Spectral CT imaging in detecting breast cancer: a pilot study_West China Medical Journal

Authors：

CHEN Jiao , GUAN Bin , LIU Yongxiu , WANG Jinqiu , GUO Zhongjie ,  LIN Hua

Department of Radiology, Mianyang Center Hospital, Mianyang, Sichuan 621000, P. R. China;

Corresponding author：

LIN Hua, Email: 2947068967@qq.com

Keywords：

Tomography, X-ray computed; Spectral imaging; Breast cancer

DOI：

10.7507/1002-0179.201702050

Video：

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Objective To analyze the ability of spectral CT imaging in displaying breast cancer lesions and explore the value of spectral CT imaging in detecting breast cancer. Methods The spectral CT images with different parameters of sixty-eight breast cancer lesions confirmed by pathology between July 2013 and February 2016 were retrospectively analyzed. The contrast noise ratio (CNR) and signal to noise ratio (SNR) as well as the ability in detecting breast cancer of the images with different parameters were compared. Results Fat-water material decomposition images showed breast cancer lesions best. Iodine-water material decomposition images had the lowest CNR. 70 keV monochromatic images and the monochromatic of best CNR images had better SNR. Fat-water material decomposition images detected all of the breast cancer lesions. Conventional CT plain scan detected least lesions. Conclusion Spectral CT imaging, especially fat-water material decomposition images, can show breast cancer lesions well, which has the potential application for detection of breast cancer lesions.

Citation： CHEN Jiao, GUAN Bin, LIU Yongxiu, WANG Jinqiu, GUO Zhongjie, LIN Hua. Spectral CT imaging in detecting breast cancer: a pilot study. West China Medical Journal, 2017, 32(11): 1739-1743. doi: 10.7507/1002-0179.201702050 Copy

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1. 陈万青, 郑荣寿. 中国女性乳腺癌发病死亡和生存状况. 中国肿瘤临床, 2015(13): 668-674.
2. Shareef M, Ashraf MA, Sarfraz M. Natural cures for breast cancer treatment. Saudi Pharm J, 2016, 24(3): 233-240.
3. Long Y, Fessler JA. Multi-material decomposition using statistical image reconstruction for spectral CT. IEEE Trans Med Imaging, 2014, 33(8): 1614-1626.
4. Ma G, He T, Yu Y, et al. Improving image quality of bronchial arteries with virtual monochromatic spectral CT images. PLoS One, 2016, 11(3): e0150985.
5. Jaeger BM, Hong AS, Letter H, et al. Advancements in imaging technology for detection and diagnosis of palpable breast masses. Clin Obstet Gynecol, 2016, 59(2): 336-350.
6. Yang Q, Li L, Zhang J, et al. A new quantitative image analysis method for improving breast cancer diagnosis using DCE-MRI examinations. Med Phys, 2015, 42(1): 103-109.
7. Huang J, Yu J, Peng Y. Association between dynamic contrast enhanced MRI imaging features and WHO histopathological grade in patients with invasive ductal breast cancer. Oncol Lett, 2016, 11(5): 3522-3526.
8. Wang X, Wang XJ, Song HS, et al. ¹H-MRS evaluation of breast lesions by using total choline signal-to-noise ratio as an indicator of malignancy: a meta-analysis. Med Oncol, 2015, 32(5): 160.
9. Dijkstra H, Dorrius MD, Wielema M, et al. Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions. J Magn Reson Imaging, 2016, 44(6): 1642-1649.
10. Ertas G, Onaygil C, Akin Y, et al. Quantitative differentiation of breast lesions at 3T diffusion-weighted imaging (DWI) using the ratio of distributed diffusion coefficient (DDC). J Magn Reson Imaging, 2016, 44(6): 1633-1641.
11. Jodłowska E, Czepczyński R, Wyszomirska A, et al. Application of positron emission tomography (PET/CT) in diagnosis of breast cancer. Part I. Diagnosis of breast cancer prior to treatment. Contemp Oncol (Pozn), 2016, 20(1): 8-12.
12. Ho AM, Kalantari BN. PET/MRI: a new frontier in breast cancer imaging. Breast J, 2016, 22(3): 261-263.
13. 杨为戈, 石洪成, 谭辉, 等. 乳腺专用伽马成像在乳腺癌诊断中的意义. 中华乳腺病杂志: 电子版, 2014(4): 16-18.
14. 李蔚萍, 苗华栋, 唐健雄, 等. 多排螺旋 CT 在乳腺癌诊断和术前评估中的价值. 中华普通外科杂志, 2011, 26(1): 29-32.
15. 张文军, 田明华, 张洪胜, 等. 能谱 CT 在乳腺癌诊断中的初步应用研究. 中国医师进修杂志, 2015, 38(4): 262-266.

West China Medical Journal

Spectral CT imaging in detecting breast cancer: a pilot study

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