Abstract
Lung cancer is the type of cancer that most makes victims around the world and often presents a late diagnosis. Computed tomography (CT) is currently the reference imaging test for the diagnosis and staging of lung tumors. Recent studies have shown relevance in the characterization of lung tumors by different sequences obtained with magnetic resonance imaging (MRI). MRI also has the advantage of not exposing the patient to ionizing radiation, as occurs in CT scans. This paper presents an investigation about the applicability of pattern recognition methods to computer-aided diagnosis of lung cancer in MRI exams. A set of 21 T1-weighted contrast-enhanced MR images associated with lung lesions (14 malignant and 7 benign) was retrospectively constructed and semi-automatically segmented. Quantitative features were obtained from tumor 2D and 3D segmentation, totaling 150 features. Unbalancing problems were solved synthetically oversampling the dataset. Tumor classification was based on five machine learning classifiers and leave-one-out cross-validation. Relevant feature selection was performed for all classifiers. Results showed significant performance on balanced dataset, presenting area under the receiver operating characteristic (ROC) curve of 0.885 during the validation, and 0.938 during the test process. The investigated approach demonstrates potential for computer-aided diagnosis of lung cancer in MRI.
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References
Hollings, N., Shaw, P.: Diagnostic imaging of lung cancer. Eur. Respir. J. 19(4), 722–742 (2002)
Santos, M.K., Muley, T., Warth, A., de Paula, W.D., Lederlin, M., Schnabel, P.A., Schlemmer, H.P., Kauczor, H.U., Heussel, C.P., Puderbach, M.: Morphological computed tomography features of surgically resectable pulmonary squamous cell carcinomas: impact on prognosis and comparison with adenocarcinomas. Eur. J. Radiol. 83(7), 1275–1281 (2014)
Ferreira, J.R., Oliveira, M.C., de Azevedo-Marques, P.M.: Characterization of pulmonary nodules based on features of margin sharpness and texture. J. Digit. Imaging, 1–13 (2017)
Junior, J.R.F., Koenigkam-Santos, M., Cipriano, F.E.G., Fabro, A.T., de Azevedo-Marques, P.M.: Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput. Methods Programs Biomed. 159, 23–30 (2018)
Coolen, J., Vansteenkiste, J., De Keyzer, F., Decaluwé, H., De Wever, W., Deroose, C., Dooms, C., Verbeken, E., De Leyn, P., Vandecaveye, V., Van Raemdonck, D.: Characterisation of solitary pulmonary lesions combining visual perfusion and quantitative diffusion MR imaging. Eur. Radiol. 24(2), 531–541 (2014)
Koenigkam-Santos, M., Optazaite, E., Sommer, G., Safi, S., Heussel, C.P., Kauczor, H., Puderbach, M.: Contrast-enhanced magnetic resonance imaging of pulmonary lesions: description of a technique aiming clinical practice. Eur. J. Radiol. 84(1), 185–192 (2015)
Zhu, L., Kolesov, I., Gao, Y. Kikinis, R., Tannenbaum, A.: An effective interactive medical image segmentation method using fast growcut. In: MICCAI Workshop on Interactive Medical Image Computing (2014)
Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., Buatti, J.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323–1341 (2012)
Lorensen, W.E., Cline. H. E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph Comput. Graph. 21(4), 163–169 (1987). ACM New York, NY, USA
Griethuysen, J.J.M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R.G.H., Fillon-Robin, J.C., Pieper, S., Aerts, H.J.W.L.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104–e107 (2017). https://doi.org/10.1158/0008-5472.CAN-17-0339
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst., Man Cybern. 6, 610–621 (1973)
Tang, X.: Texture information in run-length matrices. IEEE Trans. Image Process. 7(11), 1602–1609 (1998)
Thibault, G., Fertil, B., Navarro, C., Pereira, S., Cau, P., Levy, N., Sequeira, J., Mari, J.L.: Texture indexes and gray level size zone matrix: application to cell nuclei classification. In: 10th International Conference on Pattern Recognition and Information Processing, pp. 140–145 (2009)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Frank, E., Hall, M., Witten. I.: The WEKA workbench. Online Appendix for: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann (2016)
Kira, K., Rendell, L.: A practical approach to feature selection. In: Machine Learning Proceedings, pp. 249–256 (1992)
Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: European Conference on Machine Learning, pp. 171–182 (1994)
Kohavi, R., John, G.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)
Acknowledgements
This work was funded by São Paulo Research Foundation (FAPESP, grant number 2016/17078-0), in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001, and National Council for Scientific and Technological Development (CNPq).
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Francisco, V. et al. (2019). Computer-Aided Diagnosis of Lung Cancer in Magnetic Resonance Imaging Exams. In: Costa-Felix, R., Machado, J., Alvarenga, A. (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings, vol 70/2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2517-5_19
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DOI: https://doi.org/10.1007/978-981-13-2517-5_19
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