磁共振扩散加权成像及动态增强扫描评价肝细胞癌组织分化程度的临床应用研究
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摘要
一、目的：
(1)探讨静脉注射Gd-DTPA后肝脏磁共振扩散加权成像(diffusion-weighted imaging, DWI)的信噪比(signal-to-noise ratio, SNR)、对比噪声比(contrast-to-noise ratio, CNR)及表观扩散系数(apparent diffusion coefficient, ADC)测量值的变化,以评价增强扫描后进行DWI的可行性。
(2)通过分析DWI图像肝细胞癌(hepatocellular carcinoma, HCC)的信号强度(signal intensity, SI)、ADC值和不同组织分化之间的关系,探讨扩散加权成像评价肝细胞癌组织分化程度的诊断价值。
(3)通过分析HCC磁共振动态增强(dynamic contrast-enhanced,DCE)的时间-信号强度曲线(time of intensity curve, TIC)类型及半定量参数,探讨磁共振动态增强对HCC组织分化程度的评价价值。
二、材料与方法：
1、研究对象
(1)第一部分：2008年11月至2009年6月经B超或CT检查发现肝脏局灶性病变而行MRI检查以进一步明确诊断的患者26例。其中男性19例,女性7例。年龄范围：37～67岁,平均年龄54.50±9.12岁。以病灶的最大径测量,范围：2.1～18cm,平均7.17±4.03cm。
(2)第二部分及第三部分：采用同一组病例,2008年11月至2010年3月在齐鲁医院做肝癌手术切除患者27例,组织病理诊断为肝细胞癌。其中男性24例,女性3例。年龄范围：36～68岁,平均年龄55.90±9.52岁。以病灶的最大径测量,最小为2cm,最大18cm,平均6.19±3.10cm。
2、扫描方法
所有研究对象的磁共振扫描均在同一台3.0T的磁共振仪(Signa Exite, GE Healthcare, Milwaukee, WI)上完成,信号采集使用8通道表面相阵控线圈。扫描序列包括T1WI T2WI、DWI及3D-FAME动态增强扫描。DWI采用b值600s/mm2。
3、图像分析方法
(1)第一部分测量在ADW4.2工作站(Advantage Window, GE Medical System, Milwaukee, WI)进行,利用Function2的扩散成像自动分析软件建立ADC图。选择病灶最大直径层面作为测量感兴趣区((region of interest, ROI)层面,分别测量注射Gd-DTPA前后病灶的ADC值及SI值,周围正常肝脏的SI值、背景的SD。同一个病灶注射Gd-DTPA前后的DWI图像、ADC图及放置感兴趣区位置尽可能保持一致。SNR及CNR的计算依次根据下述公式：SNR=SInormal/SDnoise, CNR= I SIlesion-SInormal I/SDnoise。
(2)第二部分
DWI图像目测评价DWI图像HCC的信号强度分为等信号、略高信号及高信号。
ADC值及SI值的测量
测量在ADW4.2工作站(Advantage Window, GE Medical System, Milwaukee, WI)进行,利用工作站中的Function2的扩散成像自动分析软件建立ADC图。选择病灶最大直径层面作为ROI层面,测量病灶的ADC值及SI值。
(3)第三部分所有DCE-MRI图像的观察及测量均在ADW4.2工作站(Advantage Window, GE Medical System, Milwaukee, WI)上进行,利用工作站中的function2动态强化自动分析软件进行分析,将感兴趣区(ROI)置于病灶最大直径层面的病灶区,自动生成TIC,分析曲线类型。并通过TIC获得半定量参数：最大上升斜率(maximum slop of increase,MSI)及信号增强率(signal enhancement ratio, SER)。
4、组织病理学
第二及第三部分病例均为HCC,依据Edmondson-Steiner's分级系统,肝细胞癌分化程度分为高分化、中分化及低分化。如果1个病灶中同时存在不同分化程度的肿瘤组织,则选择占主要优势组织的分化代表整个病灶的分化程度。
5、统计学分析
统计学分析采用SPSS 16.0统计软件进行资料录入和统计分析。数据测量结果以平均值士标准差(X±s)的形式描述。显著性水准均采用0.05(双侧),P值<0.05被认为有统计学意义。
第一部分病灶增强前后的SNR, CNR及ADC值均符合正态分布,增强前后均数的比较采用配对t检验。
第二部分采用Fisher's精确概率法检验目测观察DWI图像HCC信号强度的差异。三种不同分化程度HCC的ADC值及SI值均符合正态分布且满足方差齐性,采用单因素方差分析(one-way ANOVA),均数之间的两两比较采用LSD检验。采用ROC分析评价ADC值诊断低分化HCC的最佳界点和相应的诊断敏感性和特异性。
第三部分三种不同分化程度HCC的MSI值、SER值均符合正态分布且满足方差齐性,采用单因素方差分析(one-way ANOVA),均数之间的两两比较采用LSD检验。
三、结果
1.第一部分
(1).DWI图像目测观察结果注射对比剂前后在DWI图像观察到相同数目病灶,但注射对比剂后信号强度略有增高。
(2).静脉注射Gd-DTPA前后SNR、CNR及ADC值的测量结果
注射对比剂后病灶的ADC值较增强前降低,且二者之间有显著性统计学差异(t=2.717,P=0.019)。注射对比剂后DWI的SNR及CNR较增强前增高,且两者之间有显著性统计学差异(t=2.356,P=0.036；t=3.170,P=0.008)。
2.第二部分
(1).组织学结果
本组27个HCC病灶中,高分化HCC病灶6个,中分化HCC病灶10个低分化HCC病灶11个。
(2).DWI图像目测信号强度结果
本组资料27个HCC中,96.3%(26/27)在DWI表现为略高信号和高信号。三种不同分化HCC的目测信号强度之间无统计学差异(P=0.567,Fisher's精确概率法)。
(3).ADC值及SI值的测量结果
高、中、低分化HCC的平均ADC值表现降低的趋势,且三者之间有显著性差异(F= 5.921, P= 0.008, one-way ANOVA检验)。其中低分化HCC的ADC值明显低于高、中分化HCC(P= 0.004, P= 0.023, LSD检验)。虽然高分化HCC的ADC值高于中分化HCC的ADC值,但二者之间的ADC值无统计学差异(P=0.281,LSD检验)。ROC分析显示ADC值用于鉴别诊断低分化HCC及高中分化HCC有显著性意义(Az=0.790, P=0.012,95%CI:0.618-0.962). ADC值用于诊断低分化HCC的最佳界点为1.1450,此时诊断的敏感性和特异性分别为100%和54.5%。
高、中、低分化HCC的平均SI值之间有显著性差异(F=.4.161, P= 0.028, one-way ANOVA检验)。其中高、低分化HCC的SI值明显高于中分化HCC(P= 0.025, P= 0.019, LSD检验)。但高、低分化HCC的SI值之间无统计学差异(P=0.281,LSD检验)。
3.第三部分
(1).组织学结果同第二部分
(2).TIC类型
TICⅠ型及Ⅱ型最常见于高、中分化HCC (14/16)。TICⅢ型最常见于低分化HCC(6/11)。
(3).MSI及SER值的测量结果
高、中、低分化HCC的平均MSI值之间有显著性差异(F= 4.071,P=0.030, one-way ANOVA检验)。高分化HCC的MSI值明显高于中、低分化HCC(P= 0.019, P= 0.014, LSD检验)。中、低分化HCC之间的MSI值无统计学差异(P=0.934,LSD检验)。
高、中、低分化HCC的平均SER值之间无统计学差异(F= 2.758, P= 0.084, one-way ANOVA检验)。
四、结论
1.静脉注射Gd-DTPA后DWI图像的SNR及CNR不但没有降低,相反明显升高,所以DWI图像质量有所提高,与注射对比剂前DWI相比更易于显示病灶。静脉注射Gd-DTPA后ADC值明显减低。由于ADC值受多种因素影响,静脉注射Gd-DTPA后ADC的变化目前尚无明确结论,DWI图像采集最好选择在注射对比剂前进行。
2.不同分化程度HCC的ADC值之间有显著性差异,低分化HCC的ADC值明显低于高、中分化HCC。因此,ADC值的测量是一有价值的非侵袭性预测HCC分化程度的指标。因不同分化程度HCC的ADC值有较大的重叠,所以仅通过ADC值来正确预测HCC的组织分化程度是有一定限度的。
3. DCE-MRI在一定程度上反映了不同分化程度HCC的血流动力学特征,低分化HCC动脉血流有降低的趋势。TIC类型和半定量参数有助于不同分化程度HCC的预测,对于HCC治疗的选择及预后能提供有价值的影像信息。
1. Purpose:
(1) To evaluate whether administration of gadolinium-based contrast material significantly affects diffusion-weighted imaging(DWI) and apparent diffusion coefficient(ADC) values at the focal hepatic lesions.
(2) To evaluate the relationship between the signal intensity of hepatocellular carcinoma (HCC) assessed with diffusion-weighted imaging(DWI) and the apparent diffusion coefficient (ADC) with the histopathologic grade of HCC.
(3) To evaluate the diagnostic value of dynamic contrast-enhanced MRI(DCE-MRI) in predicting histopathologic grade of HCC by analysis of time of intensity curve(TIC) types and semi-quantitative parameters.
2. Materials and methods:
(1) Study population
①From November 2008 to June 2009,26 patient, including 19 males and 7 females, who were diagnosed as focal hepatic lesions. The age range was 37-67 years, with the average age of 54.50±9.12 years. The diameter of the lesions ranged from 2.1-18cm in the maximum diameter(mean,7.17±4.03 cm).
②From November 2008 to March 2010,27 patients, including 24 males and 3 females, who were diagnosed as HCC. The age range was 36-38 years, with the average age of 55.90±9.52 years. The diameter of the HCCs ranged from 2.0-18cm in the maximum diameter(mean,6.19±3.10 cm). (2) MRI
MRI was performed with a 3.0T system (GE Signa Excite, GE Healthcare, Milwaukee, WI) with a phased-array multicoil for the body. The MRI protocols included the T1-weighted, T2-weighted, DWI and 3D-FAME dynamic contrast-enhanced imaging. DWI was obtained using single-shot echo planar imaging with b value of 600 s/mm2.
(3) Image analysis
①By copying and pasting ROIs, the SI value, the SDnoise value and the ADC value were measured. SNR and CNR of each lesion was calculated using the formula SNR=SInormal/SDnoise, CNR=| Sllesion-SInormal|/SDnoise.
②Visual assessment
The same two radiologists visually assessed the signal intensity of all tumors and classified them into isointense, slightly hyperintense or obviously hyperintense by mutual agreement.
ADC value and SI value of each tumor were measured.
③TICs were classified into three types.MSI and SER were derived from TIC.The MSI value and SER value of HCCs were measured.
(4) Histopathology
The differentiation of an HCC was classified into well, moderate and poor according to Edmondson-Steiner's grading system. When different tumor grades coexisted within a tumor, the more predominant differentiation of the tumor was selected.
(5) Statistical analysis
All the statistical calculations were performed using the statistical software SPSS16.0. The results of measured values were recorded in the format of mean±standard deviation'. p< 0.05 was considered statistically significant in all the statistical analyses.
SNR, CNR and ADC values obtained on pre-contrast and post-contrast were analyzed using Student's paired t test. The difference of the ADC value, SI value, MSI value and SER value was compared with the histopathologic grade using the one-way ANOVA and LSD test. ROC analysis was used to analyse the diagnostic value of ADC value in differentiating poorly-differentiated HCC from well- and moderately-differentiated HCC. The optimal cutoff point of ADC value and the correspongding sensitivity and specificity were determined.
Fisher's exact test was performed to assess the statistical difference between the histopathologic grades of HCC and visually evaluated signal intensity on DWI.
3. Results:
(1) The SNR and CNR of the DWI were significantly increased after contrast medium injection (t=2.356, p=0.036; t=3.170, p=0.008). ADC values of the focal hepatic lesions significantly decreased after contrast medium injection(t=2.717,P=0.019).
(2) Results of histological grades
For the histological differentiation,25 HCCs had a single histological grade and 2 HCCs had two different histological grades. Finally, well-differentiated HCCs were in 6 patients, moderately-differentiated HCCs were in 10 patients and poorly-differentiated HCCs were in 11 patients.
(3) Results of visual Evaluations
On DWI, lof 27 HCCs appeared isointense,9 tumors appeared slightly hyperintense, and the remaining 17 tumors appeared obviously hyperintense to the surrounding liver. Overall,96.3% (26/27) of HCCs showed hyperintensity to the surrounding hepatic parenchyma. Fisher's exact test revealed no significant difference between visual signal intensity and histopathologic grade (p=0.567).
Results of ADC value and SI value Measurements There was a significant difference in the ADC values among the well-, moderately-and poorly-differentiated HCCs (F= 5.921, p= 0.008, one-way ANOVA test). The ADC value of the poorly-differentiated HCCs was significantly lower than that of the well-and moderately-differentiated HCCs (p= 0.004, p= 0.023, LSD test). There was no significant difference between the ADC value of the well- and moderately-differentiated HCCs(p= 0.281, LSD test). ROC analysis showed that the optimal cutoff point of ADC value in diagnosing poorly-differentiated HCC was 1.1450×10-3mm2/s. A cutoff ADC value equal to or less than 1.1450×10-3mm2/s was used to differentiate poorly-differentiated HCC from well-and moderately-differentiated with a sensitivity of 100% and specificity of 54.5%.
There was a significant difference in the SI values among the well-, moderately-and poorly-differentiated HCCs (F= 4.161, p= 0.028,one-way ANOVA test). The SI value of the well- and poorly-differentiated HCCs was significantly higher than that of the moderately- differentiated HCCs (p= 0.025, p= 0.019, LSD test). There was no significant difference between the SI value of the well- and poorly-differentiated HCCs(/p= 0.791, LSD test).
(4) Patterns of TIC
TypeⅠandⅡcurves were most seen in well- and moderately-differentiated HCCs(14/16). TypeⅢcurve was most likely seen in poorly differentiated HCCs (6/11)
Results of MSI and SER Measurements
There was a significant difference in the MSI values among the well-,moderately-and poorly-differentiated HCCs (F= 4.071, p= 0.030,one-way ANOVA test). The MSI value of the well-differentiated HCCs was significantly higher than that of the moderately- and poorly-differentiated HCCs (p= 0.019, p= 0.014, LSD test).There was no significant difference between the MSI value of the moderately- and poorly-differentiated HCCs(p= 0.934, LSD test).
No statistical difference was found in the SER values among the well-moderately-and poorly-differentiated HCCs (F= 2.758, p= 0.084,one-way ANOVA test).
4. Conclusions:
(1) The SNR and CNR of the DWI were significantly increased after contrast medium injection. Therefore, the lesion was easily to appeared. ADC values of the focal hepatic lesions significantly decreased after contrast medium injection. We suggest the DWI should be performed before contrast medium injection.
(2) There was a significant difference in the ADC values among the well-, moderately- and poorly-differentiated HCCs. The ADC value of the poorly-differentiated HCCs was significantly lower than that of the well- and moderately-differentiated HCCs. Therefore, DWI with ADC measurement may be a valuable tool for noninvasively predicting the differentiation of HCC. Although correct prediction of the histopathologic grade of HCC is not possible because of the large overlap among the ADC values.
(3)DCE-MRI could reflect the patterns of hemodynamic changes among the different histopathologic grade of HCCs. TypeⅠandⅡcurves were best seen in well-and moderately-differentiated HCCs. TypeⅢcurve was most likely seen in poorly differentiated HCCs. Patterns of TIC and semi-quantitative parameters obtained from DCE-MRI were useful in differenciating histopathologic grade of HCCs.
(1)探讨静脉注射Gd-DTPA后肝脏磁共振扩散加权成像(diffusion-weighted imaging, DWI)的信噪比(signal-to-noise ratio, SNR)、对比噪声比(contrast-to-noise ratio, CNR)及表观扩散系数(apparent diffusion coefficient, ADC)测量值的变化,以评价增强扫描后进行DWI的可行性。
(2)通过分析DWI图像肝细胞癌(hepatocellular carcinoma, HCC)的信号强度(signal intensity, SI)、ADC值和不同组织分化之间的关系,探讨扩散加权成像评价肝细胞癌组织分化程度的诊断价值。
(3)通过分析HCC磁共振动态增强(dynamic contrast-enhanced,DCE)的时间-信号强度曲线(time of intensity curve, TIC)类型及半定量参数,探讨磁共振动态增强对HCC组织分化程度的评价价值。
二、材料与方法：
1、研究对象
(1)第一部分：2008年11月至2009年6月经B超或CT检查发现肝脏局灶性病变而行MRI检查以进一步明确诊断的患者26例。其中男性19例,女性7例。年龄范围：37～67岁,平均年龄54.50±9.12岁。以病灶的最大径测量,范围：2.1～18cm,平均7.17±4.03cm。
(2)第二部分及第三部分：采用同一组病例,2008年11月至2010年3月在齐鲁医院做肝癌手术切除患者27例,组织病理诊断为肝细胞癌。其中男性24例,女性3例。年龄范围：36～68岁,平均年龄55.90±9.52岁。以病灶的最大径测量,最小为2cm,最大18cm,平均6.19±3.10cm。
2、扫描方法
所有研究对象的磁共振扫描均在同一台3.0T的磁共振仪(Signa Exite, GE Healthcare, Milwaukee, WI)上完成,信号采集使用8通道表面相阵控线圈。扫描序列包括T1WI T2WI、DWI及3D-FAME动态增强扫描。DWI采用b值600s/mm2。
3、图像分析方法
(1)第一部分测量在ADW4.2工作站(Advantage Window, GE Medical System, Milwaukee, WI)进行,利用Function2的扩散成像自动分析软件建立ADC图。选择病灶最大直径层面作为测量感兴趣区((region of interest, ROI)层面,分别测量注射Gd-DTPA前后病灶的ADC值及SI值,周围正常肝脏的SI值、背景的SD。同一个病灶注射Gd-DTPA前后的DWI图像、ADC图及放置感兴趣区位置尽可能保持一致。SNR及CNR的计算依次根据下述公式：SNR=SInormal/SDnoise, CNR= I SIlesion-SInormal I/SDnoise。
(2)第二部分
DWI图像目测评价DWI图像HCC的信号强度分为等信号、略高信号及高信号。
ADC值及SI值的测量
测量在ADW4.2工作站(Advantage Window, GE Medical System, Milwaukee, WI)进行,利用工作站中的Function2的扩散成像自动分析软件建立ADC图。选择病灶最大直径层面作为ROI层面,测量病灶的ADC值及SI值。
(3)第三部分所有DCE-MRI图像的观察及测量均在ADW4.2工作站(Advantage Window, GE Medical System, Milwaukee, WI)上进行,利用工作站中的function2动态强化自动分析软件进行分析,将感兴趣区(ROI)置于病灶最大直径层面的病灶区,自动生成TIC,分析曲线类型。并通过TIC获得半定量参数：最大上升斜率(maximum slop of increase,MSI)及信号增强率(signal enhancement ratio, SER)。
4、组织病理学
第二及第三部分病例均为HCC,依据Edmondson-Steiner's分级系统,肝细胞癌分化程度分为高分化、中分化及低分化。如果1个病灶中同时存在不同分化程度的肿瘤组织,则选择占主要优势组织的分化代表整个病灶的分化程度。
5、统计学分析
统计学分析采用SPSS 16.0统计软件进行资料录入和统计分析。数据测量结果以平均值士标准差(X±s)的形式描述。显著性水准均采用0.05(双侧),P值<0.05被认为有统计学意义。
第一部分病灶增强前后的SNR, CNR及ADC值均符合正态分布,增强前后均数的比较采用配对t检验。
第二部分采用Fisher's精确概率法检验目测观察DWI图像HCC信号强度的差异。三种不同分化程度HCC的ADC值及SI值均符合正态分布且满足方差齐性,采用单因素方差分析(one-way ANOVA),均数之间的两两比较采用LSD检验。采用ROC分析评价ADC值诊断低分化HCC的最佳界点和相应的诊断敏感性和特异性。
第三部分三种不同分化程度HCC的MSI值、SER值均符合正态分布且满足方差齐性,采用单因素方差分析(one-way ANOVA),均数之间的两两比较采用LSD检验。
三、结果
1.第一部分
(1).DWI图像目测观察结果注射对比剂前后在DWI图像观察到相同数目病灶,但注射对比剂后信号强度略有增高。
(2).静脉注射Gd-DTPA前后SNR、CNR及ADC值的测量结果
注射对比剂后病灶的ADC值较增强前降低,且二者之间有显著性统计学差异(t=2.717,P=0.019)。注射对比剂后DWI的SNR及CNR较增强前增高,且两者之间有显著性统计学差异(t=2.356,P=0.036；t=3.170,P=0.008)。
2.第二部分
(1).组织学结果
本组27个HCC病灶中,高分化HCC病灶6个,中分化HCC病灶10个低分化HCC病灶11个。
(2).DWI图像目测信号强度结果
本组资料27个HCC中,96.3%(26/27)在DWI表现为略高信号和高信号。三种不同分化HCC的目测信号强度之间无统计学差异(P=0.567,Fisher's精确概率法)。
(3).ADC值及SI值的测量结果
高、中、低分化HCC的平均ADC值表现降低的趋势,且三者之间有显著性差异(F= 5.921, P= 0.008, one-way ANOVA检验)。其中低分化HCC的ADC值明显低于高、中分化HCC(P= 0.004, P= 0.023, LSD检验)。虽然高分化HCC的ADC值高于中分化HCC的ADC值,但二者之间的ADC值无统计学差异(P=0.281,LSD检验)。ROC分析显示ADC值用于鉴别诊断低分化HCC及高中分化HCC有显著性意义(Az=0.790, P=0.012,95%CI:0.618-0.962). ADC值用于诊断低分化HCC的最佳界点为1.1450,此时诊断的敏感性和特异性分别为100%和54.5%。
高、中、低分化HCC的平均SI值之间有显著性差异(F=.4.161, P= 0.028, one-way ANOVA检验)。其中高、低分化HCC的SI值明显高于中分化HCC(P= 0.025, P= 0.019, LSD检验)。但高、低分化HCC的SI值之间无统计学差异(P=0.281,LSD检验)。
3.第三部分
(1).组织学结果同第二部分
(2).TIC类型
TICⅠ型及Ⅱ型最常见于高、中分化HCC (14/16)。TICⅢ型最常见于低分化HCC(6/11)。
(3).MSI及SER值的测量结果
高、中、低分化HCC的平均MSI值之间有显著性差异(F= 4.071,P=0.030, one-way ANOVA检验)。高分化HCC的MSI值明显高于中、低分化HCC(P= 0.019, P= 0.014, LSD检验)。中、低分化HCC之间的MSI值无统计学差异(P=0.934,LSD检验)。
高、中、低分化HCC的平均SER值之间无统计学差异(F= 2.758, P= 0.084, one-way ANOVA检验)。
四、结论
1.静脉注射Gd-DTPA后DWI图像的SNR及CNR不但没有降低,相反明显升高,所以DWI图像质量有所提高,与注射对比剂前DWI相比更易于显示病灶。静脉注射Gd-DTPA后ADC值明显减低。由于ADC值受多种因素影响,静脉注射Gd-DTPA后ADC的变化目前尚无明确结论,DWI图像采集最好选择在注射对比剂前进行。
2.不同分化程度HCC的ADC值之间有显著性差异,低分化HCC的ADC值明显低于高、中分化HCC。因此,ADC值的测量是一有价值的非侵袭性预测HCC分化程度的指标。因不同分化程度HCC的ADC值有较大的重叠,所以仅通过ADC值来正确预测HCC的组织分化程度是有一定限度的。
3. DCE-MRI在一定程度上反映了不同分化程度HCC的血流动力学特征,低分化HCC动脉血流有降低的趋势。TIC类型和半定量参数有助于不同分化程度HCC的预测,对于HCC治疗的选择及预后能提供有价值的影像信息。
1. Purpose:
(1) To evaluate whether administration of gadolinium-based contrast material significantly affects diffusion-weighted imaging(DWI) and apparent diffusion coefficient(ADC) values at the focal hepatic lesions.
(2) To evaluate the relationship between the signal intensity of hepatocellular carcinoma (HCC) assessed with diffusion-weighted imaging(DWI) and the apparent diffusion coefficient (ADC) with the histopathologic grade of HCC.
(3) To evaluate the diagnostic value of dynamic contrast-enhanced MRI(DCE-MRI) in predicting histopathologic grade of HCC by analysis of time of intensity curve(TIC) types and semi-quantitative parameters.
2. Materials and methods:
(1) Study population
①From November 2008 to June 2009,26 patient, including 19 males and 7 females, who were diagnosed as focal hepatic lesions. The age range was 37-67 years, with the average age of 54.50±9.12 years. The diameter of the lesions ranged from 2.1-18cm in the maximum diameter(mean,7.17±4.03 cm).
②From November 2008 to March 2010,27 patients, including 24 males and 3 females, who were diagnosed as HCC. The age range was 36-38 years, with the average age of 55.90±9.52 years. The diameter of the HCCs ranged from 2.0-18cm in the maximum diameter(mean,6.19±3.10 cm). (2) MRI
MRI was performed with a 3.0T system (GE Signa Excite, GE Healthcare, Milwaukee, WI) with a phased-array multicoil for the body. The MRI protocols included the T1-weighted, T2-weighted, DWI and 3D-FAME dynamic contrast-enhanced imaging. DWI was obtained using single-shot echo planar imaging with b value of 600 s/mm2.
(3) Image analysis
①By copying and pasting ROIs, the SI value, the SDnoise value and the ADC value were measured. SNR and CNR of each lesion was calculated using the formula SNR=SInormal/SDnoise, CNR=| Sllesion-SInormal|/SDnoise.
②Visual assessment
The same two radiologists visually assessed the signal intensity of all tumors and classified them into isointense, slightly hyperintense or obviously hyperintense by mutual agreement.
ADC value and SI value of each tumor were measured.
③TICs were classified into three types.MSI and SER were derived from TIC.The MSI value and SER value of HCCs were measured.
(4) Histopathology
The differentiation of an HCC was classified into well, moderate and poor according to Edmondson-Steiner's grading system. When different tumor grades coexisted within a tumor, the more predominant differentiation of the tumor was selected.
(5) Statistical analysis
All the statistical calculations were performed using the statistical software SPSS16.0. The results of measured values were recorded in the format of mean±standard deviation'. p< 0.05 was considered statistically significant in all the statistical analyses.
SNR, CNR and ADC values obtained on pre-contrast and post-contrast were analyzed using Student's paired t test. The difference of the ADC value, SI value, MSI value and SER value was compared with the histopathologic grade using the one-way ANOVA and LSD test. ROC analysis was used to analyse the diagnostic value of ADC value in differentiating poorly-differentiated HCC from well- and moderately-differentiated HCC. The optimal cutoff point of ADC value and the correspongding sensitivity and specificity were determined.
Fisher's exact test was performed to assess the statistical difference between the histopathologic grades of HCC and visually evaluated signal intensity on DWI.
3. Results:
(1) The SNR and CNR of the DWI were significantly increased after contrast medium injection (t=2.356, p=0.036; t=3.170, p=0.008). ADC values of the focal hepatic lesions significantly decreased after contrast medium injection(t=2.717,P=0.019).
(2) Results of histological grades
For the histological differentiation,25 HCCs had a single histological grade and 2 HCCs had two different histological grades. Finally, well-differentiated HCCs were in 6 patients, moderately-differentiated HCCs were in 10 patients and poorly-differentiated HCCs were in 11 patients.
(3) Results of visual Evaluations
On DWI, lof 27 HCCs appeared isointense,9 tumors appeared slightly hyperintense, and the remaining 17 tumors appeared obviously hyperintense to the surrounding liver. Overall,96.3% (26/27) of HCCs showed hyperintensity to the surrounding hepatic parenchyma. Fisher's exact test revealed no significant difference between visual signal intensity and histopathologic grade (p=0.567).
Results of ADC value and SI value Measurements There was a significant difference in the ADC values among the well-, moderately-and poorly-differentiated HCCs (F= 5.921, p= 0.008, one-way ANOVA test). The ADC value of the poorly-differentiated HCCs was significantly lower than that of the well-and moderately-differentiated HCCs (p= 0.004, p= 0.023, LSD test). There was no significant difference between the ADC value of the well- and moderately-differentiated HCCs(p= 0.281, LSD test). ROC analysis showed that the optimal cutoff point of ADC value in diagnosing poorly-differentiated HCC was 1.1450×10-3mm2/s. A cutoff ADC value equal to or less than 1.1450×10-3mm2/s was used to differentiate poorly-differentiated HCC from well-and moderately-differentiated with a sensitivity of 100% and specificity of 54.5%.
There was a significant difference in the SI values among the well-, moderately-and poorly-differentiated HCCs (F= 4.161, p= 0.028,one-way ANOVA test). The SI value of the well- and poorly-differentiated HCCs was significantly higher than that of the moderately- differentiated HCCs (p= 0.025, p= 0.019, LSD test). There was no significant difference between the SI value of the well- and poorly-differentiated HCCs(/p= 0.791, LSD test).
(4) Patterns of TIC
TypeⅠandⅡcurves were most seen in well- and moderately-differentiated HCCs(14/16). TypeⅢcurve was most likely seen in poorly differentiated HCCs (6/11)
Results of MSI and SER Measurements
There was a significant difference in the MSI values among the well-,moderately-and poorly-differentiated HCCs (F= 4.071, p= 0.030,one-way ANOVA test). The MSI value of the well-differentiated HCCs was significantly higher than that of the moderately- and poorly-differentiated HCCs (p= 0.019, p= 0.014, LSD test).There was no significant difference between the MSI value of the moderately- and poorly-differentiated HCCs(p= 0.934, LSD test).
No statistical difference was found in the SER values among the well-moderately-and poorly-differentiated HCCs (F= 2.758, p= 0.084,one-way ANOVA test).
4. Conclusions:
(1) The SNR and CNR of the DWI were significantly increased after contrast medium injection. Therefore, the lesion was easily to appeared. ADC values of the focal hepatic lesions significantly decreased after contrast medium injection. We suggest the DWI should be performed before contrast medium injection.
(2) There was a significant difference in the ADC values among the well-, moderately- and poorly-differentiated HCCs. The ADC value of the poorly-differentiated HCCs was significantly lower than that of the well- and moderately-differentiated HCCs. Therefore, DWI with ADC measurement may be a valuable tool for noninvasively predicting the differentiation of HCC. Although correct prediction of the histopathologic grade of HCC is not possible because of the large overlap among the ADC values.
(3)DCE-MRI could reflect the patterns of hemodynamic changes among the different histopathologic grade of HCCs. TypeⅠandⅡcurves were best seen in well-and moderately-differentiated HCCs. TypeⅢcurve was most likely seen in poorly differentiated HCCs. Patterns of TIC and semi-quantitative parameters obtained from DCE-MRI were useful in differenciating histopathologic grade of HCCs.
引文
1. Hussain SM, Semelka RC, Mitchell DG. MR imaging of hepatocellular Carcinoma. Magn Reson Imaging Clin N Am,2002,10(1):31-52.
2. Matsui O. Imaging of multistep human hepatocarcinogenesis by CT during intraarterial contrast injection. Intervirology,2004,47(3-5):271-276.
3. Nakajima Y, Shimamura T, Kamiyama T, et al.Evaluation of surgical resection for small hepatocellular carcinomas. Am J Surg,1996,171(3):360-363.
4. Hayashi M, Matsui O, Ueda K, et al. Correlation between the blood supply and grade of malignancy of hepatocellular nodules associated with liver cirrhosis:evaluation by CT during intraarterial injection of contrast medium. AJR, 1999,172(4):969-976.
5. Karahan OI, Yikilmaz A, Isin S, et al. Characterization of hepatocellular carcinomas with triphasic CT and correlation with histopathologic findings. Acta Radiol,2003,44(6):1-6.
6.Rummeny E, Weissleder R, Stark DD, et al. Primary liver tumors:diagnosis by MR imaging. Am J Roentgenol,1989,152(1):163-172.
7. Stevens WR, Johnson CD, Stephens DH, et al. CT findings in hepatocellular carcinoma:correlation of tumor characteristics with causative factors, tumor size, and histologic tumor grade. Radiology,1994,191(2):531-537.
8. Heo SH, Jeong YY, Shin SS,et al.Apparent Diffusion Coefficient Value of Diffusion-Weighted Imaging for Hepatocellular Carcinoma:Correlation with the Histologic Differentiation and the Expression of Vascular Endothelial Growth Factor. Korean J Radiol,2010,11(3):295-303.
9.Nasu K, Kuroki Y, Tsukamoto T,et al. Diffusion-weighted imaging of surgically resected hepatocellular carcinoma:imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol,2009,193 (2):438-444.
1. Nicholson C, Phillips JM. Ion diffusion modified by tortuosity and volume fraction in the extracellular microenvironment of the rat cerebellum. J Physiol, 1981;321:225-57.
2. Szafer A, Zhong J, AndersonAW, et al. Diffusion-weighted imaging in tissues: theoretical models. NMR Biomed,1995,8(7-8):289-96.
3. Szafer A, Zhong J, Gore JC. Theoretical model for water diffusion in tissues. Magn Reson Med,1995,33(5):697-712.
4. Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology,1988,168(2):497-505.
5. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. Am J Roentgenol,2007,188(6):1622-35.
6. Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology,2010, 254(1):47-66.
7. Yamada K, Kubota H, Kizu O, et al. Effect of intravenous gadolinium-DTPA on diffusion weighted images:evaluation of normal brain and infarcts. Stroke,2002, 33(7):1799-1802.
8. Fitzek C, Mentzel HJ, Fitzek S, et al. Echoplanar diffusion-weighted MRI with intravenous gadolinium-DTPA. Neuroradiology,2003,45(9):592-597.
9. Firat AK, Sanh B, Karakas HM, et al. The effect of intravenous gadolinium-DTPA on diffusion-weighted imaging. Neuroradiology,2006,48(7):465-470.
10. Chen G, Jespersen SN, Pedersen M, et al.Intravenous administration of Gd-DTPA prior to DWI does not affect the apparent diffusion constant. Magn Reson Imaging,2005,23(5):685-689.
11.王岳,武春雪,杨学东,等.静脉注射钆类对比剂对乳腺正常腺体及肿瘤组织MR扩散加权成像的影响.中华放射学杂志,2009,43(5)：476-479.
1. Le Bihan D, Breton E, Lallemand D,et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology,1988,168(2):497-505.
2. Guo Y, Cai YQ, Cai ZL, et al. Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging,2002, 16(2):172-178.
3. Gauvain KM, McKinstry RC, Mukherjee P, et al.Evaluating pediatric brain tumor cellularity with diffusion-tensor imaging. Am J Roentgenol,2001,177(2):449-454.
4. Sugahara T, Korogi Y, Kochi M, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging,1999,9(1):53-60.
5. Lang P, Wendland MF, Saeed M, et al. Osteogenic sarcoma:noninvasive in vivo assessment of tumor necrosis with diffusion-weighted MR imaging. Radiology, 1998,206(1):227-235.
6.Charles-Edwards EM, deSouza NM.Diffusion-weighted magnetic resonance imaging and its application to cancer.Cancer Imaging,2006,13(6):135-43. Review
7. Parikh T, Drew SJ, Lee VS, et al. Focal liver lesion detection and characterization with diffusion-weighted MR imaging:comparison with standard breath-hold T2-weighted imaging. Radiology,2008,246(3):812-22.
8. Nasu K, Kuroki Y, Nawano S, et al. Hepatic metastases:diffusion weighted sensitivity-encoding versus SPIO-enhanced MR Imaging. Radiology,2006, 239(1):122-130.
9. Coenegrachts K, Delanote J, Ter Beek L, et al. Improved focal liver lesion detection:comparison of single-shot diffusion-weighted echoplanar and single-shot T2 weighted turbo spin echo techniques. Br J Radiol,2007,80(955):524-531.
10. Bruegel M, Gaa J, Waldt S, et al. Diagnosis of hepatic metastasis:comparison of respiration-triggered diffusion-weighted echo-planar MRI and five T2-weighted turbo spin-echo sequences.AmJ Roentgenol,2008,191 (5):1421-1429.
11. Zech CJ, Herrmann KA, Dietrich O,et al. Black-blood diffusion-weighted EPI acquisition of the liver with parallel imaging:comparison with a standard T2-weighted sequence for detection of focal liver lesions. Invest Radiol, 2008,43(4):261-266.
12. Hardie AD, Naik M, Hecht EM, et al. Diagnosis of liver metastases:value of diffusion-weighted MRI compared with gadolinium-enhanced MRI. Eur Radiol, 2010,20(6):1431-41.
13. Nishie A, Tajima T, Ishigami K, et al. Detection of hepatocellular carcinoma(HCC) using super paramagnetic iron oxide (SPIO)-enhanced MRI:Added value of diffusion-weighted imaging (DWI).J Magn Reson Imaging,2010,31(2):373-82.
14. Xu PJ, Yan FH, Wang JH, et al. Added value of breathhold diffusion-weighted MRI in detection of small hepatocellular carcinoma lesions compared with dynamic contrast-enhanced MRI alone using receiver operating characteristic curve analysis. J Magn Reson Imaging,2009,29(2):341-9.
15. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. AJR Am J Roentgenol,2007,188(6):1622-1635.
16. Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic resonance imaging:a biomarker for treatment response in oncology.J Clin Oncol,2007,25(26): 4104-4109.
17.Sharma U, Danishad KK, Seenu V,et al. Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed, 2009,22(l):104-113.
18.Thoeny HC, De Keyzer F, Vandecaveye V,et al. Effect of vascular targeting agent in rat tumor model:dynamic contrast-enhanced versus diffusion-weighted MR imaging. Radiology,2005,237(2):492-499.
19.Mardor Y, Roth Y, Ochershvilli A, et al.Pretreatment prediction of brain tumors'response to radiation therapy using high b-value diffusion-weighted MRI. Neoplasia,2004,6(2):136-142.
20. Patterson DM, Padhani AR, Collins DJ.Technology insight:water diffusion MRI-a potential new biomarker of response to cancer therapy.Nat Clin Pract Oncol,2008,5(4):220-233.
21. El-Serag HB. Hepatocellular carcinoma:an epidemiologic view. J Clin Gastroenterol,2002,35(5 Suppl 2):S72-8.
22. Okuda K, Ohtsu T, Obata H, et al. Natural history of hepatocellular carcinoma and prognosis in relation to treatment:study of 850 patients. Cancer,1985, 56(4):918-928.
23.Komorizono Y, Oketani M, Sako K, et al. Risk factors for local recurrence of small hepatocellular carcinoma tumors after a single session, single application of percutaneous radiofrequency ablation. Cancer,2003,97(5):1253-1262.
24.Nagasue N, Uchida M, Makino Y, et al. Incidence and factors associated with intrahepatic recurrence following resection of hepatocellular carcinoma. Gastroenterology,1993,105(2):488-493.
25.Ng IO, Lai EC, Fan ST,et al. Prognostic significance of pathologic features of hepatocellular carcinoma:a multivariate analysis of 278 patients. Cancer,1995, 76(12):2443-2448.
26.Okusaka T, Okada S, Ueno H, et al. Satellite lesions in patients with small hepatocellular carcinoma with reference to clinicopathological features. Cancer, 2002,95(9):1931-1937.
27. Okusaka T, Okada S, Ueno H, et al. Clinicopathological features of poorly differentiated hepatocellular carcinoma. J Surg Oncol,2007,95:311-316.
28.Sugahara T, Korogi Y, Kochi M, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging,1999,9(1):53-60.
29.Schabitz WR, Fisher M. Diffusion weighted imaging for acute cerebral infarction. Neurol Res,1995,17(4):270-274.
30. Nasu K, Kuroki Y, Kuroki S,et al. Diffusion-weighted single shot echo planar imaging of colorectal cancer using a sensitivity-encoding technique.Jpn J Clin Oncol,2004,34(10):620-626.
31.Kuroki Y, Nasu K, Kuroki K, et al. Diffusion weighted imaging of breast cancer with the apparent diffusion coefficient value. Magn Reson Med Sci,2004, 3(2):79-85.
32. Tanimoto A, Nakashima J, Kohn H, et al. Prostate cancer screening:the clinical value of diffusion-weighted imaging and dynamic MR imaging in combination with T2- weighted imaging. J Magn Reson Imaging,2007,25(1):146-152.
33. Shimofusa R, Fujimoto H, Akamata H, et al. Diffusion-weighted imaging of prostate cancer. J Comput Assist Tomogr,2005,29(2):149-153.
34.Nasu K, Kuroki Y, Tsukamoto T, et al. Diffusion-weighted imaging of surgically resected hepatocellular carcinoma:imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol,2009,193(2):438-444.
35. Xu H, Li X, Xie JX, et al. Diffusion-weighted magnetic resonance imaging of focal hepatic nodules in an experimental hepatocellular carcinoma rat model. Acad Radiol,2007,14(3):279-286.
36. Heo SH, Jeong YY, Shin SS,et al.Apparent Diffusion Coefficient Value of Diffusion-Weighted Imaging for Hepatocellular Carcinoma:Correlation with the Histologic Differentiation and the Expression of Vascular Endothelial Growth Factor. Korean J Radiol,2010,11(3):295-303.
37. Asayama Y, Yoshimitsu K, Nishihara Y, et al. Arterial blood supply of hepatocellular carcinoma and histologic grading:radiologic -pathologic correlation. AJR, Am J Roentgenol,2008,190(1):W28-34.
38.Kanematsu M, Osada S, Amaoka N, et al. Expression of vascular endothelial growth factor in hepatocellular carcinoma and the surrounding liver:correlation with angiographically assisted CT. AJR,2004,183(6):1585-1593.
39.汪俊萍,白人驹,孙浩然,等.兔VX2肝癌模型的MR扩散加权成像与肿瘤恶性程度的相关性[J].中华医学杂志,2009,89(31)：2210-2213.
1.Hussain SM, Semelka RC, Mitchell DG. MR imaging of hepatocellular Carcinoma. Magn Reson Imaging Clin N Am,2002,10(1):31-52.
2.Matsui O. Imaging of multistep human hepatocarcinogenesis by CT during intraarterial contrast injection. Intervirology,2004,47(3-5):271-276.
3.Hayashi M, Matsui O, Ueda K, et al. Correlation between the blood supply and grade of malignancy of hepatocellular nodules associated with liver cirrhosis:evaluation by CT during intraarterial injection of contrast medium. AJR, 1999,172(4):969-976.
4.Nakajima Y, Shimamura T, Kamiyama T, et al.Evaluation of surgical resection for small hepatocellular carcinomas. Am J Surg,1996,171(3):360-363.
5.Karahan OI, Yikilmaz A, Isin S, et al. Characterization of hepatocellular carcinomas with triphasic CT and correlation with histopathologic findings. Acta Radiol,2003, 44(6):566-71.
6.Rummeny E, Weissleder R, Stark DD, et al. Primary liver tumors diagnosis by MR imaging. Am J Roentgenol,1989,152(1):63-72.
7.Stevens WR, Johnson CD, Stephens DH, et al. CT findings in hepatocellular carcinoma:correlation of tumor characteristics with causative factors, tumor size, and histologic tumor grade. Radiology,1994,191(2):531-7.
8.Semelka RC, Helmberger TKG. Contrast agents for MR imaging of the liver. Radiology,2001,218(1):27-38.
9.Bellin M-F. MR contrast agents, the old and the new. Eur J Radiol,2006, 60(3):314-23.
1O.Gandhi SN, Brown MA, Wong JG, et al. MR contrast agents for liver imaging: what, when, how. Radiographics,2006,26(6):1621-36.
11. Hulka CA, Edmister WB, Smit h BL, et al. Dynamic echo-planar imaging of t he breast:experience in diagnosing breast carcinoma and correlation with tumor angiogenesis. Radiology,1997,205(3):837-842.
12. Hawighorst H,Knapstein PG,Knopp MV,et al.Cervical carcinoma:standard and pharmacokinetic analysis of time-intensity curves for assessment of tumor angiogenesis and patient survival. MAGMA,1999,8(1):55-62.
13. Semelka RC, Martin DR, Balci C, et al. Focal liver lesions:Comparison of dual-phase CT and multisequence multiplanar MR imaging including dynamic gadolinium enhancement. J Magn Reson,2001,13(3):397-401.
14.Wang J, Chen LT, Tsang YM, et al. Dynamic contrast-enhanced MRI analysis of perfusion changes in advanced hepatocellular carcinoma treated with an antiangiogenic agent:a preliminary study. Am J Roentgenol, 2004,183(3):713-9.
15.Tajima T, Honda H, Taguchi K, et al. Sequential hemodynamic change in hepatocellular carcinoma and dysplastic nodules:CT angiography and pathologic correlation. AJR,2002,178(4):885-897.
16.Asayama Y, Yoshimitsu K, Irie H, et al. Poorly versus moderately differentiated hepatocellular carcinoma:vascularity assessment by computed tomographic hepatic angiography in correlation with histologically counted number of unpaired arteries. J Comput Assist Tomogr,2007,31(2):188-192.
17.Asayama Y, Yoshimitsu K, Nishihara Y, et al.Arterial Blood Supply of Hepatocellular Carcinoma and Histologic Grading:Radiologic-Pathologic Correlation. AJR,2008,190(1):W28-W34.
18.Mineura K, Yasuda T, Kowada M,et al. PET evaluation of histologic malignancy in gliomas using 150 and 18F-FDG. Neurol Res,1986,8(3):164-168.
1. Ince N, Wands JR. The increasing incidence of hepatocellular carcinoma. N Engl J Med 1999; 340(10):798-799.
2. Tung-Ping Poon R, Fan ST, Wong J. Risk factors, prevention, and management of postoperative recurrence after resection of hepatocellular carcinoma. Ann Surg 2000; 232(1):10-24.
3. Haratake J, Takeda S, Kasai T, et al. Predictable factors for estimating prognosis of patients after resection of hepatocellular carcinoma. Cancer 1993; 72(4):1178-1183.
4. Kim SH, Lee WJ, Lim HK, et al. SPIO-enhanced MRI findings of well-differentiated hepatocellular carcinomas:correlation with MDCT findings. Korean J Radiol 2009; 10(2):112-120.
5. Nasu K, Kuroki Y, Tsukamoto T, et al. Diffusion-weighted imaging of surgically resected hepatocellular carcinoma:imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol 2009; 193(2):438-444.
6. Maeda M, Kato H, Sakuma H, et al. Usefulness of the apparent diffusion coefficient in line scan diffusionweighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck.AJNR Am J Neuroradiol 2005; 26(5):1186-1192.
7. Wang J, Takashima S, Takayama F, et al. Head and neck lesions:characterization with diffusion-weighted echo-planar MR imaging. Radiology 2001; 220(3):621-630.
8. Sumi M, Sakihama N, Sumi T, et al. Discrimination of metastatic cervical lymph nodes with diffusion-weighted MR imaging in patients with head and neck cancer. AJNR Am J Neuroradiol 2003; 24(8):1627-1634.
9. Xu H, Li X, Xie JX, et al. Diffusion-weighted magnetic resonance imaging of focal hepatic nodules in an experimental hepatocellular carcinoma rat model. Acad Radiol 2007;14(3):279-286.
10. Guo AC, Cummings TJ, Dash RC, et al. Lymphomas and high-grade astrocytomas:comparison of water diffusibility and histologic characteristics. Radiology 2002;224(1):177-183.
11. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. AJR Am J Roentgenol 2007;188(6):1622-1635.
12. Edmondson HA, Steiner PE. Primary carcinoma of the liver:a study of 100 cases among 48,900 necropsies. Cancer 1954;7(3):462-503.
1. Low RN. Abdominal MRI advances in the detection of liver tumours and characterisation. Lancet Oncol 2007; 8(6):525-35.
2. Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology 2010; 254(1):47-66.
3. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. Am J Roentgenol 2007; 188(6):1622-35.
4. Yamada K, Kubota H, Kizu O, et al. Effect of intravenous gadolinium-DTPA on diffusion-weighted images:evaluation of normal brain and infarcts. Stroke 2002, 33(7):1799-1802.
5. Zhong J, Kennan RP, Fulbright RK, et al. Quantification of intra- and extravascular contributions to BOLD effects induced by alteration of oxygenation or intravascular contrast agent. Magn Reson Med 1998; 40(4):526-536.
6:Fitzek C, Mentzel HJ, Fitzek S, et al. Echoplanar diffusion-weighted MRI with intravenous gadolinium-DTPA.Neuroradiology 2003,45(9):592-597
7. Le Bihan D, Breton E, Lallemand D, et al.Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988; 168(2):497-505.
8. Kidwell CS, Alger JR, Di Salle F, et al. Diffusion MRI in patients with transient ischemic attacks. Stroke 1999; 30(12):2762-2763.
9. Xing D, Papadakis NG, Huang CL, et al.Optimized diffusion-weighting for measurement of apparent diffusion coefficient (ADC) in human brain. Magn Reson Imaging 1997;15(7):771-784.
10. Wolf RL, Zimmerman RA, Clancy R, et al. Quantitative apparent diffusion coefficient measurements in term neonates for early detection of hypoxic-ischemic brain injury: initial experience. Radiology 2001; 218(3):825-833.
11.Firat AK, nli B, Karakas. HM, et al. The effect of intravenous gadolinium-DTPA on diffusion-weighted imaging. Neuroradiology 2006,48(7):465-470.
2. Matsui O. Imaging of multistep human hepatocarcinogenesis by CT during intraarterial contrast injection. Intervirology,2004,47(3-5):271-276.
3. Nakajima Y, Shimamura T, Kamiyama T, et al.Evaluation of surgical resection for small hepatocellular carcinomas. Am J Surg,1996,171(3):360-363.
4. Hayashi M, Matsui O, Ueda K, et al. Correlation between the blood supply and grade of malignancy of hepatocellular nodules associated with liver cirrhosis:evaluation by CT during intraarterial injection of contrast medium. AJR, 1999,172(4):969-976.
5. Karahan OI, Yikilmaz A, Isin S, et al. Characterization of hepatocellular carcinomas with triphasic CT and correlation with histopathologic findings. Acta Radiol,2003,44(6):1-6.
6.Rummeny E, Weissleder R, Stark DD, et al. Primary liver tumors:diagnosis by MR imaging. Am J Roentgenol,1989,152(1):163-172.
7. Stevens WR, Johnson CD, Stephens DH, et al. CT findings in hepatocellular carcinoma:correlation of tumor characteristics with causative factors, tumor size, and histologic tumor grade. Radiology,1994,191(2):531-537.
8. Heo SH, Jeong YY, Shin SS,et al.Apparent Diffusion Coefficient Value of Diffusion-Weighted Imaging for Hepatocellular Carcinoma:Correlation with the Histologic Differentiation and the Expression of Vascular Endothelial Growth Factor. Korean J Radiol,2010,11(3):295-303.
9.Nasu K, Kuroki Y, Tsukamoto T,et al. Diffusion-weighted imaging of surgically resected hepatocellular carcinoma:imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol,2009,193 (2):438-444.
1. Nicholson C, Phillips JM. Ion diffusion modified by tortuosity and volume fraction in the extracellular microenvironment of the rat cerebellum. J Physiol, 1981;321:225-57.
2. Szafer A, Zhong J, AndersonAW, et al. Diffusion-weighted imaging in tissues: theoretical models. NMR Biomed,1995,8(7-8):289-96.
3. Szafer A, Zhong J, Gore JC. Theoretical model for water diffusion in tissues. Magn Reson Med,1995,33(5):697-712.
4. Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology,1988,168(2):497-505.
5. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. Am J Roentgenol,2007,188(6):1622-35.
6. Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology,2010, 254(1):47-66.
7. Yamada K, Kubota H, Kizu O, et al. Effect of intravenous gadolinium-DTPA on diffusion weighted images:evaluation of normal brain and infarcts. Stroke,2002, 33(7):1799-1802.
8. Fitzek C, Mentzel HJ, Fitzek S, et al. Echoplanar diffusion-weighted MRI with intravenous gadolinium-DTPA. Neuroradiology,2003,45(9):592-597.
9. Firat AK, Sanh B, Karakas HM, et al. The effect of intravenous gadolinium-DTPA on diffusion-weighted imaging. Neuroradiology,2006,48(7):465-470.
10. Chen G, Jespersen SN, Pedersen M, et al.Intravenous administration of Gd-DTPA prior to DWI does not affect the apparent diffusion constant. Magn Reson Imaging,2005,23(5):685-689.
11.王岳,武春雪,杨学东,等.静脉注射钆类对比剂对乳腺正常腺体及肿瘤组织MR扩散加权成像的影响.中华放射学杂志,2009,43(5)：476-479.
1. Le Bihan D, Breton E, Lallemand D,et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology,1988,168(2):497-505.
2. Guo Y, Cai YQ, Cai ZL, et al. Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging,2002, 16(2):172-178.
3. Gauvain KM, McKinstry RC, Mukherjee P, et al.Evaluating pediatric brain tumor cellularity with diffusion-tensor imaging. Am J Roentgenol,2001,177(2):449-454.
4. Sugahara T, Korogi Y, Kochi M, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging,1999,9(1):53-60.
5. Lang P, Wendland MF, Saeed M, et al. Osteogenic sarcoma:noninvasive in vivo assessment of tumor necrosis with diffusion-weighted MR imaging. Radiology, 1998,206(1):227-235.
6.Charles-Edwards EM, deSouza NM.Diffusion-weighted magnetic resonance imaging and its application to cancer.Cancer Imaging,2006,13(6):135-43. Review
7. Parikh T, Drew SJ, Lee VS, et al. Focal liver lesion detection and characterization with diffusion-weighted MR imaging:comparison with standard breath-hold T2-weighted imaging. Radiology,2008,246(3):812-22.
8. Nasu K, Kuroki Y, Nawano S, et al. Hepatic metastases:diffusion weighted sensitivity-encoding versus SPIO-enhanced MR Imaging. Radiology,2006, 239(1):122-130.
9. Coenegrachts K, Delanote J, Ter Beek L, et al. Improved focal liver lesion detection:comparison of single-shot diffusion-weighted echoplanar and single-shot T2 weighted turbo spin echo techniques. Br J Radiol,2007,80(955):524-531.
10. Bruegel M, Gaa J, Waldt S, et al. Diagnosis of hepatic metastasis:comparison of respiration-triggered diffusion-weighted echo-planar MRI and five T2-weighted turbo spin-echo sequences.AmJ Roentgenol,2008,191 (5):1421-1429.
11. Zech CJ, Herrmann KA, Dietrich O,et al. Black-blood diffusion-weighted EPI acquisition of the liver with parallel imaging:comparison with a standard T2-weighted sequence for detection of focal liver lesions. Invest Radiol, 2008,43(4):261-266.
12. Hardie AD, Naik M, Hecht EM, et al. Diagnosis of liver metastases:value of diffusion-weighted MRI compared with gadolinium-enhanced MRI. Eur Radiol, 2010,20(6):1431-41.
13. Nishie A, Tajima T, Ishigami K, et al. Detection of hepatocellular carcinoma(HCC) using super paramagnetic iron oxide (SPIO)-enhanced MRI:Added value of diffusion-weighted imaging (DWI).J Magn Reson Imaging,2010,31(2):373-82.
14. Xu PJ, Yan FH, Wang JH, et al. Added value of breathhold diffusion-weighted MRI in detection of small hepatocellular carcinoma lesions compared with dynamic contrast-enhanced MRI alone using receiver operating characteristic curve analysis. J Magn Reson Imaging,2009,29(2):341-9.
15. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. AJR Am J Roentgenol,2007,188(6):1622-1635.
16. Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic resonance imaging:a biomarker for treatment response in oncology.J Clin Oncol,2007,25(26): 4104-4109.
17.Sharma U, Danishad KK, Seenu V,et al. Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed, 2009,22(l):104-113.
18.Thoeny HC, De Keyzer F, Vandecaveye V,et al. Effect of vascular targeting agent in rat tumor model:dynamic contrast-enhanced versus diffusion-weighted MR imaging. Radiology,2005,237(2):492-499.
19.Mardor Y, Roth Y, Ochershvilli A, et al.Pretreatment prediction of brain tumors'response to radiation therapy using high b-value diffusion-weighted MRI. Neoplasia,2004,6(2):136-142.
20. Patterson DM, Padhani AR, Collins DJ.Technology insight:water diffusion MRI-a potential new biomarker of response to cancer therapy.Nat Clin Pract Oncol,2008,5(4):220-233.
21. El-Serag HB. Hepatocellular carcinoma:an epidemiologic view. J Clin Gastroenterol,2002,35(5 Suppl 2):S72-8.
22. Okuda K, Ohtsu T, Obata H, et al. Natural history of hepatocellular carcinoma and prognosis in relation to treatment:study of 850 patients. Cancer,1985, 56(4):918-928.
23.Komorizono Y, Oketani M, Sako K, et al. Risk factors for local recurrence of small hepatocellular carcinoma tumors after a single session, single application of percutaneous radiofrequency ablation. Cancer,2003,97(5):1253-1262.
24.Nagasue N, Uchida M, Makino Y, et al. Incidence and factors associated with intrahepatic recurrence following resection of hepatocellular carcinoma. Gastroenterology,1993,105(2):488-493.
25.Ng IO, Lai EC, Fan ST,et al. Prognostic significance of pathologic features of hepatocellular carcinoma:a multivariate analysis of 278 patients. Cancer,1995, 76(12):2443-2448.
26.Okusaka T, Okada S, Ueno H, et al. Satellite lesions in patients with small hepatocellular carcinoma with reference to clinicopathological features. Cancer, 2002,95(9):1931-1937.
27. Okusaka T, Okada S, Ueno H, et al. Clinicopathological features of poorly differentiated hepatocellular carcinoma. J Surg Oncol,2007,95:311-316.
28.Sugahara T, Korogi Y, Kochi M, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging,1999,9(1):53-60.
29.Schabitz WR, Fisher M. Diffusion weighted imaging for acute cerebral infarction. Neurol Res,1995,17(4):270-274.
30. Nasu K, Kuroki Y, Kuroki S,et al. Diffusion-weighted single shot echo planar imaging of colorectal cancer using a sensitivity-encoding technique.Jpn J Clin Oncol,2004,34(10):620-626.
31.Kuroki Y, Nasu K, Kuroki K, et al. Diffusion weighted imaging of breast cancer with the apparent diffusion coefficient value. Magn Reson Med Sci,2004, 3(2):79-85.
32. Tanimoto A, Nakashima J, Kohn H, et al. Prostate cancer screening:the clinical value of diffusion-weighted imaging and dynamic MR imaging in combination with T2- weighted imaging. J Magn Reson Imaging,2007,25(1):146-152.
33. Shimofusa R, Fujimoto H, Akamata H, et al. Diffusion-weighted imaging of prostate cancer. J Comput Assist Tomogr,2005,29(2):149-153.
34.Nasu K, Kuroki Y, Tsukamoto T, et al. Diffusion-weighted imaging of surgically resected hepatocellular carcinoma:imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol,2009,193(2):438-444.
35. Xu H, Li X, Xie JX, et al. Diffusion-weighted magnetic resonance imaging of focal hepatic nodules in an experimental hepatocellular carcinoma rat model. Acad Radiol,2007,14(3):279-286.
36. Heo SH, Jeong YY, Shin SS,et al.Apparent Diffusion Coefficient Value of Diffusion-Weighted Imaging for Hepatocellular Carcinoma:Correlation with the Histologic Differentiation and the Expression of Vascular Endothelial Growth Factor. Korean J Radiol,2010,11(3):295-303.
37. Asayama Y, Yoshimitsu K, Nishihara Y, et al. Arterial blood supply of hepatocellular carcinoma and histologic grading:radiologic -pathologic correlation. AJR, Am J Roentgenol,2008,190(1):W28-34.
38.Kanematsu M, Osada S, Amaoka N, et al. Expression of vascular endothelial growth factor in hepatocellular carcinoma and the surrounding liver:correlation with angiographically assisted CT. AJR,2004,183(6):1585-1593.
39.汪俊萍,白人驹,孙浩然,等.兔VX2肝癌模型的MR扩散加权成像与肿瘤恶性程度的相关性[J].中华医学杂志,2009,89(31)：2210-2213.
1.Hussain SM, Semelka RC, Mitchell DG. MR imaging of hepatocellular Carcinoma. Magn Reson Imaging Clin N Am,2002,10(1):31-52.
2.Matsui O. Imaging of multistep human hepatocarcinogenesis by CT during intraarterial contrast injection. Intervirology,2004,47(3-5):271-276.
3.Hayashi M, Matsui O, Ueda K, et al. Correlation between the blood supply and grade of malignancy of hepatocellular nodules associated with liver cirrhosis:evaluation by CT during intraarterial injection of contrast medium. AJR, 1999,172(4):969-976.
4.Nakajima Y, Shimamura T, Kamiyama T, et al.Evaluation of surgical resection for small hepatocellular carcinomas. Am J Surg,1996,171(3):360-363.
5.Karahan OI, Yikilmaz A, Isin S, et al. Characterization of hepatocellular carcinomas with triphasic CT and correlation with histopathologic findings. Acta Radiol,2003, 44(6):566-71.
6.Rummeny E, Weissleder R, Stark DD, et al. Primary liver tumors diagnosis by MR imaging. Am J Roentgenol,1989,152(1):63-72.
7.Stevens WR, Johnson CD, Stephens DH, et al. CT findings in hepatocellular carcinoma:correlation of tumor characteristics with causative factors, tumor size, and histologic tumor grade. Radiology,1994,191(2):531-7.
8.Semelka RC, Helmberger TKG. Contrast agents for MR imaging of the liver. Radiology,2001,218(1):27-38.
9.Bellin M-F. MR contrast agents, the old and the new. Eur J Radiol,2006, 60(3):314-23.
1O.Gandhi SN, Brown MA, Wong JG, et al. MR contrast agents for liver imaging: what, when, how. Radiographics,2006,26(6):1621-36.
11. Hulka CA, Edmister WB, Smit h BL, et al. Dynamic echo-planar imaging of t he breast:experience in diagnosing breast carcinoma and correlation with tumor angiogenesis. Radiology,1997,205(3):837-842.
12. Hawighorst H,Knapstein PG,Knopp MV,et al.Cervical carcinoma:standard and pharmacokinetic analysis of time-intensity curves for assessment of tumor angiogenesis and patient survival. MAGMA,1999,8(1):55-62.
13. Semelka RC, Martin DR, Balci C, et al. Focal liver lesions:Comparison of dual-phase CT and multisequence multiplanar MR imaging including dynamic gadolinium enhancement. J Magn Reson,2001,13(3):397-401.
14.Wang J, Chen LT, Tsang YM, et al. Dynamic contrast-enhanced MRI analysis of perfusion changes in advanced hepatocellular carcinoma treated with an antiangiogenic agent:a preliminary study. Am J Roentgenol, 2004,183(3):713-9.
15.Tajima T, Honda H, Taguchi K, et al. Sequential hemodynamic change in hepatocellular carcinoma and dysplastic nodules:CT angiography and pathologic correlation. AJR,2002,178(4):885-897.
16.Asayama Y, Yoshimitsu K, Irie H, et al. Poorly versus moderately differentiated hepatocellular carcinoma:vascularity assessment by computed tomographic hepatic angiography in correlation with histologically counted number of unpaired arteries. J Comput Assist Tomogr,2007,31(2):188-192.
17.Asayama Y, Yoshimitsu K, Nishihara Y, et al.Arterial Blood Supply of Hepatocellular Carcinoma and Histologic Grading:Radiologic-Pathologic Correlation. AJR,2008,190(1):W28-W34.
18.Mineura K, Yasuda T, Kowada M,et al. PET evaluation of histologic malignancy in gliomas using 150 and 18F-FDG. Neurol Res,1986,8(3):164-168.
1. Ince N, Wands JR. The increasing incidence of hepatocellular carcinoma. N Engl J Med 1999; 340(10):798-799.
2. Tung-Ping Poon R, Fan ST, Wong J. Risk factors, prevention, and management of postoperative recurrence after resection of hepatocellular carcinoma. Ann Surg 2000; 232(1):10-24.
3. Haratake J, Takeda S, Kasai T, et al. Predictable factors for estimating prognosis of patients after resection of hepatocellular carcinoma. Cancer 1993; 72(4):1178-1183.
4. Kim SH, Lee WJ, Lim HK, et al. SPIO-enhanced MRI findings of well-differentiated hepatocellular carcinomas:correlation with MDCT findings. Korean J Radiol 2009; 10(2):112-120.
5. Nasu K, Kuroki Y, Tsukamoto T, et al. Diffusion-weighted imaging of surgically resected hepatocellular carcinoma:imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol 2009; 193(2):438-444.
6. Maeda M, Kato H, Sakuma H, et al. Usefulness of the apparent diffusion coefficient in line scan diffusionweighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck.AJNR Am J Neuroradiol 2005; 26(5):1186-1192.
7. Wang J, Takashima S, Takayama F, et al. Head and neck lesions:characterization with diffusion-weighted echo-planar MR imaging. Radiology 2001; 220(3):621-630.
8. Sumi M, Sakihama N, Sumi T, et al. Discrimination of metastatic cervical lymph nodes with diffusion-weighted MR imaging in patients with head and neck cancer. AJNR Am J Neuroradiol 2003; 24(8):1627-1634.
9. Xu H, Li X, Xie JX, et al. Diffusion-weighted magnetic resonance imaging of focal hepatic nodules in an experimental hepatocellular carcinoma rat model. Acad Radiol 2007;14(3):279-286.
10. Guo AC, Cummings TJ, Dash RC, et al. Lymphomas and high-grade astrocytomas:comparison of water diffusibility and histologic characteristics. Radiology 2002;224(1):177-183.
11. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. AJR Am J Roentgenol 2007;188(6):1622-1635.
12. Edmondson HA, Steiner PE. Primary carcinoma of the liver:a study of 100 cases among 48,900 necropsies. Cancer 1954;7(3):462-503.
1. Low RN. Abdominal MRI advances in the detection of liver tumours and characterisation. Lancet Oncol 2007; 8(6):525-35.
2. Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology 2010; 254(1):47-66.
3. Koh DM, Collins DJ. Diffusion-weighted MRI in the body:applications and challenges in oncology. Am J Roentgenol 2007; 188(6):1622-35.
4. Yamada K, Kubota H, Kizu O, et al. Effect of intravenous gadolinium-DTPA on diffusion-weighted images:evaluation of normal brain and infarcts. Stroke 2002, 33(7):1799-1802.
5. Zhong J, Kennan RP, Fulbright RK, et al. Quantification of intra- and extravascular contributions to BOLD effects induced by alteration of oxygenation or intravascular contrast agent. Magn Reson Med 1998; 40(4):526-536.
6:Fitzek C, Mentzel HJ, Fitzek S, et al. Echoplanar diffusion-weighted MRI with intravenous gadolinium-DTPA.Neuroradiology 2003,45(9):592-597
7. Le Bihan D, Breton E, Lallemand D, et al.Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988; 168(2):497-505.
8. Kidwell CS, Alger JR, Di Salle F, et al. Diffusion MRI in patients with transient ischemic attacks. Stroke 1999; 30(12):2762-2763.
9. Xing D, Papadakis NG, Huang CL, et al.Optimized diffusion-weighting for measurement of apparent diffusion coefficient (ADC) in human brain. Magn Reson Imaging 1997;15(7):771-784.
10. Wolf RL, Zimmerman RA, Clancy R, et al. Quantitative apparent diffusion coefficient measurements in term neonates for early detection of hypoxic-ischemic brain injury: initial experience. Radiology 2001; 218(3):825-833.
11.Firat AK, nli B, Karakas. HM, et al. The effect of intravenous gadolinium-DTPA on diffusion-weighted imaging. Neuroradiology 2006,48(7):465-470.
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