磁共振扩散加权成像单指数模型与扩散峰度成像模型在61例肾透明细胞癌分级中的对比

doi:10.6040/j.issn.1671-7554.0.2020.0089

摘要/Abstract

摘要： 目的探讨磁共振扩散加权成像(DWI)单指数模型和扩散峰度成像(DKI)模型在预测肾透明细胞癌(ccRCC)病理分级中的价值差异。方法前瞻性纳入经病理结果证实的ccRCC患者61例,根据Fuhrman分级将其分为低级别组27例(Ⅰ级10例、Ⅱ级17例)和高级别组34例(Ⅲ级19例、Ⅳ级15例)。患者均行肾脏常规单指数DWI序列和DKI序列扫描,测量参数包括ADC值、各向异性分数(FA)、平均扩散系数(MD)、平均扩散峰度(MK)、轴向扩散峰度(Ka)以及径向扩散峰度(Kr)值。多组间比较采用单因素方差分析(ANOVA),利用独立样本t检验进行两组间均数的比较,使用受试者工作特征(ROC)曲线得出DWI、DKI各参数的曲线下面积(AUC)、敏感度、特异度,并用Delong检验比较各参数的AUC值,以评价各参数对分级的诊断效能。结果 (1)ADC、MD、MK、Ka、Kr值在正常肾实质、低级别及高级别ccRCC间的差异有统计学意义(P<0.05),FA值在3组间的差异无统计学意义(P>0.05)。正常肾实质、低级别及高级别ccRCC的ADC值分别为(2.10±0.16)×10^-3mm²/s、(1.70±0.34)×10^-3mm²/s、(1.20±0.32)×10^-3mm²/s,FA值分别为0.26±0.06、0.26±0.11、0.28±0.14,MD值分别为(6.02±0.43)×10^-3mm²/s、(5.10±0.96)×10^-3mm²/s、(3.70±0.76)×10^-3mm²/s,MK值分别为0.49±0.04、0.57±0.07、0.84±0.20,Ka值分别为0.39±0.04、0.48±0.14、0.65±0.19,Kr值分别为0.53±0.05、0.66±0.18、0.98±0.29。与正常肾实质比较,低级别与高级别ccRCC患者的ADC、MD值均逐渐减低,MK、Ka及Kr值均逐渐升高,差异有统计学意义(P<0.05),FA值差异无统计学意义(P>0.05)。(2)绘制ROC曲线,得出ADC、MD、MK、Ka及Kr值鉴别低级别、高级别ccRCC的截断值分别为1.50×10^-3mm²/s、4.49×10^-3mm²/s、0.71、0.51、0.68,敏感度分别为85.3%、87.5%、79.2%、83.3%、95.8%,特异度分别为75.2%、90.6%、100.0%、85.3%、75.4%;各参数鉴别低级别、高级别ccRCC的AUC分别为ADC值0.831,MD值0.884,MK值0.950,Ka值0.832,Kr值0.874,其中以MK值的AUC最高。结论与单指数扩散模型相比,DKI模型,特别是其衍生出的参数MK,更适合作为ccRCC病理分级预测的成像技术。

关键词: 肾透明细胞癌, 扩散加权成像, 单指数模型, 扩散峰度成像模型

Abstract: Objective To compare the value of diffusion kurtosis imaging(DKI)and diffusion weighted imaging(DWI)mono-exponential mode in grading clear cell renal cell carcinoma(ccRCC). Methods Sixty-one patients with pathologically proven ccRCC were enrolled in this prospective clinical study. The patients were divided into low grade group(10 with grade Ⅰ and 17 with grade Ⅱ)and high-grade group(19 with grade Ⅲ and 15 with grade Ⅳ)according to the Fuhrman classification system. All patients underwent DWI examination by using both mono-exponential mode and DKI mode. The ADC, fractional anisotropy(FA), mean diffusivity(MD), mean kurtosis(MK), axial kurtosis(Ka)and radial kurtosis(Kr)values were measured, respectively. One-way analysis of variance(ANOVA)was used to compare the difference among three groups, and the independent sample t test was used to compare the difference between two groups. The AUC, sensitivity, and specificity of DWI and DKI parameters were calculated by using receiver-operating characteristic(ROC)analysis, then these AUC values were compared by Delong test to evaluate the diagnostic efficacy of the parameters. Results (1)There were statistical differences in ADC, MD, MK, Ka, and Kr values among the normal renal parenchyma, low-grade and high-grade ccRCC groups(P<0.05). The difference in FA value among the three groups was not statistically significant(P>0.05). The ADC values of the normal renal parenchyma, low-grade and high-grade ccRCC were(2.10±0.16)×10^-3mm²/s,(1.70±0.34)×10^-3mm²/s, and(1.20±0.32)×10^-3mm²/s, FA values were 0.26±0.06, 0.26±0.11, and 0.28±0.14, MD values were(6.02±0.43)×10^-3mm²/s,(5.10±0.96)×10^-3mm²/s, and(3.70±0.76)×10^-3mm²/s, MK values were 0.49±0.04, 0.57±0.07, and 0.84±0.20, Ka values were 0.39±0.04, 0.48±0.14, and 0.65±0.19, Kr values were 0.53±0.05, 0.66±0.18, and 0.98±0.29, respectively. Compared with the normal renal parenchyma, the ADC and MD values of patients with low-grade and high-grade ccRCC were gradually decreased, and the MK, Ka, and Kr values were all gradually increased(P<0.05). The difference in FA value was not statistically significant(P>0.05). (2)ROC analysis showed that the cut-off values of ADC, MD, MK, Ka and Kr in predicting pathological grade of ccRCC were 1.50×10^-3mm²/s, 4.49×10^-3mm²/s, 0.71, 0.51, 0.68, the sensitivity were 85.3%, 87.5%, 79.2%, 83.3%, 95.8%, and the specificity were 75.2%, 90.6%, 100.0%, 85.3%, 75.4%, respectively. The area under the curve(AUC)of ADC, MD, MK, Ka and Kr was 0.831, 0.884, 0.950, 0.832 and 0.874, among which the AUC of the MK value was the highest. Conclusion Compared with DWI mono-exponential mode, DKI mode, especially its derived parameter MK, is more suitable to serve as an imaging technique for predicting the pathological grade of ccRCC.

Key words: Clear cell renal cell carcinoma, Diffusion-weighted imaging, Mono-exponential mode, Diffusion kurtosis imaging mode

中图分类号:

R445.2

罗昕,何兵,聂清生,侯震波,董军,李玉花,曾祥芹,刘伟,孔德民,曹金凤. 磁共振扩散加权成像单指数模型与扩散峰度成像模型在61例肾透明细胞癌分级中的对比[J]. 山东大学学报 (医学版), 2020, 58(7): 89-95.

LUO Xin, HE Bing, NIE Qingsheng, HOU Zhenbo, DONG Jun, LI Yuhua, ZENG Xiangqin, LIU Wei, KONG Demin, CAO Jinfeng. Comparison of the value of mono-exponential mode and diffusion kurtosis imaging mode in grading clear cell renal cell carcinoma using magnetic resonance diffusion-weighted imaging[J]. Journal of Shandong University (Health Sciences), 2020, 58(7): 89-95.

参考文献

[1] Tilki D, Nguyen HG, DallEra MA, et al. Impact of histologic subtype on cancer-specific survival in patients with renal cell carcinoma and tumor thrombus[J]. Eur Urol, 2014, 66(3): 577-583.
[2] Ghavamian R, Cheville JC, Lohse CM, et al. Renal cell carcinoma in the solitary kidney: an analysis of complications and outcome after nephron sparing surgery[J]. J Urol, 2002, 168(2): 454-459.
[3] Doehn C, Siebels M, Steiner T. Follow-up of renal cell carcinoma based on stage and initial treatment[J]. Urologe A, 2020, 59(2): 162-168.
[4] Seveenco S, Heinz-Peer G, Ponhold L, et al. Utility and limitations of 3-Tesla diffusion-weighted magnetic resonance imaging for differentiation of renal tumors[J]. Eur J Radiol, 2014, 83(6): 909-913.
[5] Yu X, Lin M, Ouyang H, et al. Application of ADC measurement in characterization of renal cell carcinomas with different pathological types and grades by 3.0 T diffusion-weighted MRI[J]. Eur J Radiol, 2012, 81(11): 3061-3066.
[6] Minarikova L, Bogner W, Pinker K, et al. Investigating the prediction value of multiparametric magnetic resonace imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer[J]. Eur Radiol, 2017, 27(5): 190l-1911.
[7] Kamagata K, Andica C, Hatano T, et al. Advanced diffusion magnetic resonance imaging in patients with Alzheimers and Parkinsons diseases[J]. Neural Regen Res, 2020, 15(9): 1590-1600.
[8] Abdalla G, Sanverdi E, Machado PM, et al. Role of diffusional kurtosis imaging in grading of brain gliomas: a protocol for systematic review and meta-analysis[J]. BMJ Open, 2018, 8(12): e025123. doi: 10.1136/bmjopen-2018-025123.
[9] Pentang G, Lanzman RS, Heusch P, et al. Diffusion kurtosis imaging of the human kidney: a feasibility study[J]. Magn Reson Imaging, 2014, 32(5): 413-420.
[10] Huang Y, Chen X, Zhang Z, et al. MRI quantification of non-Gaussian water diffusion in normal human kidney: a diffusional kurtosis imaging study[J]. NMR Biomed, 2015, 28(2): 154-161.
[11] Srigley JR, Delahunt B, Eble JN, et al. The international society of urological pathology(ISUP)vancouver classification of renal neoplasia[J]. Am J Surg Pathol, 2013, 37(10): 1469-1489.
[12] Goyal A, Sharma R, Bhalla AS, et al. Diffusion-weighted MRI in renal cell carcinoma: a surrogate marker for predicting nuclear grade and histological subtype[J]. Acta Radiol, 2012, 53(3): 349-358.
[13] 孙军, 邢伟, 陈杰, 等. Tlb期肾透明细胞癌 ADC 值与病理分级相关性的研究[J]. 中华放射学杂志, 2012, 46(8): 682-686. SUN Jun, XING Wei, CHEN Jie, et al. Correlation between apparent diffusion coefficient value and pathological grading in pT1b clear cell renal carcinoma[J]. Chinese Journal of Radiology, 2012, 46(8): 682-686.
[14] Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-gaussian diffusion by means of magnetic resonance imaging[J]. Magn Reson Med, 2005, 53(6): 1432-1440.
[15] Yang AW, Jensen JH, Hu CC, et al. Effect of cerebral spinal fluid suppression for diffusional kurtosis imaging[J]. J Magn Imaging, 2013, 37(2): 365-371.
[16] Hempel JM, Brendle C, Bender B, et al. Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study[J]. Eur J Radiol, 2019, 112: 144-152. doi: 10.1016/j.ejrad.2019.01.014.
[17] Quentin M, Pentang G, Schimmoller, et al. Feasibility of diffusion kurtosis tensor imaging in prostate MRI for the assessment of prostate cancer: preliminary results[J]. Magn Reson Imaging, 2014, 32(7): 880-885.
[18] 钟燕, 叶慧义. 磁共振扩散峰度成像在前列腺癌中的研究现状[J]. 中国医学影像学杂志, 2017, 25(3): 235-237.
[19] Shan Y, Chen X, Liu K, et al. Prostate cancer aggressive prediction: preponderant diagnostic performances of intravoxel incoherent motion(IVIM)imaging and diffusion kurtosis imaging(DKI)beyond ADC at 3.0 T scanner with gleason score at final pathology[J]. Abdom Radiol(NY), 2019, 44(10): 3441-3452.
[20] 刘伟锋, 陈亮, 江新青. 磁共振扩散峰度成像鉴别诊断肾细胞癌亚型的可行性分析[J]. 哈尔滨医科大学学报, 2016, 50(4): 333-336. LIU Weifeng, CHEN Liang, JIANG Xinqing. Feasibility of diffusion kurtosis iamging in differential diagnosis of subtypes of renal cell carcinoma[J]. Journal of Harbin Medical University, 2016, 50(4): 333-336.
[21] Wu G, Zhao Z, Yao Q, et al. The study of clear cell renal carcinoma with MR diffusion kurtosis tensor imaging and its histopathologic correlation[J]. Acad Radiol, 2018, 25(4): 430-438.
[22] 朱庆强, 朱文荣, 叶靖, 等. 扩散峰度成像评价肾透明细胞癌恶性程度的价值[J]. 中华放射学杂志, 2017, 51(3): 188-191. ZHU Qingqiang, ZHU Wenrong, YE Jing, et al. Value of diffusion kurtosis in assessment of pathological grade of clear cell renal cell carcinoma[J]. Chinese Journal of Radiology, 2017, 51(3): 188-191.
[23] Dai Y, Yao Q, Wu G, et al. Characterization of clear cell renal carcinoma with diffusion kurtosis imaging: correlation between diffusion kurtosis parameters and tumor cellularity[J]. NMR Biomed, 2016, 29(7): 873-881.
[24] Raab P, Hattingen E, Franz K, et al. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences[J]. Radiology, 2010, 254(3): 876-881.
[25] Van Cauter S, Veraart J, Sijbers J, et al. Gliomas: diffusion kurtosis MR imaging in grading[J]. Radiology, 2012, 263(2): 492-501.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed