肿瘤防治研究  2018, Vol. 45 Issue (12): 1009-1013    DOI: 10.3971/j.issn.1000-8578.2018.18.0815
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基于Tirm序列联合直方图及纹理特征分析鉴别乳腺良恶性病变
李晓峰,刘彩云,石岩,罗小虎,王刚,姚标
221004 徐州,徐州市肿瘤医院放射科
Differential Diagnosis of Benign and Malignant Breast Lesions by Texture Feature Combined with Histogram Parameter Based on Tirm Sequence
LI Xiaofeng, LIU Caiyun, SHI Yan, LUO Xiaohu, WANG Gang, YAO Biao
Department of Radiology, Xuzhou Cancer Hospital, Xuzhou 221004, China
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摘要 目的 分析乳腺良恶性病变在T2反转恢复压脂序列上的直方图及纹理特征,并评价诊断乳腺良恶性病变的效能。方法 回顾性分析100例行MRI检查并经病理证实的乳腺肿瘤资料。纹理特征提取采用灰度共生矩阵法得到病灶能量值、熵值、惯量值、自相关性、逆差矩及群显著性;直方图分析得到平均值、偏度、峰度。分别评价不同参数值在乳腺良恶性病变中的诊断效能。构建受试者工作特征曲线(ROC)结合临床实际评价乳腺良恶性组间的差异,确定诊断临界值。结果 各参数在良恶性组间比较,自相关性、群显著性、平均值及峰度值差异均有统计学意义(均P<0.001);能量值、熵值、惯量值、逆差距及偏度值差异无统计学意义(P>0.05)。构建ROC将自相关性、群显著性、平均值及峰度值分别用于诊断乳腺良恶性病变,均有统计学意义(P<0.001),联合四组参数诊断良恶性病变,曲线下面积为0.868,敏感度为90.57%,特异性为72.34%。结论 基于Tirm序列的直方图及纹理分析可用于乳腺良恶性病变的鉴别诊断,自相关性、群显著性、平均值及峰度值在Tirm序列上对诊断乳腺良恶性病变有一定意义,联合诊断可提高诊断效能。
关键词 磁共振成像乳腺肿瘤直方图分析纹理分析鉴别诊断    
Abstract:Objective To explore histogram parameters and texture features of benign and malignant breast lesion on turbo inversion recovery magnitude(Tirm) sequence, and evaluate which parameter could help best differentiate benign from malignant breast lesion. Methods This retrospective study included 100 breast cancer patients who underwent conventional MRI and confirmed pathologically. Texture features were derived from the gray level co-occurrence matrix(GLCM), and entropy, energy, correlation, inertia, inverse difference moment, cluster prominence and mean value, skewness, kurtosis of histogram parameters were calculated. Then we assessed the diagnosis efficacy with these parameters among the variety kinds of benign and malignant breast lesions respectively, and establish the receiver-operating characteristic curve(ROC). We assessed the differences between benign and malignant groups by the Yoden index combined with clinic for the cut-off values. Results The differences of correlation, cluster prominence, mean value and kurtosis were statistically significant between the benign and malignant groups(all P<0.001); the differences of energy, entropy, inertia, inverse difference moment and skewness were not statistically significant(all P>0.05). The results of ROC with correlation, cluster prominence, mean value and kurtosis on the diagnosis of benign and malignant breast lesions were statistically significant, respectively(P<0.001). Combined the four parameters on diagnosis benign and malignant lesions, the AUC was 0.868, the sensitivity was 90.57% and the specificity was 72.34%. Conclusion The histogram analysis and texture analysis based on Tirm sequence could be used for the differential diagnosis of benign and malignant breast lesions. Correlation, cluster prominence, mean value and kurtosis have certain significance in the diagnosis. The combined diagnosis could improve the differential ability of benign and malignant breast lesions.
Key wordsMRI    Breast tumor    Histogram analysis    Texture analysis    Differential diagnosis
收稿日期: 2018-06-15      出版日期: 2018-12-12
:  R445.2  
  R737.9  
基金资助:徐州市科技局社会发展项目(KC16SL105)
通讯作者: 石岩,E-mail: xzshiyan@163.com   
作者简介: 李晓峰(1986-),男,硕士,主治医师,主要从事乳腺影像诊断学方面的研究
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李晓峰
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引用本文:   
李晓峰, 刘彩云, 石岩, 罗小虎, 王刚, 姚标. 基于Tirm序列联合直方图及纹理特征分析鉴别乳腺良恶性病变[J]. 肿瘤防治研究, 2018, 45(12): 1009-1013.
LI Xiaofeng, LIU Caiyun, SHI Yan, LUO Xiaohu, WANG Gang, YAO Biao. Differential Diagnosis of Benign and Malignant Breast Lesions by Texture Feature Combined with Histogram Parameter Based on Tirm Sequence. Cancer Research on Prevention and Treatment, 2018, 45(12): 1009-1013.
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[1] Gallego-Ortiz C, Martel AL. Using quantitative features extracted
from T2-weighted MRI to improve breast MRI computer-aided
diagnosis (CAD)[J]. PLoS One, 2017, 12(11): e0187501.
[2] Imbriaco M, Cuocolo R. Does Texture Analysis of MR Images
of Breast Tumors Help Predict Response to Treatment?[J].
Radiology, 2018, 286(2): 421-3.
[3] Wu J, Gong G, Cui Y, et al. Intratumor partitioning and texture
analysis of dynamic contrast-enhanced (DCE)-MRI identifies
relevant tumor subregions to predict pathological response of
breast cancer to neoadjuvant chemotherapy[J]. J Magn Reson
Imaging, 2016, 44(5): 1107-15.
[4] Pickles MD, Lowry M, Gibbs P. Pretreatment Prognostic Value
of Dynamic Contrast-Enhanced Magnetic Resonance Imaging
Vascular, Texture, Shape, and Size Parameters Compared With
Traditional Survival Indicators Obtained From Locally Advanced
Breast Cancer Patients[J]. Invest Radiol, 2016, 51(3): 177-85.
[5] Ko ES, Kim JH, Lim Y, et al. Assessment of Invasive Breast
Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance
Imaging Texture Analysis[J]. Medicine(Baltimore), 2016, 95(3):
e2453.
[6] 孙赛花, 周纯武, 赵莉芸, 等. 动态增强磁共振成像纹理分析
预测乳腺癌新辅助化疗疗效[J]. 中华肿瘤杂志, 2017, 39(5):
344-9. [Sun SH, Zhou CW, Zhao LY, et al. Texture analysis based
on contrast-enhanced MRI can predict treatment response to
neoadjuvant chemotherapy of breast cancer [J]. Zhonghua Zhong
Liu Za Zhi, 2017, 39(5): 344-9.]
[7] Prevos R, Smidt ML,Tjan-Heijnen VC, et al. Pre-treatment
differences and early response monitoring of neoadjuvant
chemotherapy in breast cancer patients using magnetic resonance
imaging: a systematic review[J]. Eur Radiol, 2012, 22(12):
2607-16.
[8] Waugh SA, Purdie CA, Jordan LB, et al. Magnetic resonance
imaging texture analysis classification of primary breast cancer[J].
Eur Radiol, 2016, 26(2): 322-30.
[9] Salem A, O’Connor JPB. Assessment of Tumor Angiogenesis:
Dynamic Contrast- enhanced MR Imaging and Beyond[J]. Magn
Reson Imaging Clin N Am, 2016, 24(1): 45-56
[10] 张竹伟, 华婷, 徐婷婷, 等. 常规MRI纹理分析鉴别乳腺良、恶
性病变的价值初探[J]. 中华放射学杂志, 2017, 51(8): 588-91.
[Zhang ZW, Hua T, Xu TT, et al. Differentiation of benign and
malignant breast lesions using texture analysis of conventional
MRI:a preliminary study[J]. Zhonghua Fang She Xue Za Zhi,
2017, 51(8): 588-91.]
[11] 冯红梅, 郭彩平, 徐志锋, 等. 乳腺X线摄影和MRI直方图在鉴别
乳腺纤维腺瘤和浸润性导管癌中的价值[J]. 医学影像学杂志,
2017, 27(1): 75-8. [Feng HM, Guo CP, Xu ZF, et al. The value of
X-ray photography and MRI histogram in distinguishing of breast
fibroadenoma and infiltrating ductal car-cinoma[J]. Yi Xue Ying
Xiang Xue Za Zhi, 2017, 27(1): 75-8.]
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