SpringerLink
Log in

A Review on Prediction and Prognosis of the Prostate Cancer and Gleason Grading of Prostatic Carcinoma Using Deep Transfer Learning Based Approaches

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Prostate cancer is a dangerous type of cancer that kills a lot of men because it is hard to diagnose. Images taken of people with carcinoma have complex and important parts that are hard to get out with traditional diagnostic methods. Deep learning (DL) can classify the aggressiveness of prostate cancer by automatically extracting characteristics from whole-slide images of prostate biopsies that have been annotated by skilled pathologists. This study uses transfer learning to create resilient DL convolutional neural networks. A technique of risk assessment for prostate cancer called Gleason grading is based on the pathologist who reports the results and is vulnerable to bias. Systems that use DL have the potential to improve the efficiency and objectivity of Gleason grading. Based on a sizable, high-quality training dataset, a cutting-edge convolutional network architecture, and an extensive training set, we developed DL-based models for identifying prostate cancer tissue in whole-slide images (MobileNet V2, InceptionResNet V2, DenseNet 169, ResNet101 V2, and NasNetMobile). Accuracy, loss, and RMSE measurements were used in a confusion matrix to evaluate performance. DenseNet 169 provided the best results, with validation accuracy of 89.76% (ISUP Grade 0), training accuracy of 95.63% (ISUP Grade 1), validation accuracy of 96.98% (ISUP Grade 2), validation accuracy of 91.98% (ISUP Grade 3), and training accuracy of 95.63% (ISUP Grade 5). InceptionResNet V2 has obtained the highest average accuracy (validation), 84.99%. The results demonstrate that InceptionResNet V2 performed better than other models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Data Availability

Not applicable.

Code Availability

Not applicable.

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 68(6):394–424

    Article  Google Scholar 

  2. Sousa AP, Costa R, Alves MG, Soares R, Baylina P, Fernandes R (2022) The impact of metabolic syndrome and type 2 diabetes mellitus on prostate cancer. Front Cell Dev Biol. https://doi.org/10.3389/fcell.2022.843458

    Article  Google Scholar 

  3. Adediran AO, Olatunbosun EI (2020) A descriptive study of prostate lesions in largest hospital In Ondo State of Nigeria. Global J Health Sci 5(2):18–39

    Google Scholar 

  4. Fossati N, Giannarini G, Joniau S, Sedelaar M, Sooriakumaran P, Spahn M, Rouprêt M (2020) Newly diagnosed oligometastatic prostate cancer: current controversies and future developments. Eur Urol Oncol. https://doi.org/10.1016/j.euo.2020.11.001

    Article  Google Scholar 

  5. Bleyer A, Spreafico F, Barr R (2020) Prostate cancer in young men: An emerging young adult and older adolescent challenge. Cancer 126(1):46–57

    Article  Google Scholar 

  6. Wrzosek M, Woźniak J, Włodarek D (2020) The causes of adverse changes of testosterone levels in men. Expert Rev Endocrinol Metab 15(5):355–362

    Article  Google Scholar 

  7. Zhang YY, Li Q, **n Y, Lv WQ (2018) Differentiating prostate cancer from benign prostatic hyperplasia using PSAD based on machine learning: Single-center retrospective study in China. IEEE/ACM Trans Comput Biol Bioinf 16(3):936–941

    Article  Google Scholar 

  8. Nagpal K, Foote D, Tan F, Liu Y, Chen PHC, Steiner DF et al (2020) Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens. JAMA Oncol 6(9):1372–1380

    Article  Google Scholar 

  9. Cai CJ, Winter S, Steiner D, Wilcox L, Terry M (2019) “ Hello AI”: uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making. Proc ACM Human-Comput Interact 3((CSCW)):1–24

    Article  Google Scholar 

  10. Chen Q, Xu X, Hu S, Li X, Zou Q, Li Y (2017) March). A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans. Medical imaging 2017: Computer-aided diagnosis, vol 10134. SPIE, Bellingham, pp 1154–1157

    Google Scholar 

  11. Yuan Y, Qin W, Buyyounouski M, Ibragimov B, Hancock S, Han B, **ng L (2019) Prostate cancer classification with multiparametric MRI transfer learning model. Med Phys 46(2):756–765

    Article  Google Scholar 

  12. Şerbănescu MS, Oancea CN, Streba CT, Pleşea IE, Pirici D, Streba L, Pleşea RM (2020) Agreement of two pre-trained deep-learning neural networks built with transfer learning with six pathologists on 6000 patches of prostate cancer from Gleason 2019 Challenge. Rom J Morphol Embryol 61(2):513

    Article  Google Scholar 

  13. Song Y, Zhang YD, Yan X, Liu H, Zhou M, Hu B, Yang G (2018) Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J Magn Reson Imaging 48(6):1570–1577

    Article  Google Scholar 

  14. Abbasi AA, Hussain L, Awan IA, Abbasi I, Majid A, Nadeem MSA, Chaudhary QA (2020) Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cogn Neurodyn 14(4):523–533

    Article  Google Scholar 

  15. Kim, H. G., Choi, Y., & Ro, Y. M. (2017). Modality-bridge transfer learning for medical image classification. In 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1–5). IEEE.

  16. Clark T, Zhang J, Baig S, Wong A, Haider MA, Khalvati F (2017) Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks. J Med Imaging 4(4):041307

    Article  Google Scholar 

  17. Sharifi-Noghabi H, Liu Y, Erho N, Shrestha R, Alshalalfa M, Davicioni E et al (2019) Deep genomic signature for early metastasis prediction in prostate cancer. BioRxiv 4:276055

    Google Scholar 

  18. Karimi D, Nir G, Fazli L, Black PC, Goldenberg L, Salcudean SE (2019) 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

    Article  Google Scholar 

  19. Abraham B, Nair MS (2019) Computer-aided grading of prostate cancer from MRI images using convolutional neural networks. Journal of Intelligent & Fuzzy Systems 36(3):2015–2024

    Article  Google Scholar 

  20. Xu H, Park S, Hwang TH (2019) Computerized classification of prostate cancer gleason scores from whole slide images. IEEE/ACM Trans Comput Biol Bioinf 17(6):1871–1882

    Article  Google Scholar 

  21. Reda, I., Ghazal, M., Shalaby, A., Elmogy, M., Aboulfotouh, A., Abou El-Ghar, M., ... & El-Baz, A. (2019, September). Detecting and localizing prostate cancer from diffusion-weighted magnetic resonance imaging. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 1405–1409). IEEE.

  22. Tolkach Y, Dohmgörgen T, Toma M, Kristiansen G (2020) High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell 2(7):411–418

    Article  Google Scholar 

  23. Elmahdy, M. S., Ahuja, T., van der Heide, U. A., & Staring, M. (2020, April). Patient-specific finetuning of deep learning models for adaptive radiotherapy in prostate CT. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 577–580). IEEE.

  24. Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B et al (2020) Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol 21(2):233–241

    Article  Google Scholar 

  25. Egevad L, Swanberg D, Delahunt B, Ström P, Kartasalo K, Olsson H et al (2020) Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading. Virchows Arch 477(6):777–786

    Article  Google Scholar 

  26. Otálora S, Marini N, Müller H, Atzori M (2021) Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification. BMC Med Imaging 21(1):1–14

    Article  Google Scholar 

  27. SAKK CC, B., & IBCSG, B. Protocol SAKK 09/10 Dose intensified salvage radiotherapy in biochemically relapsed prostate cancer without macroscopic disease. A randomized phase III trial.

  28. Yang B, **ao Z (2021) A multi-channel and multi-spatial attention convolutional neural network for prostate cancer ISUP grading. Appl Sci 11(10):4321

    Article  Google Scholar 

  29. Mandal S, Roy D, Das S (2021) Prostate cancer: Cancer detection and classification using deep learning. advanced machine learning approaches in cancer prognosis. Springer, Cham, pp 375–394

    Chapter  Google Scholar 

  30. Ström P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM et al (2020) Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol 21(2):222–232

    Article  Google Scholar 

  31. Kazemifar S, Balagopal A, Nguyen D, McGuire S, Hannan R, Jiang S, Owrangi A (2018) Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning. Biomed Phys Eng Express 4(5):055003

    Article  Google Scholar 

  32. Hoar D, Lee PQ, Guida A, Patterson S, Bowen CV, Merrimen J et al (2021) Combined transfer learning and test-time augmentation improves convolutional neural network-based semantic segmentation of prostate cancer from multi-parametric MR images. Comput Methods Programs Biomed 210:106375

    Article  Google Scholar 

  33. Kumar Y, Koul A, Mahajan S (2022) A deep learning approaches and fastai text classification to predict 25 medical diseases from medical speech utterances, transcription and intent. Soft Comput 26:8253–8272. https://doi.org/10.1007/s00500-022-07261-y

    Article  Google Scholar 

  34. Khan MM, Omee AS, Tazin T, Almalki FA, Aljohani M, Algethami H (2022) A novel approach to predict brain cancerous tumor using transfer learning. Comput Mathematical Methods Med. https://doi.org/10.1155/2022/2702328

    Article  Google Scholar 

  35. Kumar Y, Gupta S (2023) Deep transfer learning approaches to predict glaucoma, cataract, choroidal neovascularization, diabetic macular edema, DRUSEN and healthy eyes: an experimental review. Arch Computat Methods Eng 30:521–541. https://doi.org/10.1007/s11831-022-09807-7

    Article  Google Scholar 

  36. Jnawali K, Chinni B, Dogra V, Rao N (2019) March). Transfer learning for automatic cancer tissue detection using multispectral photoacoustic imaging. Medical imaging 2019: Computer-aided diagnosis, vol 10950. SPIE, Bellingham, pp 982–987

    Google Scholar 

  37. Shandilya S, Nayak SR (2022) Analysis of lung cancer by using deep neural network. Innovation in electrical power engineering, communication, and computing technology. Springer, Singapore, pp 427–436

    Google Scholar 

  38. Gupta S, Kumar Y (2022) Cancer prognosis using artificial intelligence-based techniques. SN COMPUT SCI 3:77. https://doi.org/10.1007/s42979-021-00964-3

    Article  Google Scholar 

  39. Farag HH, Said LA, Rizk MR, Ahmed MAE (2021) Hyperparameters optimization for ResNet and Xception in the purpose of diagnosing COVID-19. J Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-210925

    Article  Google Scholar 

  40. Bhardwaj P, Bhandari G, Kumar Y et al (2022) An investigational approach for the prediction of gastric cancer using artificial intelligence techniques: a systematic review. Arch Comput Methods Eng 29:4379–4400. https://doi.org/10.1007/s11831-022-09737-4

    Article  Google Scholar 

  41. Kaur I, Sandhu AK, Kumar Y (2022) Artificial intelligence techniques for predictive modeling of vector-borne diseases and its pathogens: a systematic review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-022-09724-9

    Article  MathSciNet  Google Scholar 

  42. Iqbal S, Siddiqui GF, Rehman A, Hussain L, Saba T, Tariq U, Abbasi AA (2021) Prostate cancer detection using deep learning and traditional techniques. IEEE Access 9:27085–27100

    Article  Google Scholar 

  43. Koul A, Bawa RK, Kumar Y (2022) Artificial intelligence in medical image processing for airway diseases. In: Mishra S, González-Briones A, Bhoi AK, Mallick PK, Corchado JM (eds) Connected e-health. studies in computational intelligence, vol 1021. Springer, Cham

    Google Scholar 

  44. Kaul S, Kumar Y (2020) Artificial intelligence-based learning techniques for diabetes prediction: challenges and systematic review. SN Comput Sci 1(6):1–7

    Article  Google Scholar 

  45. Koul A, Bawa RK, Kumar Y (2022) Artificial intelligence techniques to predict the airway disorders illness: a systematic review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-022-09818-4

    Article  Google Scholar 

  46. Kumar Y, Koul A, Kaur S et al (2023) Machine learning and deep learning based time series prediction and forecasting of ten nations’ COVID-19 pandemic. SN COMPUT SCI 4:91. https://doi.org/10.1007/s42979-022-01493-3

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. School of Computing Science and Engineering, VIT Bhopal University, Sehore, Madhya Pradesh, India

    G. Prabu Kanna

  2. Department of Computer Science and Engineering, Kathir College Of Engineering, Coimbatore, India

    S J K Jagadeesh Kumar

  3. Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India

    P. Parthasarathi

  4. Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Yogesh Kumar

Authors
  1. G. Prabu Kanna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S J K Jagadeesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Parthasarathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yogesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Prabu Kanna.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kanna, G.P., Kumar, S.J.K.J., Parthasarathi, P. et al. A Review on Prediction and Prognosis of the Prostate Cancer and Gleason Grading of Prostatic Carcinoma Using Deep Transfer Learning Based Approaches. Arch Computat Methods Eng 30, 3113–3132 (2023). https://doi.org/10.1007/s11831-023-09896-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-023-09896-y

Search

Navigation