Abstract
Prostate cancer is the second most commonly occurring cancer in men. Diagnosis through Magnetic Resonance Imaging (MRI) is limited, yet current practice holds a relatively low specificity. This paper extends a previous SPIE ProstateX challenge study in three ways (1) to include healthy tissue analysis, creating a solution suitable for clinical practice, which has been requested and validated by collaborating clinicians; (2) by using a voting ensemble method to assist prostate cancer diagnosis through a supervised SVM approach; and (3) using the unsupervised GTM to provide interpretability to understand the supervised SVM classification results. Pairwise classifiers of clinically significant lesion, non-significant lesion, and healthy tissue, were developed. Results showed that when combining multiparametric MRI and patient level metadata, classification of significant lesions against healthy tissue attained an AUC of 0.869 (10-fold cross-validation).
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References
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre L, Jemal A Prostate cancer statistics. World Cancer Research Fund. https://www.wcrf.org/dietandcancer/cancer-trends/prostate-cancer-statistics
Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H (2014) Computer-aided detection of prostate cancer in MRI. IEEE Trans Med Imaging 33:1083–1092. https://doi.org/10.1109/TMI.2014.2303821
Gallagher J Prostate cancer treatment “not always needed” - BBC News. https://www.bbc.co.uk/news/health-37362572
Liu S, Zheng H, Feng Y, Li W (2017) Prostate cancer diagnosis using deep learning with 3D multiparametric MRI. Presented at the 12 March 2017
Mehrtash A, Sedghi A, Ghafoorian M, Taghipour M, Tempany CM, Wells WM, Kapur T, Mousavi P, Abolmaesumi P, Fedorov A (2017) Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks. In: Armato III SG, Petrick NA (eds.) SPIE medical imaging 2017, computer-aided diagnosis, Orlando
Kitchen A, Seah J (2017) Support vector machines for prostate lesion classification. In: Armato III SG, Petrick NA (eds.) SPIE medical imaging 2017, computer-aided diagnosis. International Society for Optics and Photonics, Orlando, p 1013427
Langer DL, van der Kwast TH, Evans AJ, Plotkin A, Trachtenberg J, Wilson BC, Haider MA (2010) Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, Ktrans, ve, and corresponding histologic features. Radiology 255:485–494. https://doi.org/10.1148/radiol.10091343
Bishop CM, Svensén M, Williams CKI (1998) GTM: The generative topographic mapping. Neural Comput 10:215–234. https://doi.org/10.1162/089976698300017953
Kohonen T (2001) Self-organizing maps. Springer, Berlin, Heidelberg
Acknowledgments
This work has been funded by the Liverpool John Moores University PhD Scholarship Fund. The authors would like to thank Andy Kitchen for his assistance in validating data extraction methods.
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Riley, P., Olier, I., Rea, M., Lisboa, P., Ortega-Martorell, S. (2020). A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-19642-4_29
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DOI: https://doi.org/10.1007/978-3-030-19642-4_29
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