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J Zhejiang Univ (Med Sci)  2021, Vol. 50 Issue (1): 97-105    DOI: 10.3724/zdxbyxb-2021-0036
    
Quantitative perfusion histogram parameters of dynamic contrast-enhanced MRI to identify different pathological types of uterine leiomyoma
WANG Subo1(),ZHAO Zhenhua2,*(),ZHANG Yu2,YANG Liming2,HUANG Yanan2,RUAN Yawen2,WANG Cheng2
1. Department of Radiology,Shaoxing Hospital of Traditional Medicine,Shaoxing 312000,Zhejiang Province,China;
2. Department of Radiology,Shaoxing People’s Hospital,Shaoxing 312000,Zhejiang Province,China
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Abstract  

Objective:To explore the value of quantitative perfusion histogram parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in pathological classification of uterine leiomyoma and its correlation with Ki-67 protein expression. Methods: Thirty five patients with uterine leiomyoma confirmed by operation and pathology at Shaoxing People’s Hospital from October 2015 to September 2017 were analyzed retrospectively,including 15 cases of ordinary type,8 cases of cellular type and 12 cases of degenerative type. All patients were examined by pelvic DCE-MRI before operation,and the histogram parameters (median,mean,skewness,kurtosis,energy,entropy) of various quantitative perfusion parameters,including volume transport constant (K trans),rate constant (K ep),extravascular extracellular space distribute volume per unit tissue volume (V e),blood plasma volume per unit volume of tissue (V p) were calculated,and the efficacy of different parameters in pathological classification of uterine leiomyoma was evaluated by ROC curve. The expression of Ki-67 protein in uterine leiomyoma was detected by immunohistochemical method,and the correlation between histogram parameters and Ki-67 protein expression was analyzed by Pearson and Spearman correlation analysis. Results: The median and mean values of K trans,K ep,V e and V p in the cellular group were higher than those in the degenerative group and the ordinary group( P<0.05 or P<0.01),while the skewness of V e,the skewness and kurtosis of K ep in the cellular group were lower than those in the ordinary group (all P<0.05). The entropy of K trans in the cellular group was higher than that in the degenerative group and the ordinary group (all P < 0.05). The entropy of V p in the cellular group was higher than that in the ordinary group ( P<0.01). The median,mean,skewness of K trans,median and mean of K ep,median and mean of V e,median,mean,energy and entropy of V p were correlated with Ki-67 expression(all P<0.05). The results of ROC curve analysis showed that the median threshold of K trans was 0.994/min,the sensitivity and specificity for the diagnosis of cellular uterine leiomyoma were 100.0% and 77.8% respectively,and the area under the ROC curve was 0.949. When the mean threshold of K trans was 1.170/min,the sensitivity and specificity for diagnosing cellular uterine leiomyoma were 100.0% and 77.8% respectively,and the area under the ROC curve was 0.958. The area under the ROC curve of K trans (entropy),K ep (median,mean),V p (median,mean,entropy) in the diagnosis of cellular uterine leiomyoma were 0.755–0.907. Conclusion:DCE-MRI quantitative perfusion histogram parameters have high diagnostic value in differentiating pathological types of uterine leiomyoma,especially for cellular uterine leiomyoma.



Key wordsUterus myoma      Pathological types      Dynamic contrast-enhanced of magnetic resonance imaging      Ki-67      Diagnosis     
Received: 13 May 2020      Published: 16 May 2021
CLC:  R73  
  R73  
  A  
Corresponding Authors: ZHAO Zhenhua     E-mail: 19019095@qq.com;zhao2075@163.com
Cite this article:

WANG Subo,ZHAO Zhenhua,ZHANG Yu,YANG Liming,HUANG Yanan,RUAN Yawen,WANG Cheng. Quantitative perfusion histogram parameters of dynamic contrast-enhanced MRI to identify different pathological types of uterine leiomyoma. J Zhejiang Univ (Med Sci), 2021, 50(1): 97-105.

URL:

http://www.zjujournals.com/med/10.3724/zdxbyxb-2021-0036     OR     http://www.zjujournals.com/med/Y2021/V50/I1/97


动态对比增强磁共振成像定量灌注直方图参数对子宫肌瘤病理分型的诊断价值

目的:探讨动态对比增强磁共振成像(DCE-MRI)定量灌注直方图参数在子宫肌瘤病理分型诊断中的价值及其与Ki-67蛋白表达的相关性。 方法:回顾性分析2015年10月至2017年9月在绍兴市人民医院经手术后病理检查证实为子宫肌瘤35例患者(普通型15例,富细胞型8例,退变型12例)的资料。所有患者术前行盆腔DCE-MRI检查,计算各定量灌注参数包括容量转运常数(K trans)、速率常数 (K ep)、 血管外细胞外间隙容积分数(V e)、血管间隙容积分数(V p)的直方图参数(中位数、平均值、偏度、峰度、能量、熵),并采用受试者操作特征(ROC)曲线评估不同参数在鉴别子宫肌瘤病理分型中的效能。免疫组织化学方法测定子宫肌瘤Ki-67蛋白表达,比较不同病理类型Ki-67蛋白表达的差异,并采用Pearson和Spearman相关性分析法分析直方图参数与Ki-67蛋白表达的相关性。 结果:富细胞型组K trans、K ep、V e、V p的中位数和平均值均大于退变型组和普通型组( P<0.05或 P<0.01);V e的偏度、K ep的偏度和峰度均小于普通型组(均 P<0.05);K trans的熵高于退变型组和普通型组(均 P<0.05);V p的熵高于普通型组( P<0.01)。K trans的中位数、平均值和偏度,K ep的中位数和平均值,V e的中位数和平均值,V p的中位数、平均值、能量和熵均与Ki-67表达水平相关(均 P<0.05)。ROC 曲线分析结果显示,当K trans中位数阈值为0.994/min时,其诊断富细胞型子宫肌瘤的敏感度为100.0%,特异度为77.8%,ROC曲线下面积为0.949;当K trans平均值阈值为1.170/min时,其诊断富细胞型子宫肌瘤的敏感度为100.0%,特异度为77.8%,ROC曲线下面积为0.958;K trans的熵,K ep的中位数和平均值,V p的中位数、平均值和熵诊断富细胞型子宫肌瘤的ROC曲线下面积也较高(0.755~0.907)。 结论:DCE-MRI定量灌注直方图参数对不同病理类型子宫肌瘤尤其是富细胞型子宫肌瘤具有较高的诊断价值。


关键词: 子宫肌瘤,  病理分型,  动态增强磁共振成像,  Ki-67,  诊断 

病理类型

例数

年龄(岁)

肿瘤体积(cm 3

体质量(kg)

体重指数(kg/m 2

普通型

15

43.5±5.0

124±55

62±9

21±9

富细胞型

8

40.8±4.7

100±95

57±9

14±12

退变型

12

47.7±3.3

153±139

61±8

18±11

F

6.300

0.700

0.833

1.081

P

<0.01

>0.05

>0.05

>0.05

Table 1 Characteristics of patients with different pathological types of uterine leiomyoma

病理类型

n

K trans

中位数(min)

平均值(min)

偏度

峰度

能量

普通型

15

0.779(0.380,0.979)

0.718±0.350

1.150±1.004

1.694(1.092,6.753)

0.012(0.010,0.016)

6.617 ±0.476

富细胞型

8

1.469(1.128,2.244) **##

1.710 ±0.532 **##

0.289 ± 0.950

–0.207(–0.694,1.790)

0.117(0.101,0.164)

7.420±0.433 *#

退变型

12

0.676(0.159,1.092)

0.720±0.451

1.265±0.894

1.363(0.318,3.364)

0.010(0.007,0.021)

6.668±0.939

F/ H

13.960

16.333.

2.856.

7.500

8.641

4.258

P

<0.01

<0.01

>0.05

>0.05

>0.05

<0.05

病理类型

n

K ep

中位数(/min)

平均值(/min)

偏度

峰度

能量

普通型

15

0.984(0.619,1.074)

1.040(0.623,1.127)

2.400±2.195

7.148(1.371,22.285)

0.019(0.012,0.039)

5.850±0.863

富细胞型

8

1.727(1.128,2.922) **##

1.819(1.245,2.556) **##

0.415±0.777 *

0.715(–0.048,2.052) *

0.010(0.008,0.032)

6.706±0.980

退变型

12

0.913(0.633,1.332)

1.065(0.765,1.446)

1.415±1.267

1.362(0.185,12.050)

0.013(0.009,0.025)

6.445±0.777

F/ H

13.668

13.102

3.804

8.988

3.751

3.040

P

<0.01

<0.01

<0.05

>0.05

>0.05

>0.05

病理类型

n

V e

中位数

平均值

偏度

峰度

能量

普通型

15

0.836±0.233

0.811±0.210

–1.392(–5.167,–0.113)

3.242(-0.360,31.560)

0.007(0.006,0.012)

6.924±1.081

富细胞型

8

0.925±0.209 *#

0.908±0.200 *#

–3.532(–14.732,–2.437) *

14.787(6.370,249.257)

0.011(0.008,0.019)

6.166±1.776

退变型

12

0.651±0.337

0.666±0.254

0.010(–1.028,0.681)

–0.442(–1.168,3.576)

0.008(0.006,0.011)

7.085±0.605

F/ H

2.820

3.008

3.338

2.015

1.126

1.667

P

>0.05

>0.05

<0.05

>0.05

>0.05

>0.05

病理类型

n

V p

中位数

平均值

偏度

峰度

能量

普通型

15

0.002(0.001,0.221 )

0.053(0.005,0.290)

3.459±3.594

2.798(0.217,63.201)

0.364±0.355

4.182±2.656

富细胞型

8

0.326(0.077,0.840) **##

0.354(0.162,0.725) **##

1.149 ± 5.880

0.193(–0.765,44.816)

0.173±0.318

5.825±2.397 **

退变型

12

0.001(0.000,0.002)

0.010(0.003,0.117)

5.560±4.477

19.012(0.894,91.687)

0.544±0.287

2.739±1.994

F/ H

7.383

7.449

2.346

3.120

3.170

0.055

P

<0.01

<0.01

>0.05

>0.05

>0.05

<0.05

Table 2 DCE-MRI quantitative perfusion histogram parameters of uterine fibroids with three different pathological types

直方图参数

r

直方图参数

r

K trans

V e

?

中位数

0.562 **

中位数

0.338 *

平均值

0.566 **

平均值

0.356 *

偏度

–0.387 *

偏度

–0.272

峰度

–0.153

峰度

0.214

能量

–0.121

能量

0.175

0.268

–0.176

K ep

V p

?

中位数

0.502 **

中位数

0.377 *

平均值

0.471 **

平均值

0.397 *

偏度

–0.161

偏度

–0.285

峰度

–0.101

峰度

–0.103

能量

0.054

能量

–0.336 *

0.116

0.362 *

Table 3 Correlation between parameters of DCE-MRI quantitative perfusion histogram parameters and Ki-67 in 35 uterine leiomyoma patients
Figure 1 Quantitative perfusion of K color map and the histogram of K in three different types of uterine leiomyoma in Extended Tofts model
Figure 2 ROC curves of DCE-MRI quantitative perfusion histogram parameters in diagnosis of cellular leiomyoma

直方图参数

曲线下面积

阈值

约登指数

敏感度(%)

特异度(%)

K trans中位数

0.949

0.994/min

0.778

100.0

77.8

K trans平均值

0.958

1.170/min

0.778

100.0

77.8

K trans

0.843

7.189

0.417

75.0

81.5

K ep中位数

0.907

1.233/min

0.639

75.0

88.9

K ep平均值

0.898

1.212/min

0.690

87.5

81.5

K ep偏度

0.236

0.707

–0.163

50.0

33.7

V e偏度

0.245

–3.292

–0.204

50.0

29.6

V p中位数

0.819

0.058

0.616

87.5

74.1

V p平均值

0.796

1.443

0.616

87.5

74.1

V p

0.755

5.479

0.616

87.5

74.1

Table 4 The efficacy of DCE-MRI quantitative perfusion histogram parameters in diagnosis of cellular leiomyoma
[1]   DESHMUKH S P, GONSALVES C F, GUGLIELMO F F, et al. Role of MR imaging of uterine leiomyomas before and after embolization [J/OL]. Radiographics, 2012, 32(6): E251-E281.
doi: 10.1148/rg.326125517
[2]   张 嵘,梁碧玲,付加平,等. 子宫肌瘤的MRI表现与临床病理相关性研究[J]. 中华放射学杂志,2003,37(10):954–959. DOI:10.3760/j.issn:1005-1201. 2003.10.021.ZHANG Rong,LIANG Biling,FU Jiaping,et al. Uterine leiomyoma:comparative study with MRI and histopathology[J]. Chinese Journal of Radiology,2003,37(10):954–959. DOI:10.3760/j.issn:1005-1201.2003.10.021. (in Chinese) .
[3]   JUST N . Improving tumour heterogeneity MRI assess- ment with histograms[J]. Br J Cancer, 2014, 111(12): 2205-2213.
doi: 10.1038/bjc.2014.512
[4]   LIU M, LAWSON G, DELOS M, et al. Predictive value of the fraction of cancer cells immunolabeled for proliferating cell nuclear antigen or Ki67 in biopsies of head and neck carcinomas to identify lymph node metastasis:comparison with clinical and radiologic examinations[J]. Head Neck, 2003, 25(4): 280-288.
doi: 10.1002/hed.10218
[5]   余加懿,郭大静,罗银灯,等.磁共振动态对比增强直方图分析在前列腺癌鉴别诊断中的价值[J]. 第三军医大学学报,2017,39(22):2206–2213. DOI: 10.16016/j.1000-5404.201708017.YU Jiayi,GUO Dajing,LUO Yindeng,et al. Value of histogram analysis of quantitative parameters in dynamic contrast-enhanced magnetic resonance imaging in diagnosis of prostate cancer[J]. Journal of the Third Military Medical University,2017,39(22): 2206–2213.DOI:10.16016/j.1000-5404.201708017. (in Chinese) .
[6]   GAING B, SIGMUND E E, HUANG W C, et al. Subtype differentiation of renal tumors using voxel—based histogram analysis of intravoxel incoherent motion parameters[J]. Invest Rad, 2015, 50(3): 144-152.
doi: 10.1097/RLI.0000000000000111
[7]   MA C, LIU L, LI J, et al. Apparent diffusion coefficient (ADC) measurements in pancreatic adenocarcinoma: a preliminary study of the effect of region of interest on ADC values and interobserver variability[J]. J Magn Reson Imag, 2016, 43(2): 407-413.
doi: 10.1002/jmri.25007
[8]   BRAUNNAGEL M, RADLER E, INGRISCH M, et al. Dynamic contrast-enhanced magnetic resonance imaging measurements in renal cell carcinoma[J]. Invest Rad, 2015, 50(1): 57-66.
doi: 10.1097/RLI.0000000000000096
[9]   INOUE C, FUJII S, KANEDA S, et al. Apparent diffusion coefficient (ADC) measurement in endometrial carcinoma:effect of region of interest methods on ADC values[J]. J Magn Reson Imag, 2014, 40(1): 157-161.
doi: 10.1002/jmri.24372
[10]   CHANDARANA H, ROSENKRANTZ A B, MUSSI T C, et al. Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer[J]. Radiology, 2012, 265(3): 790-798.
doi: 10.1148/radiol.12111281
[11]   陆媛媛,黄群英,孙明华,等. ADC 直方图区分宫颈癌常见病理类型的价值[J]. 中国医学计算机成像杂志,2015,21(3):255–259.DOI:10.19627/j.cnki.cn31-1700/th.2015.03.013.LU Yuanyuan,HUANG Qunying,SUN Minghua,et al. The value of histogram-based apparent diffusion coefficient in distinguishing common pathological subtypes of cervical cancer[J]. Chinese Journal of Medical Computer Imaging,2015,21 (3): 255–259.DOI: 10.19627/j.cnki.cn31-1700/th.2015. 03.013. (in Chinese) .
[12]   TSUJIKAWA T, YAMAMOTO M, SHONO K, et al. Assessment of intratumor heterogeneity in mesenchymal uterine tumor by an 18F-FDG PET/CT texture analysis[J]. Ann Nucl Med, 2017, 31(10): 752-757.
doi: 10.1007/s12149-017-1208-x
[13]   CHEN Y L, LI R, CHEN T W, et al. Whole-tumour histogram analysis of pharmacokinetic parameters from dynamic contrast-enhanced MRI in resectable oesophageal squamous cell carcinoma can predict T-stage and regional lymph node metastasis[J]. Eur J Rad, 2019, 112-120.
doi: 10.1016/j.ejrad.2019.01.012
[14]   徐阿巧,赵振华,杨建峰,等.子宫平滑肌瘤MRI定量灌注参数与肿瘤微血管密度、VEGF及Ki-67的相关性研究[J]. 中华全科医学,2018,16(4):606–610.DOI:10.16766/j.cnki.issn.1674-4152.000169.XU Aqiao,ZHAO Zhenhua,YANG Jianfeng,et al. Correlations between of MRI quantitative perfusion parameters with MVD,VEGF and Ki-67 in uterine fibroids[J]. Chinese Journal of General Practice,2018,16 (4): 606–610.DOI:10.16766/j.cnki.issn.1674-4152.000169. (in Chinese) .
[15]   梁 挺,张毅力,杜红文,等. DCE-MRI直方图纹理分析对乳腺纤维瘤和浸润性导管癌鉴别的诊断价值[J]. 实用放射学杂志,2019,35(3):383–386,399.DOI:10.3969/j.issn.1002-1671.2019.03.011.LIANG Ting,ZHANG Yili,DU Hongwen,et al. The value of dcegmri histogram texture analysis in the differential diagnosis of breast fibroma and invasive ductal carcinoma[J]. Journal of Practical Radiology,2019,35 (3):383-386,399.DOI:10.3969/j.issn.1002-1671.2019.03.011. (in Chinese) .
[16]   LI Z, AI T, HU Y, et al. Application of whole-lesion histogram analysis of pharmacokinetic parameters in dynamic contrast-enhanced MRI of breast lesions with the CAIPIRINHA-Dixon-TWIST-VIBE technique[J]. J Magn Reson Imag, 2018, 47(1): 91-96.
doi: 10.1002/jmri.25762
[17]   朱旅聪,赵振华,杨建峰,等. 动态对比增强MRI三维直方图定量参数鉴别肝细胞癌和结直肠腺癌肝转移瘤异质性的价值[J]. 中国临床医学影像杂志,2019,30(10):721–725.DOI:10.12117/jccmi. 2019.10.009.ZHU Lyucong,ZHAO Zhenhua,YANG Jianfeng,et al. Value of quantitative parameters of three dimensional histogram by dynamic contrast enhanced MRI in differential diagnosis of the heterogeneity of hepatocellular carcinoma and hepatic metastases of colorectal adenocarcinoma[J]. Chinese Journal of Clinical Medical Imaging,2019,30(10): 721–725.DOI:10.12117/jccmi.2019.10. 009. (in Chinese) .
[18]   YOON S H, PARK C M, PARK S J, et al. Tumor heterogeneity in lung cancer:assessment with dynamic contrast-enhanced MR imaging[J]. Radiology, 2016, 280(3): 940-948.
doi: 10.1148/radiol.2016151367
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