Skip to main content
SpringerLink
Log in

Patient-related characteristics predict prostate cancers in men with PI-RADS 4–5 to further optimize the diagnostic performance of MRI

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4–5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI.

Materials and methods

The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated.

Results

A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694–0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844–0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%.

Conclusion

The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4–5.

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

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (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. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Jiang KW, Song Y, Hou Y, Zhi R, Zhang J, Bao ML, Li H, Yan X, Xi W, Zhang CX, Yao YF, Yang G, Zhang YD (2022) Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study. J Magn Reson Imaging https://doi.org/10.1002/jmri.28427

    Article  PubMed  Google Scholar 

  3. Cheng X, Chen Y, Xu H, Ye L, Tong S, Li H, Zhang T, Tian S, Qi J, Zeng H, Yao J, Song B (2022) Avoiding Unnecessary Systematic Biopsy in Clinically Significant Prostate Cancer: Comparison Between MRI-Based Radiomics Model and PI-RADS Category. J Magn Reson Imaging https://doi.org/10.1002/jmri.28333

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ahdoot M, Wilbur AR, Reese SE, Lebastchi AH, Mehralivand S, Gomella PT, Bloom J, Gurram S, Siddiqui M, Pinsky P, Parnes H, Linehan WM, Merino M, Choyke PL, Shih JH, Turkbey B, Wood BJ, Pinto PA (2020) MRI-Targeted, Systematic, and Combined Biopsy for Prostate Cancer Diagnosis. N Engl J Med 382(10):917-928. https://doi.org/10.1056/NEJMoa1910038

    Article  PubMed  PubMed Central  Google Scholar 

  5. Exterkate L, Wegelin O, Barentsz JO, van der Leest MG, Kummer JA, Vreuls W, de Bruin PC, Bosch J, van Melick HHE, Somford DM (2020) Is There Still a Need for Repeated Systematic Biopsies in Patients with Previous Negative Biopsies in the Era of Magnetic Resonance Imaging-targeted Biopsies of the Prostate? Eur Urol Oncol 3(2):216-223. https://doi.org/10.1016/j.euo.2019.06.005

    Article  PubMed  Google Scholar 

  6. Kim CH, Kim CK, Park JJ, Park SY, Yoon YC (2021) Yield of concurrent systemic biopsy during MRI-targeted biopsy according to Prostate Imaging Reporting and Data System version 2 in patients with suspected prostate cancer. Eur Radiol 31(3):1667-1675. https://doi.org/10.1007/s00330-020-07167-z

    Article  PubMed  Google Scholar 

  7. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Tempany CM, Choyke PL, Cornud F, Margolis DJ, Thoeny HC, Verma S, Barentsz J, Weinreb JC (2019) Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 76(3):340-351. https://doi.org/10.1016/j.eururo.2019.02.033

    Article  PubMed  Google Scholar 

  8. Mottet N, van den Bergh RCN, Briers E, Van den Broeck T, Cumberbatch MG, De Santis M, Fanti S, Fossati N, Gandaglia G, Gillessen S, Grivas N, Grummet J, Henry AM, van der Kwast TH, Lam TB, Lardas M, Liew M, Mason MD, Moris L, Oprea-Lager DE, van der Poel HG, Rouvière O, Schoots IG, Tilki D, Wiegel T, Willemse PM, Cornford P (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. https://doi.org/10.1016/j.eururo.2020.09.042

    Article  CAS  PubMed  Google Scholar 

  9. Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, Collaco-Moraes Y, Ward K, Hindley RG, Freeman A, Kirkham AP, Oldroyd R, Parker C, Emberton M (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389(10071):815-822. https://doi.org/10.1016/s0140-6736(16)32401-1

    Article  PubMed  Google Scholar 

  10. Moldovan PC, Van den Broeck T, Sylvester R, Marconi L, Bellmunt J, van den Bergh RCN, Bolla M, Briers E, Cumberbatch MG, Fossati N, Gross T, Henry AM, Joniau S, van der Kwast TH, Matveev VB, van der Poel HG, De Santis M, Schoots IG, Wiegel T, Yuan CY, Cornford P, Mottet N, Lam TB, Rouvière O (2017) What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel. Eur Urol 72(2):250-266. https://doi.org/10.1016/j.eururo.2017.02.026

    Article  PubMed  Google Scholar 

  11. Kasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, Briganti A, Budäus L, Hellawell G, Hindley RG, Roobol MJ, Eggener S, Ghei M, Villers A, Bladou F, Villeirs GM, Virdi J, Boxler S, Robert G, Singh PB, Venderink W, Hadaschik BA, Ruffion A, Hu JC, Margolis D, Crouzet S, Klotz L, Taneja SS, Pinto P, Gill I, Allen C, Giganti F, Freeman A, Morris S, Punwani S, Williams NR, Brew-Graves C, Deeks J, Takwoingi Y, Emberton M, Moore CM (2018) MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med 378(19):1767-1777. https://doi.org/10.1056/NEJMoa1801993

    Article  PubMed  PubMed Central  Google Scholar 

  12. Maxeiner A, Kittner B, Blobel C, Wiemer L, Hofbauer SL, Fischer T, Asbach P, Haas M, Penzkofer T, Fuller F, Miller K, Cash H (2018) Primary magnetic resonance imaging/ultrasonography fusion-guided biopsy of the prostate. BJU Int 122(2):211-218. https://doi.org/10.1111/bju.14212

    Article  PubMed  Google Scholar 

  13. Thompson IM, Ankerst DP, Chi C, Lucia MS, Goodman PJ, Crowley JJ, Parnes HL, Coltman CA, Jr. (2005) Operating characteristics of prostate-specific antigen in men with an initial PSA level of 3.0 ng/ml or lower. Jama 294(1):66-70. https://doi.org/10.1001/jama.294.1.66

    Article  CAS  PubMed  Google Scholar 

  14. Liu Y, Dong L, Xiang L, Zhou B, Wang H, Zhang Y, Xu G, Wu J, Wang S, Zhang Y, Xu H (2021) Does PSA level affect the choice of prostate puncture methods among MRI-ultrasound fusion targeted biopsy, transrectal ultrasound systematic biopsy or the combination of both? Br J Radiol 94(1123):20210312. https://doi.org/10.1259/bjr.20210312

    Article  PubMed  PubMed Central  Google Scholar 

  15. Liu J, Wang ZQ, Li M, Zhou MY, Yu YF, Zhan WW (2020) Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4-10 ng ml(-1)). Asian J Androl 22(2):213-216. https://doi.org/10.4103/aja.aja_46_19

    Article  CAS  PubMed  Google Scholar 

  16. Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA (2016) 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 40(2):244-252. https://doi.org/10.1097/pas.0000000000000530

    Article  PubMed  Google Scholar 

  17. Kornienko K, Reuter M, Maxeiner A, Günzel K, Kittner B, Reimann M, Hofbauer SL, Wiemer LE, Heckmann R, Asbach P, Wendler JJ, Schostak M, Schlomm T, Friedersdorff F, Cash H (2022) Follow-up of men with a PI-RADS 4/5 lesion after negative MRI/Ultrasound fusion biopsy. Sci Rep 12(1):13603. https://doi.org/10.1038/s41598-022-17260-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bangma CH, van Leenders G, Roobol MJ, Schoots IG (2021) Restricting False-positive Magnetic Resonance Imaging Scans to Reduce Overdiagnosis of Prostate Cancer. Eur Urol 79(1):30-32. https://doi.org/10.1016/j.eururo.2020.10.013

    Article  PubMed  Google Scholar 

  19. Borofsky S, George AK, Gaur S, Bernardo M, Greer MD, Mertan FV, Taffel M, Moreno V, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Turkbey B (2018) What Are We Missing? False-Negative Cancers at Multiparametric MR Imaging of the Prostate. Radiology 286(1):186-195. https://doi.org/10.1148/radiol.2017152877

    Article  PubMed  Google Scholar 

  20. Panebianco V, Giganti F, Kitzing YX, Cornud F, Campa R, De Rubeis G, Ciardi A, Catalano C, Villeirs G (2018) An update of pitfalls in prostate mpMRI: a practical approach through the lens of PI-RADS v. 2 guidelines. Insights Imaging 9(1):87-101. https://doi.org/10.1007/s13244-017-0578-x

    Article  PubMed  Google Scholar 

  21. Miyai K, Mikoshi A, Hamabe F, Nakanishi K, Ito K, Tsuda H, Shinmoto H (2019) Histological differences in cancer cells, stroma, and luminal spaces strongly correlate with in vivo MRI-detectability of prostate cancer. Mod Pathol 32(10):1536-1543. https://doi.org/10.1038/s41379-019-0292-y

    Article  CAS  PubMed  Google Scholar 

  22. Chatterjee A, Watson G, Myint E, Sved P, McEntee M, Bourne R (2015) Changes in Epithelium, Stroma, and Lumen Space Correlate More Strongly with Gleason Pattern and Are Stronger Predictors of Prostate ADC Changes than Cellularity Metrics. Radiology 277(3):751-762. https://doi.org/10.1148/radiol.2015142414

    Article  PubMed  Google Scholar 

  23. Stavrinides V, Norris JM, Karapanagiotis S, Giganti F, Grey A, Trahearn N, Freeman A, Haider A, Carmona Echeverría LM, Bott SRJ, Brown LC, Burns-Cox N, Dudderidge TJ, El-Shater Bosaily A, Ghei M, Henderson A, Hindley RG, Kaplan RS, Oldroyd R, Parker C, Persad R, Rosario DJ, Shergill IS, Winkler M, Kirkham A, Punwani S, Whitaker HC, Ahmed HU, Emberton M (2022) Regional Histopathology and Prostate MRI Positivity: A Secondary Analysis of the PROMIS Trial. Radiology:220762. https://doi.org/10.1148/radiol.220762

    Article  PubMed  Google Scholar 

  24. Ding Z, Jiao Y, Wu H, Zhang L, Song H, Ni Z, Ye X, Xu J, Dong F (2020) Clinical Value of the Elastographic Q-Analysis Score in Assisting Real-Time Elastography-Guided Prostate Biopsy: A Retrospective Study of 125 Patients. J Ultrasound Med 39(1):83-87. https://doi.org/10.1002/jum.15075

    Article  PubMed  Google Scholar 

  25. Wildeboer RR, Mannaerts CK, van Sloun RJG, Budäus L, Tilki D, Wijkstra H, Salomon G, Mischi M (2020) Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics. Eur Radiol 30(2):806-815. https://doi.org/10.1007/s00330-019-06436-w

    Article  PubMed  Google Scholar 

  26. Movassaghi M, Ahmed F, Patel H, Luk L, Hyams E, Wenske S, Shaish H (2022) Association of Patient and Imaging-Related Factors With False Negative MRI-Targeted Prostate Biopsies of Suspicious PI-RADS 4 and 5 Lesions. Urology 167:165-170. https://doi.org/10.1016/j.urology.2022.04.026

    Article  PubMed  Google Scholar 

  27. Liu J, Yu S, Dong B, Hong G, Tao J, Fan Y, Zhu Z, Wang Z, Zhang X (2021) Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan. Front Oncol 11:732027. https://doi.org/10.3389/fonc.2021.732027

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all of the study participants.

Funding

This work was financially supported by Natural Science Foundation of Bengbu Medical College (Grant No. 2022byzd156) and National Natural Science Foundation of China (Grant No. 82202153).

Author information

Authors and Affiliations

  1. Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People’s Hospital, School of Medicine, Tongji university, Shanghai, 200072, China

    Lihua Xiang & Yunyun Liu

  2. Department of Medical Ultrasound, Bengbu First People’s Hospital, Bengbu, 233000, Anhui, China

    Suping Ma, Yongqiang Xu & Hongyan Liu

  3. Department of Urinary Surgery, Bengbu First People’s Hospital, Bengbu, 233000, Anhui, China

    Lei Jiang & Hao Guo

Authors
  1. Lihua Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suping Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongqiang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hao Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongyan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yunyun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YYL and HYL carried out the study design. SPM carried out the data collection, statistical analysis, and data interpretation. LHX drafted the manuscript. YQX, LJ and HG conceived of the study, participated in its design and coordination, and helped draft the manuscript. LHX and SPM supervised the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yunyun Liu.

Ethics declarations

Conflict of interest

The author declares that there is no conflict of interest between them.

Ethical approval

Institutional Review Board approval was obtained.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Consent for publication

Informed consent from all eligible patients was obtained.

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 310 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

Xiang, L., Ma, S., Xu, Y. et al. Patient-related characteristics predict prostate cancers in men with PI-RADS 4–5 to further optimize the diagnostic performance of MRI. Abdom Radiol 48, 3766–3773 (2023). https://doi.org/10.1007/s00261-023-04011-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-023-04011-y

Keywords

Search

Navigation