前列腺癌扩散峰度成像的初步研究
The preliminary study of diffusion kurtosis imaging in prostate cancer
姚秋英 1所世腾 1庄治国 1范瑜 1陈小溪 1吴连明 1许建荣1
作者信息
- 1. 上海交通大学医学院附属仁济医院放射科，上海 200127
- 折叠
摘要
目的：初步探讨扩散峰度成像(DKI)在前列腺癌诊断中的作用以及在评估前列腺癌侵袭性中的价值。方法回顾性分析30例前列腺癌患者术前 MR 及多 b 值扩散加权成像(DWI)资料。分别采用 DKI 模型和单指数衰减模型计算 D 图、K 图以及表观扩散系数(ADC)图。分析比较各参数在前列腺癌与良性组织间以及不同 Gleason 评分前列腺癌间的差异。P <0.05认为差异有统计学意义。结果前列腺癌的 ADC 和 D 值均显著低于良性组织(P<0.001),而前列腺癌的 K 值显著高于良性组织(P <0.001)。ADC值、D 值和 K 值在不同 Gleason 评分(6,7及≥8)的病灶中均存在显著性差异(P <0.001、P =0.015和 P <0.001)。结论 DKI 模型可以较好地模拟前列腺癌扩散信号随 b 值的变化,有助于前列腺癌的诊断与分级。K 值有望从微观角度对前列腺癌组织微结构复杂性进行定量评估。
Abstract
Objective To evaluate the feasibility of diffusion kurtosis imaging (DKI)in the diagnosis and aggressiveness assessment of prostate cancer.Methods The MR data with multi-b-value diffusion-weighted imaging (DWI)of 30 male patients with prostate cancer were retrospectively analyzed.D maps and K maps were generated from the DKI model and ADC maps were obtained using the conventional monoexponential model.Differences in the D,K and ADC values between prostate cancer and benign peripheral zone (PZ)tissues,among tumors with different Gleason scores were analyzed.P<0.05 was considered statistical significance.Results ADC and D values were significantly lower in the prostate cancers than in the benign PZs (P <0.001).K values were significantly greater in the prostate cancers than in the benign PZs (P <0.001).ADC,D and K values were different significantly among the tumors with Glea-son scores of 6,7 and ≥8 (P <0.001,P =0.01 5 and P <0.001,respectively).Conclusion The DKI model can better describe the signal intensity attenuation with b values increasing,which is helpful for diagnosing and grading prostate cancer.K value may be used to quantitatively evaluate the complicated microstructure of prostate cancer.
关键词
扩散加权成像/扩散峰度成像/前列腺癌Key words
diffusion-weighted imaging/diffusion kurtosis imaging/prostate cancer引用本文复制引用
基金项目
上海市重点学科建设项目(S30203)
上海交通大学医学院重点学科项目[重点学科(第三期)]()
国家重大科学研究计划(973项目(2012CB932600)
上海交通大学医工交叉基金(YG2013MS37)
出版年
2015
NETL
NSTL
维普
万方数据