Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors

Wang Botao; Fan Wenping; Xu Huan; Li Lihui; Zhang Xiaohuan; Wang Kun; Liu Mengqi; You Junhao; Chen Zhiye

doi:10.24920/003516

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Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors

Chinese Medical Sciences Journal Vol. 34, Issue 1, Pages: 33-37(2019)

Original Articles | Updated：2024-04-10

- Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors
- Affiliations：
  
  1. Department of Radiology,Department of Oncology, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan 572013, China
  2. Department of Oncology, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan 572013, China
  3. Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
- Author bio：
  
  Junhao You E-mail: 24103936@qq.com
  * Zhiye Chen Tel: 86-898-37330852, E-mail: yyqf@hotmail.com;
- Funds：
- DOI：10.24920/003516
  CLC：
- Received：18 September 2018，
  
  Revised：22 February 2019，
  
  Published Online：30 March 2019，
  
  Published：30 March 2019
- Accepted：
Scan QR Code
Wang Botao, Fan Wenping, Xu Huan, et al. Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors[J]. Chinese medical sciences journal, 2019, 34(1): 33-37.
DOI：

Wang Botao, Fan Wenping, Xu Huan, et al. Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors[J]. Chinese medical sciences journal, 2019, 34(1): 33-37. DOI： 10.24920/003516.

摘要

目的

比较乳腺良、恶性肿瘤的磁共振成像(MRI)扩散加权(DWI)序列图像纹理特征的差异。

方法

选取在中国人民解放军总医院海南分院就诊的56例肿块性乳腺癌、16例乳腺纤维腺瘤和4例乳腺导管内乳头状肿瘤患者，根据术后的病理学诊断结果分成良性肿瘤组(n=20)和恶性肿瘤组(n=56)。回顾性分析受试者MRI扫描的DWI轴位图像，进行纹理特征分析，选取角二阶矩、对比度、自相关、逆差距、熵5个纹理特征参数作为分析指标。采用独立样本t检验及Mann-Whitney U检验分析两组之间纹理特征的差异。采用二元Logistic回归分析法建立回归模型，并进行受试者工作特征(ROC)曲线分析。

结果

在DWI图像纹理特征参数中，两组间的角二阶矩、对比度、自相关及熵的差异具有统计学意义(P

角二阶矩

= 0.014，P

对比度

= 0.019，P

自相关

= 0.010，P熵 = 0.007)。以上4个指标的ROC曲线下面积为0.685，0.681，0.754和0.683，纳入Logistic回归模型的联合变量(角二阶矩、对比度和熵)的ROC曲线下面积为0.802。二元Logistic回归分析提示角二阶矩、对比度和熵可以作为鉴别乳腺良、恶性肿瘤的变量。

结论

MRI的DWI图像纹理特征分析可以作为乳腺良、恶性肿瘤的鉴别诊断工具。

Abstract

Objective

To investigate the difference in texture features on diffusion weighted imaging (DWI) images between breast benign and malignant tumors.

Methods

Patients including 56 with mass-like breast cancer

16 with breast fibroadenoma

and 4 with intraductal papilloma of breast treated in the Hainan Hospital of Chinese PLA General Hospital were retrospectively enrolled in this study

and allocated to the benign group (20 patients) and the malignant group (56 patients) according to the post-surgically pathological results. Texture analysis was performed on axial DWI images

and five characteristic parameters including Angular Second Moment (ASM)

Contrast

Correlation

Inverse Difference Moment (IDM)

and Entropy were calculated. Independent sample

-test and Mann-Whitney

test were performed for intergroup comparison. Regression model was established by using Binary Logistic regression analysis

and receiver operating characteristic curve (ROC) analysis was carried out to evaluate the diagnostic efficiency.

Results

The texture features ASM

Contrast

Correlation and Entropy showed significant differences between the benign and malignant breast tumor groups (

ASM

=0.014

contrast

=0.019

correlation

=0.010

entropy

=0.007). The area under the ROC curve was 0.685

0.681

0.754

and 0.683 respectively for the positive texture variables mentioned above

and that for the combined variables (ASM

Contrast

and Entropy) was 0.802 in the model of Logistic regression. Binary Logistic regression analysis demonstrated that ASM

Contrast and Entropy were considered as the specific imaging variables for the differential diagnosis of breast benign and malignant tumors.

Conclusion

The texture analysis of DWI may be a simple and effective tool in the differential diagnosis between breast benign and malignant tumors.

关键词

Keywords

references

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