim Y, Kim SH, Song BJ, et al. Early prediction of response to neoadjuvant chemotherapy using dynamic contrast-enhanced MRI and ultrasound in breast cancer. Korean J Radiol, 2018,19(4):682-691.
[18] Cho N, Im SA, Park IA, et al. Breast cancer: Early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging. Radiology, 2014,272(2):385-396.
[2] El Khouli RH, Macura KJ, Kamel IR, et al. 3.0T dynamic contrast-enhanced MRI of the breast: Pharmacokinetic parameters versus conventional kinetic curve analysis. AJR Am J Roentgenol, 2011,197(6):1498-1505.
[3] Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: Are signalintensity time course data useful for differential diagnosis of enhancing lesions? Radiology, 1999,211(1):101-110.
[4] Tofts PS, Berkowitz B, Schnall MD. Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn Reson Med, 1995,33(4):564-568.
[5] Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: Standardized quantities and symbols. J Magn Reson Imaging, 1999,10(3):223-232.
[6] Georgiou L, Sharma N, Broadbent DA, et al. Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI. Magn Reson Med, 2018,79(1):317-326.
[7] Li L, Wang K, Sun X, et al. Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer. Med Sci Monit, 2015,21:376-382.
[8] 盛美红,汤卫霞,陆益花,等.MRI动态增强扫描早期强化比值联合周围血管管径鉴别诊断乳腺良恶性病变的价值.中华放射学杂志,2016,50(5):324-328.
[9] 李丽环,刘万花,王瑞,等.MRI定量增强参数与乳腺癌预后因子及分子分型的相关性.中华放射学杂志,2016,50(5):329-333.
[10] Koo HR, Cho N, Song IC, et al. Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers. J Magn Reson Imaging, 2012,36(1):145-151.
[11] Sardanelli F, Boetes C, Borisch B, et al. Magnetic resonance imaging of the breast: Recommendations from the EUSOMA working group. Eur J Cancer, 2010,46(8):1296-1316.
[12] El Khouli RH, Macura KJ, Jacobs MA, et al. Dynamiccontrast-enhanced MRI of the breast: Quantitative method for kinetic curve type assessment. AJR Am J Roentgenol, 2009,193(4):W295-W300.
[13] Turnbull LW. Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR Biomed, 2009,22(1):28-39.
[14] Cheng ZL, Wu Z, Shi GZ, et al. Discrimination between benign and malignant breastlesions using volumetric quantitative dynamic contrast-enhanced MR imaging. Eur Radiol, 2018,28(3):982-991.
[15] Rahbar H, Partridge SC. Multiparametric imaging of breast cancer. Magn Reson Imaging Clin N Am, 2016,24(1):223-238.
[16] 南聪慧,张伟,王慧颖,等.动态增强MRI定量评估乳腺良恶性病变的效能.中国医学影像技术,2018,34(4):558-562.
[17] Kim Y, Kim SH, Song BJ, et al. Early prediction of response to neoadjuvant chemotherapy using dynamic contrast-enhanced MRI and ultrasound in breast cancer. Korean J Radiol, 2018,19(4):682-691.
[18] Cho N, Im SA, Park IA, et al. Breast cancer: Early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging. Radiology, 2014,272(2):385-396.