Value of histogram analysis of apparent diffusion coefficient maps in differentiating breast lesions

SHEN Lijuan; MENG Fanhua; SUN Ruihong; JIANG Zhaoxia; PENG Weijun

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Value of histogram analysis of apparent diffusion coefficient maps in differentiating breast lesions

更新时间：2025-12-15

- Value of histogram analysis of apparent diffusion coefficient maps in differentiating breast lesions
- Vol. 26, Issue 2, Pages: 114-120(2017)
- 作者机构：
  
  1. 复旦大学附属上海市第五人民医院放射科，复旦大学上海医学院妇产科学系,上海,200240
  2. 复旦大学附属肿瘤医院放射诊断科，复旦大学上海医学院肿瘤学系,上海,200032
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：02 June 2017，
  
  Published：02 June 2017
- 稿件说明：
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沈丽娟,孟凡华,孙瑞红,等. 采用表观扩散系数直方图分析法诊断乳腺肿块样病变的价值[J]. 肿瘤影像学, 2017, 26(2): 114-120
DOI：

沈丽娟,孟凡华,孙瑞红,等. 采用表观扩散系数直方图分析法诊断乳腺肿块样病变的价值[J]. 肿瘤影像学, 2017, 26(2): 114-120 DOI：

摘要

目的：

探讨MRI表观扩散系数(apparent diffusion coefficient，ADC)直方图分析法鉴别诊断乳腺肿块样病变良恶性的价值。

方法：

回顾性分析91例乳腺肿块患者(单发病灶82例、多发病灶8例；总病灶104个，良性病变25个、恶性病变79个)资料。所有患者均行3.0 T乳腺MRI增强和扩散加权成像(diffusion weighted imaging，DWI)扫描，并与病理结果对照。采用Medlab软件对ADC图进行直方图分析，观察肿块的直方图特征并获得直方图参数，包括各百分比ADC、平均ADC(ADCmean)、最小ADC(ADCmin)、最大ADC(ADCmax)、偏度及峰度。采用t检验分析良恶性病变的直方图各参数，采用受试者工作特征(receiver operating characteristic，ROC)曲线分析各参数的鉴别诊断效能。

结果：

恶性病变各ADC值均小于良性病变，除ADCmax(

=0.113)外差异均具有统计学意义(

＜0.05)。恶性病变的偏度系数、峰度系数高于良性病变，但无统计学差异(

=0.191、0.165)。25th ADC的曲线下面积(area under curve，AUC)最大，为0.814，灵敏度、特异度分别为0.88、0.696。ADCmean为0.9210

-3

/s时AUC为0.79，灵敏度、特异度分别为0.92、0.658。

结论：

ADC直方图分析可为诊断乳腺肿块样病变提供更多定量信息，对鉴别病变良恶性具有一定价值。

Abstract

Objective:

To evaluate the value of histogram analysis of apparent diffusion coefficient (ADC) in differentiating benign breast lesions from malignancy.

Methods:

Ninety-one patients with breast masses were retrospectively analyzed (82 single and 8 multiple; 104 lesions in total; benign 25 and malignant 79). All patients received MR examination including enhanced

MRI and diffusion weighted imaging (DWI) and were confirmed by pathology. Histogram analysis of ADC maps was performed by using Medlab software to observe the histogram features of tumors and obtain the histogram parameters

including percentile ADC values

ADCmean

ADCmin

ADCmax

skewness and kurtosis. All parameters were analyzed by using t-test between benign and malignant groups

then the receiver operating characteristic (ROC) curves were constructed to observe the efficiency of differential diagnosis.

Results:

All ADC values were lower in malignant lesions than benign lesions

the differences in the above parameters except ADCmax (

=0.113) were statistically significant (

0.05). Skewness and kurtosis were higher in malignant lesions than benign lesions

but had no statistical significance (

=0.191 and 0.165

respectively). The areas under the ROC curves (AUCs) of 10th-50th ADCs were higher than ADCmean. The ACU of 25th ADC was the highest (0.814) at the cutoff value of 0.8810

-3

and the sensitivity and specificity were 0.88 and 0.696

respectively. The ACU of ADCmean was 0.79 at the cutoff value of 0.9210

-3

and the sensitivity and specificity were 0.92 and 0.658

respectively.

Conclusion:

ADC histogram analysis is valuable in differentiating benign breast lesions from malignancy by providing additional quantitative parameters.

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