体素内不相干运动和扩散成像对移行区前列腺癌鉴别诊断价值的对照研究

The comparative study of in transition zone prostate cancer: diagnostic performance of new intravoxel incoherent motion and diffusion kurtosis imaging models

目的探讨MR体素内不相干运动(IVIM)和扩散峰度成像(DKI)定量参数鉴别移行区前列腺癌和鉴别中高危前列腺癌的价值,并分析其与前列腺癌Gleason评分的相关性。方法回顾性分析2015年1月至2017年6月苏州大学附属第一医院诊断经MR-经直肠超声融合靶向穿刺活检证实,且进行了多参数MRI(T2WI、DWI、IVIM和DKI)检查的55例患者。三维勾画每个病灶的ROI,测量IVIM和DKI模型的定量参数,包括扩散系数(D)、伪扩散系数(D*)、灌注分数(f)、平均扩散系数(MD)、平均扩散峰度(MK)及各项异性(FA)值。采用独立样本t检验比较前列腺癌和BPH、低危(BPH+Gleason评分6分)和中高危病变(Gleason评分≥7分)病变参数的差异,ADC值、IVIM和DKI参数与Gleason评分间的相关性采用Spearman分析,采用ROC分析评价各参数鉴别诊断前列腺癌和BPH、低危和中高危病变诊断的效能。结果移行带癌27例(36个病灶),BPH患者28例(40个病灶);Gleason评分≥7分组(中高危)病灶33个,BPH+Gleason评分6分(低危)组病灶43个。移行带前列腺癌和BPH组间、中高危和低危组患者间的ADC、D、MD、MK、FA值差异均有统计学意义(P均<0.05),D*和f值的差异均无统计学意义(P均>0.05)。ADC、MD值与Gleason评分呈负相关,r值分别为-0.585、-0.489,P均<0.05。鉴别移行带前列腺癌和BPH,ADC值鉴别诊断的ROC下面积最大(0.864),与D值(0.853)的ROC下面积差异无统计学意义(P>0.05);DKI模型参数中,MD值的ROC下面积最大(0.796);多个模型参数联合诊断,ADC+D值、ADC+MD值、ADC+MD+D值的ROC下面积分别为0.892、0.884、0.897,与单一ADC值的差异无统计学意义(P>0.05)。鉴别前列腺低危和中高危病变,ADC值鉴别诊断的ROC下面积最大(0.826),DKI模型参数中MD值的ROC下面积最大(0.743);多个模型参数联合诊断,ADC+D值、ADC+MD值、ADC+MD+D值的ROC下面积分别为0.851、0.846、0.856,与单一ADC值的差异无统计学意义(P>0.05)。结论IVIM和DKI模型可用于鉴别诊断移行带前列腺癌并可预测中高级别前列腺癌,但与ADC值相比无明显优势,ADC值和MD值与移行区前列腺癌Gleason评分具有相关性。

Objective To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods A total of 55 patients who were undergoing preoperative muti-parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI-transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo-diffusivity (D^*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t-test was used to compare the differences in lesion parameters between prostate cancer and BPH, low-risk (BPH+Gleason score 6 points) and medium-high-risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis. Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low-risk or high-risk. Results 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia (BPH) in PZ were included, meanwhile, the cases of GS ≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa-BHP group and high-low risk group in TZ (P<0.05), D^* and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were -0.585 and -0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS ≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid-higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.