Skip to main content

Advertisement

SpringerLink
Log in

Development and validation of a clinical-radiomics model for prediction of prostate cancer: a multicenter study

  • Original Article
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

To develop an early diagnosis model of prostate cancer based on clinical-radiomics to improve the accuracy of imaging diagnosis of prostate cancer.

Methods

The multicenter study enrolled a total of 449 patients with prostate cancer from December 2017 to January 2022. We retrospectively collected information from 342 patients who underwent prostate biopsy at Minhang Hospital. We extracted T2WI images through 3D-Slice, and used mask tools to mark the prostate area manually. The radiomics features were extracted by Python using the “Pyradiomics” module. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for data dimensionality reduction and feature selection, and the radiomics score was calculated according to the correlation coefficients. Multivariate logistic regression analysis was used to develop predictive models. We incorporated the radiomics score, PI-RADS, and clinical features, and this was presented as a nomogram. The model was validated using a cohort of 107 patients from the Xuhui Hospital.

Results

In total, 110 effective radiomics features were extracted. Finally, 9 features were significantly associated with the diagnosis of prostate cancer, from which we calculated the radiomics score. The predictors contained in the individualized prediction nomogram included age, fPSA/tPSA, PI-RADS, and radiomics score. The clinical-radiomics model showed good discrimination in the validation cohort (C-index = 0.88).

Conclusion

This study presents a clinical-radiomics model that incorporates age, fPSA/PSA, PI-RADS, and radiomics score, which can be conveniently used to facilitate individualized prediction of prostate cancer before prostate biopsy.

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

Similar content being viewed by others

Data Availability

The data that support the fndings of this study are available from the corresponding author, [Hang Wang], upon reasonable request.

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249

    Article  PubMed  Google Scholar 

  2. Loeb S, Vellekoop A, Ahmed HU et al (2013) Systematic review of complications of prostate biopsy. Eur Urol 64(6):876–892

    Article  PubMed  Google Scholar 

  3. Falagario UG, Lantz A, Jambor I et al (2023) Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors. World J Urol 41(11):2967–2974

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mottet N, van den Bergh R, Briers E et al (2021) EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol. 79(2):243–262

    Article  CAS  PubMed  Google Scholar 

  5. Warlick C, Futterer J, Maruf M et al (2019) Beyond transrectal ultrasound-guided prostate biopsies: available techniques and approaches. World J Urol 37(3):419–427

    Article  PubMed  Google Scholar 

  6. Avolio PP, Lughezzani G, Paciotti M et al (2021) The use of 29 MHz transrectal micro-ultrasound to stratify the prostate cancer risk in patients with PI-RADS III lesions at multiparametric MRI: a single institutional analysis. Urol Oncol Semin Orig Investig 39(12):832.e1-832.e7

    Google Scholar 

  7. Avolio PP, Lughezzani G, Fasulo V et al (2023) Assessing the role of high-resolution microultrasound among naïve patients with negative multiparametric magnetic resonance imaging and a persistently high suspicion of prostate cancer. Eur Urol Open Sci 47:73–797

    Article  PubMed  Google Scholar 

  8. Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to radiomics. J Nucl Med 61(4):488–495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chiacchio G, Castellani D, Nedbal C et al (2023) Radiomics vs radiologist in prostate cancer. Results from a systematic review. World J Urol. 41(3):709–724

    Article  PubMed  Google Scholar 

  10. Stanzione A, Gambardella M, Cuocolo R et al (2020) Prostate MRI radiomics: a systematic review and radiomic quality score assessment. Eur J Radiol 129:109095

    Article  PubMed  Google Scholar 

  11. Sun Y, Reynolds HM, Parameswaran B et al (2019) Multiparametric MRI and radiomics in prostate cancer: a review. Australas Phys Eng Sci Med 42(1):3–25

    Article  PubMed  Google Scholar 

  12. Cuocolo R, Cipullo MB, Stanzione A et al (2019) Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp 3(1):35

    Article  PubMed  PubMed Central  Google Scholar 

  13. Turkbey B, Haider MA (2021) Deep learning-based artificial intelligence applications in prostate MRI: brief summary. Br J Radiol. 95:1131

    Google Scholar 

  14. Reda I, Khalil A, Elmogy M et al (2018) Deep learning role in early diagnosis of prostate cancer. Technol Cancer Res Treat 17:1533034618775530

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mottet N, Bellmunt J, Bolla M et al (2017) EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol. 71(4):618–629

    Article  PubMed  Google Scholar 

  16. Greer MD, Choyke PL, Turkbey B (2017) PI-RADSv2: How we do it. J Magn Reson Imaging 46(1):11–23

    Article  PubMed  Google Scholar 

  17. Smith CP, Harmon SA, Barrett T et al (2019) Intra- and interreader reproducibility of PI-RADSv2: a multireader study. J Magn Reson Imaging 49(6):1694–1703

    Article  PubMed  Google Scholar 

  18. Marra G, Zhuang J, Marquis A et al (2020) Pain in men undergoing transperineal free-hand multiparametric magnetic resonance imaging fusion targeted biopsies under local anesthesia: outcomes and predictors from a multicenter study of 1008 patients. J Urol 204(6):1209–1215

    Article  PubMed  Google Scholar 

  19. Lomas DJ, Ahmed HU (2020) All change in the prostate cancer diagnostic pathway. Nat Rev Clin Oncol 17(6):372–381

    Article  PubMed  Google Scholar 

  20. Bjurlin MA, Carter HB, Schellhammer P et al (2013) Optimization of initial prostate biopsy in clinical practice: sampling, labeling and specimen processing. J Urol 189(6):2039–2046

    Article  PubMed  PubMed Central  Google Scholar 

  21. Marra G, Zhuang J, Beltrami M et al (2021) Transperineal freehand multiparametric MRI fusion targeted biopsies under local anaesthesia for prostate cancer diagnosis: a multicentre prospective study of 1014 cases. Bju Int 127(1):122–130

    Article  CAS  PubMed  Google Scholar 

  22. Liu H, Ruan M, Wang H et al (2020) Can fewer transperineal systematic biopsy cores have the same prostate cancer detection rate as of magnetic resonance imaging/ultrasound fusion biopsy? Prostate Cancer Prostatic Dis 23(4):589–595

    Article  CAS  PubMed  Google Scholar 

  23. Jelidi A, Ohana M, Labani A et al (2017) Prostate cancer diagnosis: efficacy of a simple electromagnetic MRI-TRUS fusion method to target biopsies. Eur J Radiol 86:127–134

    Article  PubMed  Google Scholar 

  24. Oto J, Fernández-Pardo Á, Royo M et al (2020) A predictive model for prostate cancer incorporating PSA molecular forms and age. Sci Rep 10(1):2463

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kartasalo K, Bulten W, Delahunt B et al (2021) Artificial intelligence for diagnosis and gleason grading of prostate cancer in biopsies-current status and next steps. Eur Urol Focus 7(4):687–691

    Article  PubMed  Google Scholar 

  26. Ström P, Kartasalo K, Olsson H 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  PubMed  Google Scholar 

  27. Woźnicki P, Westhoff N, Huber T et al (2020) Multiparametric MRI for prostate cancer characterization: combined use of radiomics model with PI-RADS and clinical parameters. Cancers (Basel). 12(7):1767

    Article  PubMed  PubMed Central  Google Scholar 

  28. Qi Y, Zhang S, Wei J et al (2020) Multiparametric MRI-based radiomics for prostate cancer screening with PSA in 4–10 ng/mL to reduce unnecessary biopsies. J Magn Reson Imaging 51(6):1890–1899

    Article  PubMed  Google Scholar 

  29. Bjurlin MA, Rosenkrantz AB, Sarkar S et al (2018) Prediction of prostate cancer risk among men undergoing combined MRI-targeted and systematic biopsy using novel pre-biopsy nomograms that incorporate MRI findings. Urology 112:112–120

    Article  PubMed  Google Scholar 

  30. Ding Z, Song D, Wu H et al (2021) Development and validation of a nomogram based on multiparametric magnetic resonance imaging and elastography-derived data for the stratification of patients with prostate cancer. Quant Imag Med Surg 11(7):3252–3262

    Article  Google Scholar 

  31. Chen M, Wang R, Zhang T et al (2022) Nomogram predicting prostate cancer in patients with negative prebiopsy multiparametric magnetic resonance. Future Oncol 18(12):1473–1483

    Article  CAS  PubMed  Google Scholar 

  32. Ge Q, Zhang S, Xu H et al (2023) Development and validation of a novel nomogram predicting clinically significant prostate cancer in biopsy-naive men based on multi-institutional analysis. Cancer Med 12(24):21820–21829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by grant from Excellent Training Program of Minhang Hospital, Fudan University [2023MHPY03], Natural Science Foundation of Shanghai [22ZR1458000], Smart Medical Special Fund of Zhongshan Hospital, Fudan University [2020ZSLC16], and National Natural Science Foundation of China [62273099].

Author information

Author notes

  1. Jiaqi Huang and Chang He have contributed equally to this study.

Authors and Affiliations

  1. Department of Urology, Minhang Hospital, Fudan University, Shanghai, China

    Jiaqi Huang, Chang He, Bingde Yin, Minke He, Xuwei Lu & Jiawen Wu

  2. Department of Urology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China

    Peirong Xu

  3. Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China

    Bin Song & Hainan Zhao

  4. Department of Urology, Zhongshan Hospital, Fudan University, 180th Fengling Rd, Xuhui District, Shanghai, 200032, China

    Peirong Xu & Hang Wang

Authors
  1. Jiaqi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peirong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hainan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bingde Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Minke He
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xuwei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jiawen Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

(I) Conception and design: Jiaqi Huang and Hang Wang (II) Administrative support: Chang He; (III) Provision of study materials or patients: Jiaqi Huang, Chang He and Hang Wang; (IV) Collection and assembly of data: Peirong Xu, Bin Song, Hainan Zhao, Bingde Yin and Minke He; (V) Data analysis and interpretation: Jiaqi Huang, Xuwei Lu and Jiawen Wu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Corresponding author

Correspondence to Hang Wang.

Ethics declarations

Conflict of interest

The authors have declared no conflicts of interest.

Ethical approval

The authors are accountable for all aspects of the work by ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committees of Shanghai Xuhui Central Hospital [Approval No.: 2021–123] and informed consent was taken from all individual participants.

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 30 KB)

Supplementary file2 (DOCX 16 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

Huang, J., He, C., Xu, P. et al. Development and validation of a clinical-radiomics model for prediction of prostate cancer: a multicenter study. World J Urol 42, 275 (2024). https://doi.org/10.1007/s00345-024-04995-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00345-024-04995-2

Keywords

Search

Navigation