Abstract
Prostate cancer (PCa) is the most frequent genre-specific malignancy and the fourth overall behind lung, breast, and colon cancers. PCa is diagnosed non-invasively with serum prostate-specific antigen assay, digital rectal examination, and trans-rectal ultrasound and invasively with multiple rectal biopsies from which a Gleason score is assigned. The biopsy tissue is subject to sampling error and cellular interpretation that in turn can lead to disagreement as to whether treatment is needed, and if so, the method and the extent of therapy. Magnetic resonance (MR) imaging is proving to be progressively useful in evaluating PCa. New sequences are continually being introduced that are proving to be even more accurate in determining the extent and degree of tumor malignancy than other imaging modalities. The MR images, however, are evaluated by radiologists whose interpretation is subjective. This study reviews the currently available artificial intelligence and machine learning techniques that may eliminate the need for multiple rectal biopsies and provide a more uniform classification of these malignancies. Also, the evaluation of treatment outcome can be better assessed with more precise tumor size and classification. This paper investigates and analyzes projects related to prostate cancer’s automatic diagnosis using artificial intelligence.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)
Cao, R., et al.: Joint prostate cancer detection and gleason score prediction in mp-MRI via focalnet. IEEE Trans. Med. Imaging 38(11), 2496–2506 (2019)
Draisma, G., et al.: Lead time and overdiagnosis in prostate-specific antigen screening: importance of methods and context. J. Natl. Cancer Inst. 101(6), 374–383 (2009). https://doi.org/10.1093/jnci/djp001
Elabbady, A., Kotb, A.F.: Unusual presentations of prostate cancer: a review and case reports. Arab J. Urol. 11(1), 48–53 (2013). https://doi.org/10.1016/j.aju.2012.10.002
Etzioni, R.: Overdiagnosis due to prostate-specific antigen screening: lessons from U.S. prostate cancer incidence trends. CancerSpectrum Knowl. Environ. 94(13), 981–990 (2002). https://doi.org/10.1093/jnci/94.13.981
Fang, F., et al.: Immediate risk of suicide and cardiovascular death after a prostate cancer diagnosis: cohort study in the United States. J. Natl Cancer Inst. 102(5), 307–314 (2010). https://doi.org/10.1093/jnci/djp537
Fehr, D.: Automatic classification of prostate cancer gleason scores from multiparametric magnetic resonance images. Proc. Natl. Acad. Sci. 112(46), E6265–E6273 (2015)
Giannini, V.: A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging. Comput. Med. Imaging Graph. 46, 219–226 (2015)
Ginsburg, S.B.: Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study. J. Magn. Reson. Imaging 46(1), 184–193 (2017)
Greer, M.D., et al.: Accuracy and agreement of PIRADSv2 for prostate cancer mpmri: a multireader study. J. Magn. Reson. Imaging 45(2), 579–585 (2017)
Kitchenham, B.: Procedures for undertaking systematic reviews: Joint technical report. http://www.inf.ufsc.br/ aldo.vw/kitchenham.pdf (2004)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
Miller, D.D., Brown, E.W.: Artificial intelligence in medical practice: the question to the answer? Am. J. Med. 131(2), 129–133 (2018). https://doi.org/10.1016/j.amjmed.2017.10.035
Nguyen, T.H., et al.: Automatic gleason grading of prostate cancer using quantitative phase imaging and machine learning. J. Biomed. Opt. 22(3), 036015 (2017)
Peyret, R., Khelifi, F., Bouridane, A., Al-Maadeed, S.: Automatic diagnosis of prostate cancer using multispectral based linear binary pattern bagged codebooks. In: 2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1–4. IEEE (2017)
Quinn, M., Babb, P.: Patterns and trends in prostate cancer incidence, survival, prevalence and mortality. Part I: international comparisons. BJU Int. 90(2), 162–173 (2002). https://doi.org/10.1046/j.1464-410X.2002.2822.x
Reda, I., et al.: Computer-aided diagnostic tool for early detection of prostate cancer. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2668–2672. IEEE (2016)
Siegel, R.L., Miller, K.D.: (2018) cancer statistics. CA Cancer J. Clin. 68(1), 7–30 (2018)
Singh, H., et al.: Overdiagnosis: causes and consequences in primary health care. Can. Fam. Physician 64(9), 654–659 (2018)
Villers, A., Grosclaude, P.: Épidémiologie du cancer de la prostate. Article de revue. Medecine Nucleaire 32(1), 2–4 (2008). https://doi.org/10.1016/j.mednuc.2007.11.003
Vegega, C., Pytel, P., Pollo-Cattaneo, M.F.: Evaluation of the bias in the management of patient’s appointments in a pediatric office. ParadigmPlus 1(1), 1–21 (2020)
Wang, J., Wu, C.J., Bao, M.L., Zhang, J., Wang, X.N., Zhang, Y.D.: Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur. Radiol. 27(10), 4082–4090 (2017). https://doi.org/10.1007/s00330-017-4800-5
Yepes-Calderon, F., Nelson, M.D., McComb, J.G.: Automatically measuring brain ventricular volume within PACS using artificial intelligence. PLOS ONE 13(3), 1–14 (2018). https://doi.org/10.1371/journal.pone.0193152
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Espinosa, C., Garcia, M., Yepes-Calderon, F., McComb, J.G., Florez, H. (2020). Prostate Cancer Diagnosis Automation Using Supervised Artificial Intelligence. A Systematic Literature Review. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-61702-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61701-1
Online ISBN: 978-3-030-61702-8
eBook Packages: Computer ScienceComputer Science (R0)