Recently, the use of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) technique is widely used to detect and diagnose breast cancer. This technique has shown to be very useful particularly in screening women with high risk for breast cancer, as well as assessing the potential effects of new therapy. Thus, the aim of the present study is to appraise the efficacy of combined employment of global and local features in discriminating malignant and benign lesions. A dataset of one hundred and twenty one (121) DCE-MRI investigations was assembled and used. Out of that number, fifty (50) were biopsy-proved malignant tumors and seventy-one (71) were benign. Firstly, the suspicious mass regions were automatically detected and segmented with 3D region growing algorithm. Meanwhile, Local and global features were used. Thereafter, sequential floating forward selection method (SFFS) and support vector machine classifier (SVM) were used for classification. The overall classification performance of different kind of features were evaluated via receiver operating characteristic (ROC) analysis in a 3-fold cross validation scheme. It was observed that global feature produced classification accuracy of 84.32 % followed by local feature with accuracy of 85.95 %. When the local and global features were combined, the classification accuracy increased to 94.36 %. Based on the obtained results, this study has demonstrated that the combined use of local and global features could effectively function as a better indicator in differentiating malignant and benign tumors.
| Published in | International Journal of Clinical Oncology and Cancer Research (Volume 6, Issue 4) |
| DOI | 10.11648/j.ijcocr.20210604.12 |
| Page(s) | 151-156 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Breast Cancer, DCE-MRI, Local and Global Features, Benign and Malignant Tumor
| [1] | https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2017/cancer-facts-and-figures-2017.pdf |
| [2] | Tabár, L., Vitak, B., Chen, H. H. T., Yen, M. F., Duffy, S. W., Smith, R. A. (2001). Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer. Interdisciplinary international journal of the American cancer society, 91 (9), 1724-1731. |
| [3] | Smith, R. A., Manassaram-Baptiste, D., Brooks, D., Doroshenk, M., Fedewa, S., Saslow, D, Wender, R. (2015). Cancer screening in the United States, 2015: a review of current American cancer society guidelines and current issues in cancer screening. CA: a cancer journal for clinicians, 65 (1), 30-54. |
| [4] | Fenton, J. J., Egger, J., Carney, P. A., Cutter, G., D'Orsi, C., Sickles, E. A., Hendrick, R. E. (2006). Reality check: perceived versus actual performance of community mammographers. American Journal of Roentgenology, 187 (1), 42-46. |
| [5] | Berlin, L., & Hall, F. M. (2010). More More mammography muddle: emotions, Politics, science, costs, and polarization. Radiology, 255 (2), 311-316. |
| [6] | Kriege, M., Brekelmans, C. T., Boetes, C., Bernard, P. E., Wonderland, H. M., Obdeijn, I. M., & Muller, S. H. (2004). Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. New England Journal of Medicine, 351 (5), 427-437. |
| [7] | Klijn, J. G. M. (2010). Early diagnosis of hereditary breast cancer by magnetic resonance imaging: What is realistic? Clin. Oncol. 28, 1441–1445. |
| [8] | Graham, L. J., Shupe, M. P., Schneble, E. J. (2014). Current approaches and challenges in monitoring treatment 405 response in breast cancer. Cancer 5, 58–68. |
| [9] | Klijn J. G. M., “Early diagnosis of hereditary breast cancer by magnetic resonance imaging: J. Clin. Oncol, 28, 1441–1445, Doi: 10.1200/JCO.2009.26.3467, (2010). |
| [10] | Graham L. J., Shupe M. P., Schneble E. J., “Current approaches and challenges in monitoring treatment 405 response in breast cancer,” J. Cancer, 5: 58–68 DOI: 10.7150/jca.7047, (2014). |
| [11] | Kuhl CK., Klaschik S., Mielcarek P., Gieseke J., Wardelmann E., Schild HH. (1999). Do T2-weighted pulse? Sequences help with the differential diagnosis of Enhancing lesions in dynamic breast MRI? Journal of Magnetic resonance imaging. 9 (2): 187–196 Doi.org/10.1002/ (SICI) 1522-2586. |
| [12] | Nishikawa, RM., Pesce, LL. (2013). Estimating sensitivity and specificity for technology assessment based on observer studies, Academic radiology. 20 (7): 825–30. doi.org/10.1016/j.acra. |
| [13] | Faranak Aghaei., Maxine Tan., Alan B. Hollingsworth., Samuel Cheng., Bin Zheng. (2016). Computer aided global breast MR image feature analysis for prediction of tumor response to chemotherapy, Medical imaging, 1605-7422/16/518, doi: 10.111/12.22.16326. |
| [14] | Anuradha, CK., Pritti, PR. (2016). Fusion of local and global features for classification of abnormality in mammograms, Indian Academy of Sciences, 41 (4) 385–395, DOI 10.1007/s12046-016-0482-y. |
| [15] | Giger ML., Chan HP, Boone J. (2008). History and status of CAD and quantitative image analysis, the role of Medical Physics and AAPM, medical physics, 35 (12): 5799–5820, doi.org/10.1118/1.3013555 PMID. |
| [16] | Zheng, Y., Baloch S., Englander, S., Schnall, MD., Shen D. (2007). Segmentation and classification of breast tumor using dynamic-contrast enhanced MR images. Med Image Comput Assist Interv, 10: 393–401 14. |
| [17] | Agner, SC., Soman, S., Libfeld E., McDonald, M., Thomas, K., Englander, S., Rosen, MA., Chin D, Nosher J, Madabhushi A. (2011). Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification, 24: 446–463 15, Doi 10.1007/s10278-010-9298-1. |
| [18] | Yading, Yuan., Maryellen, Giger L., Hui Li., Neha Bhooshan., Charlene Sennett A. (2010). Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol, 17 (9): 1158-1167, Doi: 10.1016/j.acra.2010.04.015. |
| [19] | DeMartini, W., Lehman, C., Partridge S., (2008). “Breast MRI for cancer detection and characterization: a review of evidence-based clinical applications,” Academic radiology, vol. 15, pp. 408-416. |
| [20] | Morris, E. A.; Lieberman, L.; (2015). Breast MRI- diagnosis and intervention New York, springer. |
| [21] | Orel, S. G.; Schnall, M. D.; (2001). MR imaging of the breast for the detection, diagnosis and staging of the breast cancer, Radiology, 220 (1): 13-30. |
| [22] | Fusco R, Di Marzo M, Sansone C, Sansone M, Petrillo A. Breast DCEMRI: Lesion classification using dynamic and morphological features by means of a multiple classifier system. Eur Radiol Exp 2017; 1: 10. |
| [23] | Chen W, Wei MU, Zhang W. Value of texture feature analysis of MRI dynamic contrast enhancement in diagnosis of benign and malignant breast nodules. Chin J Med Imaging Technol 2017; 33: 647–651. |
| [24] | Dalmis MU, Gubern-Merida A, Vreemann S, Karssemeijer N, Mann R, Platel B. A computer-aided diagnosis system for breast DCE-MRI at high spatiotemporal resolution. Med Phys 2016; 43: 84. |
APA Style
Arukalam Felicity Mmaezi, Okafor Sixtus Amarachukwu, Okafor Wilson Chimaobi, Eziefuna Ebere Oniyinye, Okafor Afoma Lorreta, et al. (2021). Combined Use of Local and Global Features for Classification of Breast Lesion Using DCE-MRI Images. International Journal of Clinical Oncology and Cancer Research, 6(4), 151-156. https://doi.org/10.11648/j.ijcocr.20210604.12
ACS Style
Arukalam Felicity Mmaezi; Okafor Sixtus Amarachukwu; Okafor Wilson Chimaobi; Eziefuna Ebere Oniyinye; Okafor Afoma Lorreta, et al. Combined Use of Local and Global Features for Classification of Breast Lesion Using DCE-MRI Images. Int. J. Clin. Oncol. Cancer Res. 2021, 6(4), 151-156. doi: 10.11648/j.ijcocr.20210604.12
AMA Style
Arukalam Felicity Mmaezi, Okafor Sixtus Amarachukwu, Okafor Wilson Chimaobi, Eziefuna Ebere Oniyinye, Okafor Afoma Lorreta, et al. Combined Use of Local and Global Features for Classification of Breast Lesion Using DCE-MRI Images. Int J Clin Oncol Cancer Res. 2021;6(4):151-156. doi: 10.11648/j.ijcocr.20210604.12
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Download@article{10.11648/j.ijcocr.20210604.12,
author = {Arukalam Felicity Mmaezi and Okafor Sixtus Amarachukwu and Okafor Wilson Chimaobi and Eziefuna Ebere Oniyinye and Okafor Afoma Lorreta and Chibuike Tochukwu Emmanuel},
title = {Combined Use of Local and Global Features for Classification of Breast Lesion Using DCE-MRI Images},
journal = {International Journal of Clinical Oncology and Cancer Research},
volume = {6},
number = {4},
pages = {151-156},
doi = {10.11648/j.ijcocr.20210604.12},
url = {https://doi.org/10.11648/j.ijcocr.20210604.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijcocr.20210604.12},
abstract = {Recently, the use of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) technique is widely used to detect and diagnose breast cancer. This technique has shown to be very useful particularly in screening women with high risk for breast cancer, as well as assessing the potential effects of new therapy. Thus, the aim of the present study is to appraise the efficacy of combined employment of global and local features in discriminating malignant and benign lesions. A dataset of one hundred and twenty one (121) DCE-MRI investigations was assembled and used. Out of that number, fifty (50) were biopsy-proved malignant tumors and seventy-one (71) were benign. Firstly, the suspicious mass regions were automatically detected and segmented with 3D region growing algorithm. Meanwhile, Local and global features were used. Thereafter, sequential floating forward selection method (SFFS) and support vector machine classifier (SVM) were used for classification. The overall classification performance of different kind of features were evaluated via receiver operating characteristic (ROC) analysis in a 3-fold cross validation scheme. It was observed that global feature produced classification accuracy of 84.32 % followed by local feature with accuracy of 85.95 %. When the local and global features were combined, the classification accuracy increased to 94.36 %. Based on the obtained results, this study has demonstrated that the combined use of local and global features could effectively function as a better indicator in differentiating malignant and benign tumors.},
year = {2021}
}
TY - JOUR T1 - Combined Use of Local and Global Features for Classification of Breast Lesion Using DCE-MRI Images AU - Arukalam Felicity Mmaezi AU - Okafor Sixtus Amarachukwu AU - Okafor Wilson Chimaobi AU - Eziefuna Ebere Oniyinye AU - Okafor Afoma Lorreta AU - Chibuike Tochukwu Emmanuel Y1 - 2021/10/29 PY - 2021 N1 - https://doi.org/10.11648/j.ijcocr.20210604.12 DO - 10.11648/j.ijcocr.20210604.12 T2 - International Journal of Clinical Oncology and Cancer Research JF - International Journal of Clinical Oncology and Cancer Research JO - International Journal of Clinical Oncology and Cancer Research SP - 151 EP - 156 PB - Science Publishing Group SN - 2578-9511 UR - https://doi.org/10.11648/j.ijcocr.20210604.12 AB - Recently, the use of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) technique is widely used to detect and diagnose breast cancer. This technique has shown to be very useful particularly in screening women with high risk for breast cancer, as well as assessing the potential effects of new therapy. Thus, the aim of the present study is to appraise the efficacy of combined employment of global and local features in discriminating malignant and benign lesions. A dataset of one hundred and twenty one (121) DCE-MRI investigations was assembled and used. Out of that number, fifty (50) were biopsy-proved malignant tumors and seventy-one (71) were benign. Firstly, the suspicious mass regions were automatically detected and segmented with 3D region growing algorithm. Meanwhile, Local and global features were used. Thereafter, sequential floating forward selection method (SFFS) and support vector machine classifier (SVM) were used for classification. The overall classification performance of different kind of features were evaluated via receiver operating characteristic (ROC) analysis in a 3-fold cross validation scheme. It was observed that global feature produced classification accuracy of 84.32 % followed by local feature with accuracy of 85.95 %. When the local and global features were combined, the classification accuracy increased to 94.36 %. Based on the obtained results, this study has demonstrated that the combined use of local and global features could effectively function as a better indicator in differentiating malignant and benign tumors. VL - 6 IS - 4 ER -
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