Computer-aided Diagnosis in Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Malignant Tumor:A Technical Review of Current Research_Journal of Biomedical Engineering

Authors：

1. School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China;
2. Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou 511400, China;
3. Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510275, China;

Corresponding?author：

Keywords：

malignant tumor; dynamic contrast-enhanced magnetic resonance imaging; computer-aided diagnosis

DOI：

10.7507/1001-5515.20160128

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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide more information in diagnosis of malignant tumor compared to conventional magnetic resonance imaging (MRI). Nowadays, in order to utilize the information expediently and efficiently, many researchers are aiming at the development of computer-aided diagnosis (CAD) of malignant tumor based on DCE-MRI. In this review, we survey the research in this field and summarize the literature in four parts, i.e. ① image preprocessing——noise reduction and image registration; ② region of interests (ROI) segmentation; ③ feature extraction——exploring the image characteristics by analyzing the ROI quantitatively; ④ tumor lesion recognition and classification——distinguishing and classifying tumor lesions by learning the features of ROI. We summarize the application of CAD techniques of DCE-MRI for cancer diagnosis and, finally, give some discussion on how to improve the efficiency of CAD in the future research.

Citation： ZHOUYuxiang, QINJing, BINGuo, CHENHanwei, FENGShiting, WANGTianfu, HUANGBingsheng. Computer-aided Diagnosis in Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Malignant Tumor:A Technical Review of Current Research. Journal of Biomedical Engineering, 2016, 33(4): 794-800. doi: 10.7507/1001-5515.20160128 Copy

1.	陳萬青, 鄭榮壽, 張思維, 等.2003~2007年中國癌癥發病分析[J].中國腫瘤, 2012, 21(3):161-170.
2.	VERMA S, TURKBEY B, MURADYAN N, et al. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management[J]. AJR Am J Roentgenol, 2012, 198(6):1277-1288.
3.	PADHANI A R. Dynamic contrast-enhanced MRI in clinical oncology:Current status and future directions[J]. J Magn Reson Imaging, 2002, 16(4):407-422.
4.	CUENOD C A, BALVAY D. Perfusion and vascular permeability:basic concepts and measurement in DCE-CT and DCE-MRI[J]. Diagn Interv Imaging, 2013, 94(12):1187-1204.
5.	LANG N, SU M Y, YU H J, et al. Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI[J]. Magn Reson Imaging, 2013, 31(8):1285-1291.
6.	HUANG B, WONG C S, WHITCHER B, et al. Dynamic contrast-enhanced magnetic resonance imaging for characterising nasopharyngeal carcinoma:comparison of semiquantitative and quantitative parameters and correlation with tumour stage[J]. Eur Radiol, 2013, 23(6):1495-1502.
7.	王偉勝, 駱嘉偉, 林紅利.醫學圖像計算機輔助診斷數據平臺研究[J].中國生物醫學工程學報, 2013, 32(1):105-108.
8.	VAN GINNEKEN B, SCHAEFER-PROKOP C M, PROKOP M. Computer-aided diagnosis:how to move from the laboratory to the clinic[J]. Radiology, 2011, 261(3):719-732.
9.	GINNEKEN B V. CORNELIA M SCHAEFER-PROKOP, MATHIAS PROKOP.computer-aided diagnosis:how to move from the laboratory to the clinic[J]. Radiology, 2011, 261:719-732.
10.	BALVAY D, KACHENOURA N, ESPINOZA S A, et al. Signal-to-noise ratio improvement in dynamic contrast-enhanced CT and MR imaging with automated principal component analysis filtering[J]. Radiology, 2011, 258(2):435-445.
11.	AKBARI H, MACYSZYN L, DA X, et al. Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity[J]. Radiology, 2014, 273(2):502-510.
12.	CHIUSANO G, STAGLIANO A, BASSO C. Unsupervised tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging[J]. Artif Intell Med, 2014, 61(1):53-61.
13.	HAQ N F, KOZLOWSKI P, JONES E C, et al. A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI[J]. Comput Med Imaging Graph, 2015, 41(SI):37-45.
14.	GUBERN-MéRIDA A, MARTI R, MELENDEZ J, et al. Automated localization of breast cancer in DCE-MRI[J]. Med Image Anal, 2015, 20(1):265-274.
15.	LEVMAN J E, WARNER E, CAUSER P, et al. A vector machine formulation with application to the computer-Aided diagnosis of breast Cancer from DCE-MRI screening examinations[J]. J Digit Imaging, 2014, 27(1):145-151.
16.	MILENKOVIC J, HERTL K, KOSIR A, et al. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions[J]. Artif Intell Med, 2013, 58(2):101-114.
17.	GLASS J O, REDDICK W E. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma[J]. Magn Reson Imaging, 1998, 16(9):1075-1083.
18.	PANG Y, LI L, HU W, et al. Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced MR images using fuzzy c-means clustering and snake algorithm[J]. Comput Math Methods Med, 2012, 2012:634907.
19.	HOSSAM ABD EL MUNIM, ALY A FARAG, MOHAMEDABO EL-GHAR, et al. A new shape-based segmentation approach for the DCE-MRI kidney images[C]//IEEE International Symposium on Signal Processing and Information Technology. Giza:2007:1186-1191.
20.	ASHRAF A B, GAVENONIS S C, DAYE D, et al. A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk[J]. IEEE Trans Med Imaging, 2013, 32(4):637-648.
21.	CAI Hongmin, PENG Yanxia, OU Caiwen, et al. Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR:A machine learning approach[J]. PLoS One, 2014, 9(1):e87387.
22.	AGNER S C, ROSEN M A, ENGLANDER S A, et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images:a feasibility study[J]. Radiology, 2014, 272(1):91-99.
23.	HEYE T, DAVENPORT M S, HORVATH J J, et al. Reproducibility of dynamic contrast-enhanced MR imaging part Ⅰ. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions[J]. Radiology, 2013, 266(3):801-811.
24.	IRVING B, CIFOR A, PAPIEZ B L, et al. Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2014:17th International Conference. Boston:2014, Ⅰ:609-616.
25.	FARJAM R, TSIEN C I, LAWRENCE T S, et al. DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy[J]. Med Phys, 2014, 41(1):011708.
26.	WANG Yi, MORRELL G, HEIBRUN M E, et al. 3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction[J]. Acad Radiol, 2013, 20(2):137-147.

1. 陳萬青, 鄭榮壽, 張思維, 等.2003~2007年中國癌癥發病分析[J].中國腫瘤, 2012, 21(3):161-170.
2. VERMA S, TURKBEY B, MURADYAN N, et al. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management[J]. AJR Am J Roentgenol, 2012, 198(6):1277-1288.
3. PADHANI A R. Dynamic contrast-enhanced MRI in clinical oncology:Current status and future directions[J]. J Magn Reson Imaging, 2002, 16(4):407-422.
4. CUENOD C A, BALVAY D. Perfusion and vascular permeability:basic concepts and measurement in DCE-CT and DCE-MRI[J]. Diagn Interv Imaging, 2013, 94(12):1187-1204.
5. LANG N, SU M Y, YU H J, et al. Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI[J]. Magn Reson Imaging, 2013, 31(8):1285-1291.
6. HUANG B, WONG C S, WHITCHER B, et al. Dynamic contrast-enhanced magnetic resonance imaging for characterising nasopharyngeal carcinoma:comparison of semiquantitative and quantitative parameters and correlation with tumour stage[J]. Eur Radiol, 2013, 23(6):1495-1502.
7. 王偉勝, 駱嘉偉, 林紅利.醫學圖像計算機輔助診斷數據平臺研究[J].中國生物醫學工程學報, 2013, 32(1):105-108.
8. VAN GINNEKEN B, SCHAEFER-PROKOP C M, PROKOP M. Computer-aided diagnosis:how to move from the laboratory to the clinic[J]. Radiology, 2011, 261(3):719-732.
9. GINNEKEN B V. CORNELIA M SCHAEFER-PROKOP, MATHIAS PROKOP.computer-aided diagnosis:how to move from the laboratory to the clinic[J]. Radiology, 2011, 261:719-732.
10. BALVAY D, KACHENOURA N, ESPINOZA S A, et al. Signal-to-noise ratio improvement in dynamic contrast-enhanced CT and MR imaging with automated principal component analysis filtering[J]. Radiology, 2011, 258(2):435-445.
11. AKBARI H, MACYSZYN L, DA X, et al. Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity[J]. Radiology, 2014, 273(2):502-510.
12. CHIUSANO G, STAGLIANO A, BASSO C. Unsupervised tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging[J]. Artif Intell Med, 2014, 61(1):53-61.
13. HAQ N F, KOZLOWSKI P, JONES E C, et al. A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI[J]. Comput Med Imaging Graph, 2015, 41(SI):37-45.
14. GUBERN-MéRIDA A, MARTI R, MELENDEZ J, et al. Automated localization of breast cancer in DCE-MRI[J]. Med Image Anal, 2015, 20(1):265-274.
15. LEVMAN J E, WARNER E, CAUSER P, et al. A vector machine formulation with application to the computer-Aided diagnosis of breast Cancer from DCE-MRI screening examinations[J]. J Digit Imaging, 2014, 27(1):145-151.
16. MILENKOVIC J, HERTL K, KOSIR A, et al. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions[J]. Artif Intell Med, 2013, 58(2):101-114.
17. GLASS J O, REDDICK W E. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma[J]. Magn Reson Imaging, 1998, 16(9):1075-1083.
18. PANG Y, LI L, HU W, et al. Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced MR images using fuzzy c-means clustering and snake algorithm[J]. Comput Math Methods Med, 2012, 2012:634907.
19. HOSSAM ABD EL MUNIM, ALY A FARAG, MOHAMEDABO EL-GHAR, et al. A new shape-based segmentation approach for the DCE-MRI kidney images[C]//IEEE International Symposium on Signal Processing and Information Technology. Giza:2007:1186-1191.
20. ASHRAF A B, GAVENONIS S C, DAYE D, et al. A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk[J]. IEEE Trans Med Imaging, 2013, 32(4):637-648.
21. CAI Hongmin, PENG Yanxia, OU Caiwen, et al. Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR:A machine learning approach[J]. PLoS One, 2014, 9(1):e87387.
22. AGNER S C, ROSEN M A, ENGLANDER S A, et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images:a feasibility study[J]. Radiology, 2014, 272(1):91-99.
23. HEYE T, DAVENPORT M S, HORVATH J J, et al. Reproducibility of dynamic contrast-enhanced MR imaging part Ⅰ. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions[J]. Radiology, 2013, 266(3):801-811.
24. IRVING B, CIFOR A, PAPIEZ B L, et al. Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2014:17th International Conference. Boston:2014, Ⅰ:609-616.
25. FARJAM R, TSIEN C I, LAWRENCE T S, et al. DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy[J]. Med Phys, 2014, 41(1):011708.
26. WANG Yi, MORRELL G, HEIBRUN M E, et al. 3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction[J]. Acad Radiol, 2013, 20(2):137-147.

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