Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Clinical Studies

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence

British Journal of Cancer volume 129, pages 1625–1633 (2023)Cite this article

Subjects

Abstract

Background

To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy.

Methods

Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction.

Results

RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D’Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score.

Conclusions

AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Flowchart of iBCR-Net construction.
Fig. 2: The performance of AI-derived predictions including RadS, AI-detected ECE and PLNM for BCR-free survival.
Fig. 3: Results of baseline iBCR-Net M0 using Cox-PH, Cox-GBM and Cox-DL.
Fig. 4: Plots of predicted vs observed BCR-free survival curves.
Fig. 5: Cox-PH M5 expressed as a simplified survival nomogram for clinical application, by which, with AI-determined ECE and RadS at MRI, as well as with PSA, the predicted 1-year, 3-year and 5-year relapse-free survival rate can be calculated preoperatively.

Similar content being viewed by others

Data availability

Requests for the raw images and associated imaging data used to train and evaluate the model can be directed to Y-DZ and made available after specific IRB approvals and bespoke data agreement is established between the hospital health network and the requesting party.

Code availability

Source code of Lasso-Cox and Cox-GBM is archived on GitHub (https://github.com/scikitting/ethan/releases/tag/ibcr). Source code of Cox-DL is archived on GitHub (https://github.com/havakv/pycox/). Source code of AI-based ECE staging model is archived on GitHub (https://github.com/Cherishzyh/ProstateECE). The PLNM risk is calculated using the formula: Y = 0.34 × log (D-max) - 4.63× log (ADC) + 0.28 × log (PI-RADS v2 score) + 0.18 ×log (MRI T-stage) + 0.61× log (MRI-SVI) + 0.85 × log (number of positive cores) + 1.12 × log (MRI-LNI).

References

  1. Rebello RJ, Oing C, Knudsen KE, Loeb S, Johnson DC, Reiter RE, et al. Prostate cancer. Nat Rev Dis Prim. 2021;7:9.

    Article  PubMed  Google Scholar 

  2. Han M, Partin AW, Zahurak M, Piantadosi S, Epstein JI, Walsh PC. Biochemical (prostate specific antigen) recurrence probability following radical prostatectomy for clinically localized prostate cancer. J Urol. 2003;169:517–23.

    Article  PubMed  Google Scholar 

  3. Liesenfeld L, Kron M, Gschwend JE, Herkommer K. Prognostic factors for biochemical recurrence more than 10 years after radical prostatectomy. J Urol. 2017;197:143–8.

    Article  PubMed  Google Scholar 

  4. Walz J, Chun FK, Klein EA, Reuther A, Saad F, Graefen M, et al. Nomogram predicting the probability of early recurrence after radical prostatectomy for prostate cancer. J Urol. 2009;181:601–7.

    Article  PubMed  Google Scholar 

  5. Simmons MN, Stephenson AJ, Klein EA. Natural history of biochemical recurrence after radical prostatectomy: risk assessment for secondary therapy. Eur Urol. 2007;51:1175–84.

    Article  PubMed  Google Scholar 

  6. D’Amico AV, Whittington R, Malkowicz SB, Schultz D, Blank K, Broderick GA, et al. Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. JAMA. 1998;280:969–74.

    Article  PubMed  Google Scholar 

  7. Cooperberg MR, Pasta DJ, Elkin EP, Litwin MS, Latini DM, DuChane J, et al. The University of California, San Francisco cancer of the prostate risk assessment score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. J Urol. 2005;173:1938–42.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Cooperberg MR, Hilton JF, Carroll PR. The CAPRA-S score: a straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer. 2011;117:5039–46.

    Article  PubMed  Google Scholar 

  9. Zelic R, Garmo H, Zugna D, Stattin P, Richiardi L, Akre O, et al. Predicting prostate cancer death with different pretreatment risk stratification tools: a head-to-head comparison in a nationwide cohort study. Eur Urol. 2020;77:180–8.

    Article  PubMed  Google Scholar 

  10. Punnen S, Freedland SJ, Presti JC, Aronson WJ, Terris MK, Kane CJ, et al. Multi-institutional validation of the CAPRA-S score to predict disease recurrence and mortality after radical prostatectomy. Eur Urol. 2014;65:1171–7.

    Article  PubMed  Google Scholar 

  11. Lughezzani G, Budaus L, Isbarn H, Sun M, Perrotte P, Haese A, et al. Head-to-head comparison of the three most commonly used preoperative models for prediction of biochemical recurrence after radical prostatectomy. Eur Urol. 2010;57:562–8.

    Article  PubMed  Google Scholar 

  12. Brajtbord JS, Leapman MS, Cooperberg MR. The CAPRA score at 10 years: contemporary perspectives and analysis of supporting studies. Eur Urol. 2017;71:705–9.

    Article  PubMed  Google Scholar 

  13. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, et al. Prostate Imaging Reporting and Data System version 2.1: 2019 update of Prostate Imaging Reporting and Data System version 2. Eur Urol. 2019;76:340–51.

    Article  PubMed  Google Scholar 

  14. Morlacco A, Sharma V, Viers BR, Rangel LJ, Carlson RE, Froemming AT, et al. The incremental role of magnetic resonance imaging for prostate cancer staging before radical prostatectomy. Eur Urol. 2017;71:701–4.

    Article  PubMed  Google Scholar 

  15. Ho R, Siddiqui MM, George AK, Frye T, Kilchevsky A, Fascelli M, et al. Preoperative multiparametric magnetic resonance imaging predicts biochemical recurrence in prostate cancer after radical prostatectomy. PLoS ONE. 2016;11:e0157313.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gandaglia G, Ploussard G, Valerio M, Marra G, Moschini M, Martini A, et al. Prognostic implications of multiparametric magnetic resonance imaging and concomitant systematic biopsy in predicting biochemical recurrence after radical prostatectomy in prostate cancer patients diagnosed with magnetic resonance imaging-targeted biopsy. Eur Urol Oncol. 2020;3:739–47.

    Article  PubMed  Google Scholar 

  17. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.

    Article  PubMed  Google Scholar 

  18. Shiradkar R, Ghose S, Jambor I, Taimen P, Ettala O, Purysko AS, et al. Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings. J Magn Reson Imaging. 2018;48:1626–36.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bourbonne V, Vallières M, Lucia F, Doucet L, Visvikis D, Tissot V, et al. MRI-derived radiomics to guide post-operative management for high-risk prostate cancer. Front Oncol. 2019;9:807.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wang P, Li Y, Reddy CK. Machine learning for survival analysis: a survey. ACM Comput Surv. 2019;51:110.

  21. Bello GA, Dawes TJW, Duan J, Biffi C, de Marvao A, Howard L, et al. Deep learning cardiac motion analysis for human survival prediction. Nat Mach Intell. 2019;1:95–104.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hansen N, Patruno G, Wadhwa K, Gaziev G, Miano R, Barrett T, et al. Magnetic resonance and ultrasound image fusion supported transperineal prostate biopsy using the ginsburg protocol: technique, learning points, and biopsy results. Eur Urol. 2016;70:332–40.

    Article  PubMed  Google Scholar 

  23. Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA, et al. The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma: definition of grading patterns and proposal for a new grading system. Am J Surg Pathol. 2016;40:244–52.

    Article  PubMed  Google Scholar 

  24. Epstein JI, Allsbrook WC Jr., Amin MB, Egevad LL, Committee IG. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol. 2005;29:1228–42.

    Article  PubMed  Google Scholar 

  25. Cornford P, van den Bergh RCN, Briers E, den Broeck TV, Cumberbatch MG, De Santis M, et al. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer. Part II—2020 update: treatment of relapsing and metastatic prostate cancer. Eur Urol. 2021;79:263–82.

  26. Brockman JA, Alanee S, Vickers AJ, Scardino PT, Wood DP, Kibel AS, et al. Nomogram predicting prostate cancer-specific mortality for men with biochemical recurrence after radical prostatectomy. Eur Urol. 2015;67:1160–7.

    Article  PubMed  Google Scholar 

  27. Hou Y, Zhang YH, Bao J, Bao ML, Yang G, Shi HB, et al. Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study. Eur J Nucl Med Mol Imaging. 2021;48:3805–16.

    Article  CAS  PubMed  Google Scholar 

  28. Hou Y, Bao ML, Wu CJ, Zhang J, Zhang YD, Shi HB. A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection. BJU Int. 2019;124:972–83.

    Article  PubMed  Google Scholar 

  29. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16:385–95.

    Article  CAS  PubMed  Google Scholar 

  30. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189–232.

    Article  Google Scholar 

  31. Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18:24.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Håvard Kvamme ØB, Scheel I. Time-to-event prediction with neural networks and Cox regression. J Mach Learn Res. 2019;20:1–30.

    Google Scholar 

  33. Lee C, Zame W, Yoon J, van der Schaar M. Deephit: a deep learning approach to survival analysis with competing risks. Thirty-Second AAAI Conference on Artificial Intelligence; 2018.

  34. Fotso S. Deep neural networks for survival analysis based on a multi-task framework. [Preprint]. 2018. Available from: https://arxiv.org/abs/1801.05512.

  35. Kvamme H, Borgan Ø. Continuous and discrete-time survival prediction with neural networks. [Preprint]. 2019. Available from: https://arxiv.org/abs/1910.06724.

  36. Meissner VH, Rauscher I, Schwamborn K, Neumann J, Miller G, Weber W, et al. Radical prostatectomy without prior biopsy following multiparametric magnetic resonance imaging and prostate-specific membrane antigen positron emission tomography. Eur Urol. 2022;82:156–60.

  37. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10:7252–9.

    Article  CAS  PubMed  Google Scholar 

  38. Wilczak W, Wittmer C, Clauditz T, Minner S, Steurer S, Buscheck F, et al. Marked prognostic impact of minimal lymphatic tumor spread in prostate cancer. Eur Urol. 2018;74:376–86.

    Article  PubMed  Google Scholar 

  39. Jeong BC, Chalfin HJ, Lee SB, Feng Z, Epstein JI, Trock BJ, et al. The relationship between the extent of extraprostatic extension and survival following radical prostatectomy. Eur Urol. 2015;67:342–6.

    Article  PubMed  Google Scholar 

  40. Wang J, Wu CJ, Bao ML, Zhang J, Shi HB, Zhang YD. Using support vector machine analysis to assess PartinMR: a new prediction model for organ-confined prostate cancer. J Magn Reson Imaging. 2018;48:499–506.

    Article  PubMed  Google Scholar 

  41. von Bodman C, Godoy G, Chade DC, Cronin A, Tafe LJ, Fine SW, et al. Predicting biochemical recurrence-free survival for patients with positive pelvic lymph nodes at radical prostatectomy. J Urol. 2010;184:143–8.

    Article  Google Scholar 

Download references

Funding

Funding

This work was supported by the Key Research and Development Program of Jiangsu Province (BE2017756, Y-DZ) and the Outstanding Postdoctoral Program of Jiangsu Province (2023ZB612, YH).

Author information

Author notes

  1. These authors contributed equally: Ying Hou, Ke-Wen Jiang.

  2. These authors jointly supervised this work: Jin-Rong Qu, Fei-Peng Zhu, Yu-Dong Zhang.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, P. R. China

    Ying Hou, Rui Zhi, Qiao Li, Jing Zhang, Fei-Peng Zhu & Yu-Dong Zhang

  2. Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China

    Ke-Wen Jiang

  3. Department of Breast Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, 350014, Fuzhou, China

    Li-Li Wang

  4. Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, P. R. China

    Mei-Ling Bao

  5. Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, Henan, China

    Jin-Rong Qu

Authors
  1. Ying Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ke-Wen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li-Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Zhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mei-Ling Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jin-Rong Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fei-Peng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yu-Dong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YH: Conceptualization, Data curation, Formal analysis, Writing—original draft. K-WJ: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing—original draft, Writing—review & editing. L-LW: Data curation, Formal analysis. RZ: Data curation, Formal analysis. M-LB: Data curation, Formal analysis. QL: Data curation, Formal analysis. JZ: Data curation, Formal analysis. F-PZ, J-RQ, and Y-DZ: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Validation, Project administration, Resources, Software, Supervision, Writing—original draft, Writing—review & editing.

Corresponding author

Correspondence to Yu-Dong Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

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

Hou, Y., Jiang, KW., Wang, LL. et al. Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence. Br J Cancer 129, 1625–1633 (2023). https://doi.org/10.1038/s41416-023-02441-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41416-023-02441-5

Search

Advanced search

Quick links