Can the Physical Parameters with the Support Vector Machine (SVM) Method Able to Classify Benign and Malignant Breast Cancer?
Asian Journal of Research in Computer Science, Volume 14, Issue 4,
Page 8-20
DOI:
10.9734/ajrcos/2022/v14i4288
Abstract
Objective: Evaluating the diagnostic performance of SVM to classify benign and malignant by performing a meta-analysis.
Methods: The data used for this study were secondary data. It consisted of 221 mammogram images (mean age 57.5 years) with 164 malignant and 57 benign, taken from a radiological database that has been examined by a radiologist with more than 20 years of experience. Also, histopathological record data that had been examined by an oncologist with more than 20 years of experience. Mammograms were taken from January 2022 to June 2022. In all, 221 mammograms consisting of 164 malignant and 57 benign were used as SVM method training, and 20 mammograms consisting of 10 malignant and 10 benign were used to test the performance of the SVM method. It was then evaluated using pathology results as the gold standard.
Results: Benign had a significantly lower deviation (an average of 29.2661230 ± 10.14916673) than malignant (an average of 33.1841234 ± 11.70238757). The SVM method performance value obtained the values of TP, FP, TN, FN, accuracy, sensitivity, Specificity, and Precision, respectively 7,7, 3, 3, 50%, 70%, 30%, and 50%.
Conclusion: A proper performance to distinguish benign and malignant can be obtained using the physical deviation parameters with the SVM classification approach. However, these findings should be proven in larger datasets with different mammographic scanners. Our meta-analysis shows that the physical parameters and SVM have high sensitivity but low specificity. Of the nine physical parameters in the mammogram, only the parameter deviation was significant to distinguish between benign and malignant. The SVM method proved to be able to differentiate between benign and malignant.
- Mammography
- benign
- malignant
- SVM
- physical parameters
How to Cite
References
Khamis MEM, Alaa El-deen AM, Azim Ismail AA. The diagnostic value of sonoelastographic strain ratio in discriminating malignant from benign solid breast masses. Egypt J Rad Nucl Med. 2017;48(4):1149-57.
DOI: 10.1016/j.ejrnm.2017.05.005
Gunawan AAN, Poniman S, Supardi IW. Classification of breast cancer grades using physical parameters and K-nearest neighbor method. Telkomnika. 2019;17(2): 637-44.
DOI: 10.12928/TELKOMNIKA.V17I2.9797
Awad FM. Role of supersonic shear wave imaging quantitative elastography (SSI) in differentiating benign and malignant solid breast masses. Egypt J Rad Nucl Med. 2013;44(3):681-5.
DOI: 10.1016/j.ejrnm.2013.04.001
Abd El-Aleem RA, Abo El-Hamd EA, Yousef HA, Radwan MEM, Mohammed RAA. The added value of qualitative and quantitative diffusion-weighted magnetic resonance imaging (DW-MRI) in differentiating benign from malignant breast lesions. Egypt J Rad Nucl Med. 2018;49(1):272-80.
DOI: 10.1016/j.ejrnm.2017.10.015
Mus RD, Borelli C, Bult P, Weiland E, Karssemeijer N, Barentsz JO, et al. Time to enhancement derived from ultrafast breast MRI as a novel parameter to discriminate benign from malignant breast lesions. Eur J Radiol. 2017;89:90-6.
DOI: 10.1016/j.ejrad.2017.01.020, PMID 28267555.
Li J, Guo L, Yin L, Fang H, Ye W, Zhao B et al. Can different regions of interest influence the diagnosis of benign and malignant breast lesions using quantitative parameters of contrast-enhanced sonography? Eur J Radiol. 2018;108:1-6.
DOI: 10.1016/j.ejrad.2018.09.005, PMID 30396640.
Forte S, Wang Z, Arboleda C, Lång K, Singer G, Kubik-Huch RA, et al. Can grating interferometry-based mammography discriminate benign from malignant microcalcifications in fresh biopsy samples? Eur J Radiol. 2020; 129:109077.
DOI: 10.1016/j.ejrad.2020.109077, PMID 32446126.
Ma W, Mao J, Wang T, Huang Y, Zhao ZH. Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis. Eur J Radiol. 2021; 141:109809.
DOI: 10.1016/j.ejrad.2021.109809, PMID 34116452.
Demirler Şimşir B, Krug KB, Burke C, Hellmich M, Maintz D, Coche E. Possibility to discriminate benign from malignant breast lesions detected on dual-layer spectral CT-evaluation. Eur J Radiol. 2021;142:109832.
DOI: 10.1016/j.ejrad.2021.109832, PMID 34246013.
Ma D, Lu F, Zou X, Zhang H, Li Y, Zhang L et al. Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions. Magn Reson Imaging. 2017; 36:175-9.
DOI: 10.1016/j.mri.2016.10.005, PMID 27742437.
Kanao S, Kataoka M, Iima M, Ikeda DM, Toi M, Togashi K. Differentiating benign and malignant inflammatory breast lesions: value of T2 weighted and diffusion weighted MR images. Magn Reson Imaging. 2018;50:38-44.
DOI: 10.1016/j.mri.2018.03.012, PMID 29545213.
Fusco R, Granata V, Pariante P, Cerciello V, Siani C, Di Bonito M et al. Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination. Magn Reson Imaging. 2021;75:51-9.
DOI: 10.1016/j.mri.2020.10.008, PMID 33080334.
Zhang Q, Spincemaille P, Drotman M, Chen C, Eskreis-Winkler S, Huang W et al. Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics. Magn Reson Imaging. 2022;86:86-93.
DOI: 10.1016/j.mri.2021.10.039, PMID 34748928.
Fuller AM, Olsson LT, Midkiff BR, Kirk EL, McNaughton KK, Calhoun BC, et al. Vascular density of histologically benign breast tissue from women with breast cancer: associations with tissue composition and tumor characteristics. Hum Pathol. 2019;91:43-51.
DOI: 10.1016/j.humpath.2019.06.003, PMID 31271812.
Han BW, Cai GX, Liu Q, Yang X, Guo ZW, Huang LM, et al. Noninvasive discrimination of benign and malignant breast lesions using genome-wide nucleosome profiles of plasma cell-free DNA. Clin Chim Acta. 2021;520:95-100.
DOI: 10.1016/j.cca.2021.06.008, PMID 34107314.
Papachristopoulou G, Tsapralis N, Michaelidou K, Ardavanis-Loukeris G, Griniatsos I, Scorilas A, et al. Human kallikrein-related peptidase 12 (KLK12) splice variants discriminate benign from cancerous breast tumors. Clin Biochem. 2018;58:78-85.
DOI: 10.1016/j.clinbiochem.2018.05.017, PMID 29807016.
Wubulihasimu M, Maimaitusun M, Xu XL, Liu XD, Luo BM. The added value of contrast-enhanced ultrasound to conventional ultrasound in differentiating benign and malignant solid breast lesions: A systematic review and meta-analysis. Clin Radiol. 2018;73(11):936-43.
DOI: 10.1016/j.crad.2018.06.004, PMID 30297035.
Chen BY, Xie Z, Nie P, Yang D, Hu YC, Liu ST et al. Multiple b-value diffusion-weighted imaging in differentiating benign from malignant breast lesions: Comparison of conventional mono-, bi- and stretched exponential models. Clin Radiol. 2020; 75(8):642.e1-8.
DOI: 10.1016/j.crad.2020.03.039, PMID 32389372.
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