Abstract
Prostate cancer is the most common cancer in men in the UK. An accurate diagnosis at the earliest stage possible is critical in its treatment. Multi-parametric Magnetic Resonance Imaging is gaining popularity in prostate cancer diagnosis, it can be used to actively monitor low-risk patients, and it is convenient due to its non-invasive nature. However, it requires specialist knowledge to review the abundance of available data, which has motivated the use of machine learning techniques to speed up the analysis of these many and complex images. This paper focuses on assessing the capabilities of two neural network approaches to accurately discriminate between three tissue types: significant prostate cancer lesions, non-significant lesions, and healthy tissue. For this, we used data from a previous SPIE ProstateX challenge that included significant and non-significant lesions, and we extended the dataset to include healthy prostate tissue due to clinical interest. Feed-Forward and Convolutional Neural Networks have been used, and their performances were evaluated using 80/20 training/test splits. Several combinations of the data were tested under different conditions and summarised results are presented. Using all available imaging data, a Convolutional Neural Network three-class classifier comparing prostate lesions and healthy tissue attains an Area Under the Curve of 0.892.
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References
Prostate cancer screening scan hope - BBC News
An, J.Y., Sidana, A., Choyke, P.L., Wood, B.J., Pinto, P.A., Türkbey, İ.B.: Multiparametric magnetic resonance imaging for active surveillance of prostate cancer. Balkan Med. J. 34, 388–396 (2017). https://doi.org/10.4274/balkanmedj.2017.0708
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943). https://doi.org/10.1007/BF02478259
Srivastava, N., Hinton, G., Krizhevsky, A., Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (2014)
Riley, P., Olier, I., Rea, M., Lisboa, P., Ortega-Martorell, S.: A voting ensemble method to assist the diagnosis of prostate cancer using multiparametric MRI. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J.D. (eds.) WSOM 2019. AISC, vol. 976, pp. 294–303. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-19642-4_29
Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: Computer-aided detection of prostate cancer in MRI. IEEE Trans. Med. Imaging 33, 1083–1092 (2014). https://doi.org/10.1109/TMI.2014.2303821
Gallagher, J.: Prostate cancer treatment “not always needed” - BBC News/ Health (2016). https://www.bbc.co.uk/news/health-37362572
Chen, Q., Xu, X., Hu, S., Li, X., Zou, Q., Li, Y.: A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans. In: Armato III, S.G., Petrick, N.A. (eds.) SPIE Medical Imaging 2017: Computer-Aided Diagnosis, p. 101344F. International Society for Optics and Photonics, Orlando (2017)
Kitchen, A., Seah, J.: Support vector machines for prostate lesion classification. In: Armato III, S.G., Petrick, N.A. (eds.) SPIE Medical Imaging 2017: Computer-Aided Diagnosis, p. 1013427. International Society for Optics and Photonics, Orlando (2017)
Seah, J.C.Y., Tang, J.S.N., Kitchen, A.: Detection of prostate cancer on multiparametric MRI. In: Armato, S.G., Petrick, N.A. (eds.) SPIE Medical Imaging 2017: Computer-Aided Diagnosis. p. 1013429. International Society for Optics and Photonics (2017)
Langer, D.L., et al.: Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, Ktrans, ve, and corresponding histologic features. Radiology 255, 485–494 (2010). https://doi.org/10.1148/radiol.10091343
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This work has been funded by the LJMU Scholarship Fund.
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Marnell, S., Riley, P., Olier, I., Rea, M., Ortega-Martorell, S. (2019). A Comparative Assessment of Feed-Forward and Convolutional Neural Networks for the Classification of Prostate Lesions. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_15
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