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

doi:10.3971/j.issn.1000-8578.2018.18.0815

Cancer Research on Prevention and Treatment > 2018 > 45(12): 1009-1013. > DOI: 10.3971/j.issn.1000-8578.2018.18.0815

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[J]. Cancer Research on Prevention and Treatment, 2018, 45(12): 1009-1013. DOI: 10.3971/j.issn.1000-8578.2018.18.0815

Citation:

PDF (6880 KB)

Differential Diagnosis of Benign and Malignant Breast Lesions by Texture Feature Combined with Histogram Parameter Based on Tirm Sequence

Department of Radiology, Xuzhou Cancer Hospital, Xuzhou 221004, China

More Information

Corresponding author:
SHI Yan, E-mail:xzshiyan@163.com
Received Date: June 14, 2018
Revised Date: September 24, 2018
Available Online: January 12, 2024

Graphical Abstract

Abstract

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.
- MRI,
- Breast tumor,
- Histogram analysis,
- Texture analysis,
- Differential diagnosis

FullText(HTML)

References (11)

References

[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. doi: 10.1371/journal.pone.0187501
[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. doi: 10.1148/radiol.2017172454
[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. doi: 10.1002/jmri.25279
[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. doi: 10.1097/RLI.0000000000000222
[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. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=8c0b24339f353624ba381e15cd9f78fc
[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. doi: 10.3760/cma.j.issn.0253-3766.2017.05.005 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. doi: 10.3760/cma.j.issn.0253-3766.2017.05.005
[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. doi: 10.1007/s00330-012-2653-5
[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. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0e918f60a991f47f32e564c7c67352a8
[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 doi: 10.1016/j.mric.2015.08.010
[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. doi: 10.3760/cma.j.issn.1005-1201.2017.08.006 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. doi: 10.3760/cma.j.issn.1005-1201.2017.08.006
[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. http://d.old.wanfangdata.com.cn/Periodical/yxyxxzz201701020 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. http://d.old.wanfangdata.com.cn/Periodical/yxyxxzz201701020

[1]	DAI Nan, ZHAO Xiaolong, DAI Xiaoyan, LI Mengxia. Effect of Exosomal APE1 on Sensitivity of NSCLC A549 Cells to Cisplatin[J]. Cancer Research on Prevention and Treatment, 2020, 47(7): 492-497. DOI: 10.3971/j.issn.1000-8578.2020.19.1609
[2]	HUANG Zejian, FANG Chang, YU Baodan, CHENG Qing, LYU Ping. 3E10 Targeting CD24 Enhances Chemotherapy Sensitivity of Hepatocellular Carcinoma HuH-7 Cells[J]. Cancer Research on Prevention and Treatment, 2018, 45(8): 540-544. DOI: 10.3971/j.issn.1000-8578.2018.17.1484
[3]	CAI Rui, CHEN Qiuqiu, JIANG Wei. 5-azacytidine Increases Radiation Sensitivity of Nasopharyngeal Carcinoma Cell Line C666-1[J]. Cancer Research on Prevention and Treatment, 2017, 44(2): 94-97. DOI: 10.3971/j.issn.1000-8578.2017.02.003
[4]	ZHANG Juping, SHI Yehui, JIA Yongsheng, ZHOU Liyan, TONG Zhongsheng. GDF11 is Involved in Human Hepatic Carcinoma Cells SMMC-7721 Proliferation and Sensitivity to DDP[J]. Cancer Research on Prevention and Treatment, 2016, 43(6): 459-462. DOI: 10.3971/j.issn.1000-8578.2016.06.005
[5]	LI Wei, PENG Junqin, LI Jiansheng, TANG Rijie. MR Apparent Diffusion Coefficient Predicts Sensitivity of Nasopharyngeal Carcinoma to Radiotherapy and Related Factors[J]. Cancer Research on Prevention and Treatment, 2015, 42(12): 1221-1226. DOI: 10.3971/j.issn.1000-8578.2015.12.011
[6]	HU Lili, YIN Yanjun, ZHONG Wenjuan, QIU Feng. miR-200c Enhances Sensitivity of Lung Cancer Cell A549 to Paclitaxel and Gefitinib and Related Mechanism[J]. Cancer Research on Prevention and Treatment, 2015, 42(08): 760-764. DOI: 10.3971/j.issn.1000-8578.2015.08.003
[7]	CAO Xinmei, ZHANG Daiquan, XIA Jiyi, WANG Xu, GAO Yan, XIONG Wei. Effects of HER2 shRNA on Chemotherapy Sensitivity of Mouse Lewis Cells[J]. Cancer Research on Prevention and Treatment, 2014, 41(05): 366-368. DOI: 10.3971/j.issn.1000-8578.2014.05.004
[8]	Yang Qingshan, Liu Yuanyuan, Jiang Lipeng. Effect of Expression Vector of Human BAG-1 Gene on Radio-sensitivity of Lung Adenocarcinoma Cells[J]. Cancer Research on Prevention and Treatment, 2012, 39(02): 127-129. DOI: 10.3971/j.issn.1000-8578.2012.02.002
[9]	FANG Chuan, TAN Yan-li, WANG Jia-liang, SHI Yan-fang, SHAN Xiao-song, LI Wei. Primary Culture and Drug Sensitivity of Human Glioma Cells[J]. Cancer Research on Prevention and Treatment, 2010, 37(12): 1380-1382. DOI: 10.3971/j.issn.1000-8578.2010.12.012
[10]	ZHANG Wei, GU Min. Geldanamycin Sensitizes Human Breast Cancer Cells to Adriamycin both in vitro and in vivo[J]. Cancer Research on Prevention and Treatment, 2010, 37(10): 1109-1112. DOI: 10.3971/j.issn.1000-8578.2010.10.004

Cited By

Cited by

Periodical cited type(9)

1.	成俊，徐雪峰，李伟. D-CBCT在肺癌容积旋转调强计划精准治疗中的临床应用. 中国CT和MRI杂志. 2024(04): 35-37 .
2.	蒋浩，曹新超，吴桐，郭志斌. 低剂量CT联合外周血循环肿瘤细胞在肺癌早期诊断中的价值研究. 中国CT和MRI杂志. 2024(06): 57-60 .
3.	王晶，翟成凯. 肿瘤自身抗体及CT人工智能在NSCLC早期诊断中的应用研究进展. 解放军医学杂志. 2024(07): 848-854 .
4.	李雪娇. 多排螺旋CT低剂量胸部扫描在肺癌早期筛查中的应用价值分析. 医药前沿. 2024(15): 79-81 .
5.	易梅玲，易芳玲. 双层探测器光谱CT智能剂量调控技术在低剂量胸部体检中的应用价值. 中国医疗器械信息. 2024(22): 8-10+81 .
6.	杏子，彭源. 低剂量CT在肺癌筛查中的价值. 中国防痨杂志. 2024(S2): 234-236 .
7.	陈蔚，蒋伟. 低剂量螺旋CT扫描用于肺癌筛查及鉴别的效果评估. 深圳中西医结合杂志. 2023(01): 71-73 .
8.	慕珂珂，肖凌云，董炎红. 早期肺癌患者血清和胸腔积液中CYFRA211、NSE及SCC水平变化及意义. 中国实用医刊. 2023(09): 34-37 .
9.	陈彪. 低剂量螺旋CT联合血清肿瘤标志物在肺癌诊断中的临床应用价值. 影像研究与医学应用. 2023(22): 24-26 .

Other cited types(1)

Get Citation

PDF

XML

Article views (1866) PDF downloads (232) Cited by(10)

Turn off MathJax

Article Contents

Abstract

References

Differential Diagnosis of Benign and Malignant Breast Lesions by Texture Feature Combined with Histogram Parameter Based on Tirm Sequence