Skip to main content

Advertisement

SpringerLink
Log in

Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer

  • Topic Paper
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

To established an AI system to make the pathological diagnosis of prostate cancer.

Methods

Prostate histopathological whole mount (WM) sections from patients underwent robot-assisted laparoscopic prostatectomy were prepared. All the prostate WM pathological sections were converted to digital image data and marked with different colors on the basis of the ISUP Gleason grade group. The image was then fed into a segmentation algorithm. We chose modified U-Net as our fundamental network architecture.

Results

172 patients were involved in this study. 896 pieces of prostate WM pathological sections from 160 patients, in which 826 pieces of WM sections from 148 patients were assigned to the training set randomly. After image segmentation there were totally 2,138,895 patches, of which 1,646,535 patches were valid for training. The other WM section was arranged for testing. Based on the whole image testing, AI and pathologists presented the same answers among 21 of 22 pieces of sections. To evaluate the diagnostic results at the pixel level, we anticipated correct cancer or non-cancer diagnose from this AI system. The area under the ROC curve as 96.8%. The value of pixel accuracy of three methods (binary analysis, clinically oriented analysis and analysis for different ISUP Gleason grade) were 96.93%, 95.43% and 93.88%, respectively. The value of frequency weighted IoU were 94.32%, 92.13% and 90.21%, respectively.

Conclusions

This AI system is able to assist pathologists to make a final diagnosis, indicating the great potential and a wide-range of applications of AI in the medical field.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author, HG, upon reasonable request.

References

  1. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30. https://doi.org/10.3322/caac.21442

    Article  PubMed  Google Scholar 

  2. 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. CA Cancer J Clin 15:10. https://doi.org/10.3322/caac.21492

    Article  Google Scholar 

  3. Rice KR, Furusato B, Chen Y, McLeod DG, Sesterhenn IA, Brassell SA (2009) Clinicopathological behavior of single focus prostate adenocarcinoma. J Urol 182(6):2689–2694. https://doi.org/10.1016/j.juro.2009.08.055

    Article  PubMed  Google Scholar 

  4. Epstein JI, Zelefsky MJ, Sjoberg DD, Nelson JB, Egevad L, Magi-Galluzzi C et al (2016) A contemporary prostate cancer grading system: a validated alternative to the Gleason score. Eur Urol 69(3):428–435. https://doi.org/10.1016/j.eururo.2015.06.046

    Article  PubMed  Google Scholar 

  5. Elmore JG, Longton GM, Carney PA, Geller BM, Onega T, Tosteson AN et al (2015) Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11):1122–1132. https://doi.org/10.1001/jama.2015.1405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Al-Maghrabi JA, Bakshi NA, Farsi HM (2013) Gleason grading of prostate cancer in needle core biopsies: a comparison of general and urologic pathologists. Ann Saudi Med 33(1):40–44. https://doi.org/10.5144/0256-4947.2013.40

    Article  PubMed  PubMed Central  Google Scholar 

  7. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on neural information processing systems—Volume 1. Lake Tahoe, Nevada: Curran Associates Inc., pp 1097–105

  8. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al (2015) Going deeper with convolutions. In: Cvpr

  9. Liu S, Deng W (2015) Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian Conference on pattern recognition (ACPR), pp 730–4.

  10. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)

  11. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 3431–40

  12. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2016) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs (2016). arXiv preprint arXiv:160600915

  13. Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–87

  14. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–9

  15. Rabilloud N, Allaume P, Acosta O, De Crevoisier R, Bourgade R, Loussouarn D et al (2023) Deep learning methodologies applied to digital pathology in prostate cancer: a systematic review. Diagnostics (Basel). https://doi.org/10.3390/diagnostics13162676

    Article  PubMed  Google Scholar 

  16. Frewing A, Gibson AB, Robertson R, Urie PM, Corte DD (2023) Don’t fear the artificial intelligence: a systematic review of machine learning for prostate cancer detection in pathology. Arch Pathol Lab Med. https://doi.org/10.5858/arpa.2022-0460-RA

    Article  PubMed  Google Scholar 

  17. McNeal JE, Haillot O (2001) Patterns of spread of adenocarcinoma in the prostate as related to cancer volume. Prostate 49(1):48–57. https://doi.org/10.1002/pros.1117

    Article  CAS  PubMed  Google Scholar 

  18. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) UNet++: a nested U-NET architecture for medical image segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support 2018(11045):3–11. https://doi.org/10.1007/978-3-030-00889-5_1

    Article  Google Scholar 

  19. Obuchowski NA (1997) Nonparametric analysis of clustered ROC curve data. Biometrics 53(2):567–578

    Article  MathSciNet  CAS  PubMed  Google Scholar 

  20. Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Garcia-Rodriguez J (2017) A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:170406857

  21. Litjens G, Sanchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I et al (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6:26286. https://doi.org/10.1038/srep26286

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yin Y, Zhang Q, Zhang H, He Y, Huang J (2017) Molecular signature to risk-stratify prostate cancer of intermediate risk. Clin Cancer Res 23(1):6–8. https://doi.org/10.1158/1078-0432.CCR-16-2400

    Article  CAS  PubMed  Google Scholar 

  23. Kayalibay B, Jensen G, van der Smagt P (2017) CNN-based segmentation of medical imaging data. arXiv preprint arXiv:170103056

  24. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234–41

  25. Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582. https://doi.org/10.1148/radiol.2017162326

    Article  PubMed  Google Scholar 

  26. Norman B, Pedoia V, Majumdar S (2018) Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR IMAGING DATA TO DETERMINE RELAXOMETRY AND MORPHOMEtry. Radiology 288(1):177–185. https://doi.org/10.1148/radiol.2018172322

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China (81902581, 81572519, 81772710), Basic Research Program (Natural Science Foundation) of Jiangsu Province (BK20190117), China Postdoctoral Foundation (2019M660111), Mobility Programme of Sino-German Center (M-0670)

Author information

Author notes

  1. Chengwei Zhang, Xiubin Gao, Bo Fan have contributed equally to this work.

Authors and Affiliations

  1. Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China

    Chengwei Zhang, Xiaoyu Lyu, Qing Zhang & Hongqian Guo

  2. Nanjing Innovative Data Technologies, Inc., Nanjing, 210014, Jiangsu, China

    Xiubin Gao & Peng Liu

  3. Department of Urology, The First People’s Hospital of Changshu, The Changshu Hospital Affiliated to Soochow University, Changshu, 215500, China

    Bo Fan

  4. College of Global Public Health, New York University, NY, 10012, USA

    Suhan Guo

  5. Department of Pathology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China

    Jiong Shi & Yao Fu

Authors
  1. Chengwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiubin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suhan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoyu Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yao Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Peng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hongqian Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CZ: Project development, Data collection, Data analysis, Manuscript writing. XG: Data analysis, Manuscript writing. BF: Data collection, Manuscript writing. SG: Data analysis, Manuscript writing. XL: Data collection. JS: Data analysis. YF: Data analysis. QZ: Project development, Manuscript editing. PL: Project development. HG: Project development.

Corresponding authors

Correspondence to Qing Zhang, Peng Liu or Hongqian Guo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

Anonymous data of human participants were involved in this study. The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki. Animals were not included in this study.

Informed consent

All the patients involved in the study were provided signed informed consent, which was approved by the ethics committee of Nanjing Drum Tower Hospital.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 864 KB)

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

Zhang, C., Gao, X., Fan, B. et al. Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer. World J Urol 42, 93 (2024). https://doi.org/10.1007/s00345-024-04775-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00345-024-04775-y

Keywords

Search

Navigation