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Automated Diagnosis of Prostate Cancer Using Artificial Intelligence. A Systematic Literature Review

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Applied Informatics (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1874))

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Abstract

Prostate cancer is one of the most preventable causes of death. Periodic testing, seconded by precursors such as living habits, heritage, and exposure to specific materials, help healthcare providers achieve early detection, a desirable scenario that positively correlates with survival. However, the currently available diagnosing mechanisms have a great opportunity of improvement in terms of invasiveness, sensitivity and timing before patients reach advanced stages with a significant probability of metastasis. Supervised artificial intelligence enables early diagnosis and excludes patients from unpleasant biopsies. In this work, we gathered information about methodologies, techniques, metrics, and benchmarks to accomplish early prostate cancer detection, including pipelines with associated patents and knowledge transfer mechanisms, intending to find the reasons precluding the solutions from being massively adopted in the standards of care.

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References

  1. Ferlay, J., Ervik, M., et al.: Global cancer observatory: cancer today. International Agency for Research on Cancer, Lyon (2020)

    Google Scholar 

  2. Elmore, S.: Apoptosis: a review of programmed cell death. Toxicol. Pathol. 35(4), 495–516 (2007)

    Article  Google Scholar 

  3. World Cancer Research Fund International: Prostate cancer statistics. Cancer Trends, Prostate cancer statistics (2023)

    Google Scholar 

  4. World Health Organization: Cancer. World Health Organization Fact Sheet, Detail, Cancer (2022)

    Google Scholar 

  5. Prostate Cancer Foundation. About prostate cancer. About Prostate Cancer (2023)

    Google Scholar 

  6. Urology Care Foundation. Prostate cancer-early-stage. Urology Health Organization (2023)

    Google Scholar 

  7. Carter, H.B., Albertsen, P.C., Barry, M.J., et al.: Early detection of prostate cancer: AUA guideline. J. Urol. 190, 419 (2013)

    Article  Google Scholar 

  8. Filella, X., et al.: Prostate cancer screening: guidelines review and laboratory issues. Clin. Chem. Lab. Med. (CCLM) 57(10), 1474–1487 (2019)

    Article  Google Scholar 

  9. The American Cancer Society medical and editorial content team. Prostate cancer early detection, diagnosis, and staging. Cancer A-Z, Prostate Cancer, p. 10 (2019)

    Google Scholar 

  10. Humphrey, P.A.: Histopathology of prostate cancer. Cold Spring Harbor Perspect. Med. 7(10), a030411 (2017)

    Article  Google Scholar 

  11. The American Cancer Society medical and editorial content team. Prostate cancer early detection, diagnosis, and staging. Cancer A-Z, Prostate Cancer, p. 24:25 (2019)

    Google Scholar 

  12. Weinreb, J.C., et al.: PI-RADS prostate imaging-reporting and data system: 2015, version 2. Eur. Urol. 69(1), 16–40 (2016)

    Article  Google Scholar 

  13. The American Cancer Society medical and editorial content team. Prostate cancer early detection, diagnosis, and staging. Cancer A-Z, Prostate Cancer, p. 26 (2019)

    Google Scholar 

  14. Herman, G.T.: Fundamentals of Computerized Tomography: Image Reconstruction from Projections. Springer, Heidelberg (2009). https://doi.org/10.1007/978-1-84628-723-7

    Book  Google Scholar 

  15. The American Cancer Society medical and editorial content team. Prostate cancer early detection, diagnosis, and staging. Cancer A-Z, Prostate Cancer, p. 23 (2019)

    Google Scholar 

  16. National Cancer Institute. NCI dictionary of cancer terms. NCI Dictionary of Cancer Terms, p. G (2023)

    Google Scholar 

  17. Wei, Yu., Zhou, L.: Early diagnosis of prostate cancer from the perspective of Chinese physicians. J. Cancer 11(11), 3264 (2020)

    Article  Google Scholar 

  18. Gurina, T.S., Simms, L.: Histology, Staining. SataPearls Publishing-Europe PMC (2020)

    Google Scholar 

  19. Kitchenham, B.A., Dyba, T., Jorgensen, M.: Evidence-based software engineering. In: Proceedings of the 26th International Conference on Software Engineering, pp. 273–281. IEEE (2004)

    Google Scholar 

  20. Wieringa, R., Maiden, N., Mead, N., Rolland, C.: Requirements engineering paper classification and evaluation criteria: a proposal and a discussion. Requirements Eng. 11(1), 102–107 (2006)

    Article  Google Scholar 

  21. Salvador, S., Florencia, P.-C., Fernando, Y.C.: Automated diagnosis of prostate cancer using artificial intelligence. A systematic literature review. Extraction and Summary Forms (2023)

    Google Scholar 

  22. Bourbonne, V., et al.: External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer. Cancers 12(4), 814 (2020)

    Article  Google Scholar 

  23. de Vente, C., Vos, P., Hosseinzadeh, M., Pluim, J., Veta, M.: Deep learning regression for prostate cancer detection and grading in bi-parametric MRI. IEEE Trans. Biomed. Eng. 68(2), 374–383 (2021)

    Article  Google Scholar 

  24. Javadi, G., et al.: Characterizing the uncertainty of label noise in systematic ultrasound-guided prostate biopsy. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 424–428. IEEE (2021)

    Google Scholar 

  25. Liu, Q., Dou, Q., Yu, L., Heng, P.A.: MS-net: multi-site network for improving prostate segmentation with heterogeneous MRI data. IEEE Trans. Med. Imaging 39(9), 2713–2724 (2020)

    Article  Google Scholar 

  26. Jia, H., et al.: 3D APA-net: 3D adversarial pyramid anisotropic convolutional network for prostate segmentation in MR images. IEEE Trans. Med. Imaging 39(2), 447–457 (2019)

    Article  Google Scholar 

  27. Schömig-Markiefka, B., et al.: Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod. Pathol. 34(12), 2098–2108 (2021)

    Article  Google Scholar 

  28. Comelli, A., et al.: Deep learning-based methods for prostate segmentation in magnetic resonance imaging. Appl. Sci. 11(2), 782 (2021)

    Article  Google Scholar 

  29. Tian, Z., et al.: Graph-convolutional-network-based interactive prostate segmentation in MR images. Med. Phys. 47(9), 4164–4176 (2020)

    Article  Google Scholar 

  30. Karimi, D., Nir, G., Fazli, L., Black, P.C., Goldenberg, L., Salcudean, S.E.: Deep learning-based Gleason grading of prostate cancer from histopathology images-role of multiscale decision aggregation and data augmentation. IEEE J. Biomed. Health Inform. 24(5), 1413–1426 (2019)

    Article  Google Scholar 

  31. Arif, M., et al.: Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI. Eur. Radiol. 30(12), 6582–6592 (2020)

    Article  Google Scholar 

  32. Shirabad, J.S., Menzies, T.J.: The PROMISE repository of software engineering databases. School of Information Technology and Engineering, University of Ottawa, Canada (2005)

    Google Scholar 

  33. He, K., et al.: MetricUNet: synergistic image-and voxel-level learning for precise prostate segmentation via online sampling. Med. Image Anal. 71, 102039 (2021)

    Article  Google Scholar 

  34. Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: Cancer imaging archive wiki (2017). https://doi.org/10.7937/K9TCIA

  35. Giger, M., Drukker, K.: SPIE-AAPM-NCI PROSTATE MR Gleason grade group challenge PROSTATEx-2: performance evaluation. American Association of Physicist in Medicine (2017)

    Google Scholar 

  36. Duran-Lopez, L., Dominguez-Morales, J.P., Conde-Martin, A.F., Vicente-Diaz, S., Linares-Barranco, A.: PROMETEO: a CNN-based computer-aided diagnosis system for WSI prostate cancer detection. IEEE Access 8, 128613–128628 (2020)

    Article  Google Scholar 

  37. Iqbal, S., et al.: Prostate cancer detection using deep learning and traditional techniques. IEEE Access 9, 27085–27100 (2021)

    Article  Google Scholar 

  38. Annas, G.J.: HIPAA regulations-a new era of medical-record privacy? (2003)

    Google Scholar 

  39. Yepes Calderon, F., Rea, N., McComb, J.G.: Enabling the medical applications engine. In: Florez, H., Diaz, C., Chavarriaga, J. (eds.) ICAI 2018. CCIS, vol. 942, pp. 131–143. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01535-0_10

    Chapter  Google Scholar 

  40. Calderon, F.Y., McComb, J.G.: Enabling the centralization of medical derived data for artificial intelligence implementations. Technical report Patent No. US20200273551A1, Children Hospital Los Angeles (2020)

    Google Scholar 

  41. Espinosa, C., Garcia, M., Yepes-Calderon, F., McComb, J.G., Florez, H.: Prostate cancer diagnosis automation using supervised artificial intelligence. A systematic literature review. In: Florez, H., Misra, S. (eds.) ICAI 2020. CCIS, vol. 1277, pp. 104–115. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61702-8_8

    Chapter  Google Scholar 

  42. Yepes-Calderon, F., et al.: EdgeRunner: a novel shape-based pipeline for tumours analysis and characterisation. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 6(1), 84–92 (2018)

    Google Scholar 

  43. Yepes-Calderón, F., Medina, F.M., Rea, N.D., Abella, J.: Tumor malignancy characterization in clinical environments: an approach using the FYC-index of spiculation and artificial intelligence. In: Tumor Progression and Metastasis. IntechOpen (2018)

    Google Scholar 

  44. Yepes-C, F., et al.: The 3D edgerunner pipeline: a novel shape-based analysis for neoplasms characterization. In: Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 9788, pp. 681–685. SPIE (2016)

    Google Scholar 

  45. Matoso, A., Epstein, J.I.: Defining clinically significant prostate cancer on the basis of pathological findings. Histopathology 74(1), 135–145 (2019)

    Article  Google Scholar 

  46. Silva-Rodríguez, J., Colomer, A., Naranjo, V.: WeGleNet: a weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images. Comput. Med. Imaging Graph. 88, 101846 (2021)

    Article  Google Scholar 

  47. Chen, C.-M., Huang, Y.-S., Fang, P.-W., Liang, C.-W., Chang, R.-F.: A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional densenet. Med. Phys. 47(3), 1021–1033 (2020)

    Article  Google Scholar 

  48. Koziarski, M., et al.: DiagSet: a dataset for prostate cancer histopathological image classification. arXiv preprint arXiv:2105.04014 (2021)

  49. Bhattacharjee, S., et al.: Cluster analysis of cell nuclei in H &E-stained histological sections of prostate cancer and classification based on traditional and modern artificial intelligence techniques. Diagnostics 12(1), 15 (2021)

    Article  MathSciNet  Google Scholar 

  50. Kalapahar, A., Silva-Rodríguez, J., Colomer, A., López-Mir, F., Naranjo, V.: Gleason grading of histology prostate images through semantic segmentation via residual U-net. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2501–2505. IEEE (2020)

    Google Scholar 

  51. Pinckaers, H., Bulten, W., van der Laak, J., Litjens, G.: Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels. IEEE Trans. Med. Imaging 40(7), 1817–1826 (2021)

    Article  Google Scholar 

  52. Wang, J., Chen, R.J., Lu, M.Y., Baras, A., Mahmood, F.: Weakly supervised prostate TMA classification via graph convolutional networks. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 239–243. IEEE (2020)

    Google Scholar 

  53. To, M.N.N., et al.: Deep learning framework for epithelium density estimation in prostate multi-parametric magnetic resonance imaging. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 438–441. IEEE (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universidad Tecnologica Nacional Regional Buenos Aires, Grupo Gemis, Buenos Aires, Argentina

    Salvador Soto & María F. Pollo-Cattaneo

  2. SBP LLC - RnD Department, 604, Fort Pierce, Fl, 34950, USA

    Fernando Yepes-Calderon

  3. GYM Group SA - Departamento I+R, Cra 78A No. 6-58, Cali, Colombia

    Fernando Yepes-Calderon

Authors
  1. Salvador Soto
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  2. María F. Pollo-Cattaneo
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  3. Fernando Yepes-Calderon
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Corresponding authors

Correspondence to Salvador Soto , María F. Pollo-Cattaneo or Fernando Yepes-Calderon .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Universidad Ecotec, Guayaquil, Ecuador

    Marcelo Leon

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Soto, S., Pollo-Cattaneo, M.F., Yepes-Calderon, F. (2024). Automated Diagnosis of Prostate Cancer Using Artificial Intelligence. A Systematic Literature Review. In: Florez, H., Leon, M. (eds) Applied Informatics. ICAI 2023. Communications in Computer and Information Science, vol 1874. Springer, Cham. https://doi.org/10.1007/978-3-031-46813-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-46813-1_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-46813-1

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