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.

Development of a novel nomogram to identify the candidate to extended pelvic lymph node dissection in patients who underwent mpMRI and target biopsy only

Abstract

Background

Nowadays a tool able to predict the risk of lymph-node invasion (LNI) in patients underwent target biopsy (TB) only before radical prostatectomy (RP) is still lacking. Our aim is to develop a model based on mp-MRI and target biopsy (TB) alone able to predict the risk of LNI.

Methods

We retrospectively extracted data of patients with preoperative positive mp-MRI and TB only who underwent RARP with ePLND from April 2014 to March 2020. A logistic regression model was performed to evaluate the impact of pre- and intra-operative factors on the risk of LNI. Model discrimination was assessed using an area under (AUC) the ROC curve. A nomogram, and its calibration plot, to predict the risk of LNI were generated based on the logistic model. A validation of the model was done using a similar cohort.

Results

461 patients were included, of which 52 (11.27) had LNI. After logistic regression analysis and multivariable model DRE, PI-RADS, seminal vesicle invasion, PSA and worst GS at I and II target lesions were significant predictors of LNI. The AUC was 0.74 [0.67–0.81] 95% CI. The calibration plot shows that our model is very close to the ideal one which is in the 95% CI. After the creation of a visual nomogram, the cut-off to discriminate between the risk or not of LNI was set with Youden index at 60 points that correspond to a risk of LNI of 7%. The model applied on a similar cohort shown a LH+ of 2.58 [2.17–2.98] 95% CI.

Conclusions

Our nomogram for patients undergoing MRI-TB only takes into account clinical stage, SVI at MRI, biopsy Gleason pattern and PSA and it is able to identify patients with risk of LNI when a score higher than 7% is achieved.

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

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Evaluation of modelʼs performance.
Fig. 2

Data availability

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

References

  1. Checcucci E, Amparore D, De Luca S, Autorino R, Fiori C, Porpiglia F. Precision prostate cancer surgery: an overview of new technologies and techniques. Minerva Urol Nefrol. 2019;71:487–501. https://doi.org/10.23736/S0393-2249.19.03365-4.

    Article  PubMed  Google Scholar 

  2. Onol FF, Bhat S, Moschovas M, Rogers T, Albala D, Patel V. The ongoing dilemma in pelvic lymph node dissection during radical prostatectomy: who should decide and in which patients? J Robot Surg. 2020;14:549–58. https://doi.org/10.1007/s11701-019-01041-x.

    Article  PubMed  Google Scholar 

  3. Fossati N, Willemse PM, Van den Broeck T, van den Bergh RCN, Yuan CY, Briers E, et al. The benefits and harms of different extents of lymph node dissection during radical prostatectomy for prostate cancer: a systematic review. Eur Urol. 2017;72:84–109. https://doi.org/10.1016/j.eururo.2016.12.003.

    Article  PubMed  Google Scholar 

  4. Tyritzis SI, Kalampokis N, Grivas N, van der Poel H, Wiklund NP. Robot-assisted extended lymphadenectomy in prostate cancer. Minerva Chir. 2019;74:88–96. https://doi.org/10.23736/S0026-4733.18.07780-5.

    Article  PubMed  Google Scholar 

  5. Mottet N, van den Bergh RCN, Briers E, Van den Broeck T, Cumberbatch MG, De Santis M, et al. EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol. 2021;79:243–62. https://doi.org/10.1016/j.eururo.2020.09.042.

    CAS  Article  PubMed  Google Scholar 

  6. Sebben M, Tafuri A, Pirozzi M, Processali T, Rizzetto R, Amigoni N, et al. Open approach, extended pelvic lymph node dissection, and seminal vesicle invasion are independent predictors of hospital readmission after prostate cancer surgery: a large retrospective study. Minerva Urol Nefrol. 2020;72:72–81. https://doi.org/10.23736/S0393-2249.19.03586-0.

    Article  PubMed  Google Scholar 

  7. Bandini M, Marchioni M, Pompe RS, Tian Z, Gandaglia G, Fossati N, et al. First North American validation and head-to-head comparison of four preoperative nomograms for prediction of lymph node invasion before radical prostatectomy. BJU Int. 2018;121:592–9. https://doi.org/10.1111/bju.14074.

    Article  PubMed  Google Scholar 

  8. Gandaglia G, Ploussard G, Valerio M, Mattei A, Fiori C, Fossati N, et al. A novel nomogram to identify candidates for extended pelvic lymph node dissection among patients with clinically localized prostate cancer diagnosed with magnetic resonance imaging-targeted and systematic biopsies. Eur Urol. 2019;75:506–14. https://doi.org/10.1016/j.eururo.2018.10.012.

    Article  PubMed  Google Scholar 

  9. Porpiglia F, Manfredi M, Mele F, Bertolo R, Bollito E, Gned D, et al. Indication to pelvic lymph nodes dissection for prostate cancer: the role of multiparametric magnetic resonance imaging when the risk of lymph nodes invasion according to Briganti updated nomogram is <5. Prostate Cancer Prostatic Dis. 2018;21:85–91. https://doi.org/10.1038/s41391-017-0026-5.

    Article  PubMed  Google Scholar 

  10. Porpiglia F, Manfredi M, Mele F, Cossu M, Bollito E, Veltri A, et al. Diagnostic pathway with multiparametric magnetic resonance imaging versus standard pathway: results from a randomized prospective study in biopsy-naïve patients with suspected prostate cancer. Eur Urol. 2017;72:282–8. https://doi.org/10.1016/j.eururo.2016.08.041.

    Article  PubMed  Google Scholar 

  11. Checcucci E, De Cillis S, Piramide F, Amparore D, Kasivisvanathan V, Giganti F, et al. The role of additional standard biopsy in the MRI-targeted biopsy era. Minerva Urol Nefrol. 2020;72:637–9. https://doi.org/10.23736/S0393-2249.20.03958-2.

    Article  PubMed  Google Scholar 

  12. Checcucci E, De Cillis S, Amparore D, Garrou D, Aimar R, Piana A et al. Naive patients with suspicious prostate cancer and positive multiparametric magnetic resonance imaging (mp-MRI): is it time for fusion target biopsy alone? J Clin Urol. 2021. https://doi.org/10.1177/20514158211023713.

  13. Miah S, Hosking-Jervis F, Connor MJ, Eldred-Evans D, Shah TT, Arya M, et al. A multicentre analysis of the detection of clinically significant prostate cancer following transperineal image-fusion targeted and nontargeted systematic prostate biopsy in men at risk. Eur Urol Oncol. 2020;3:262–9. https://doi.org/10.1016/j.euo.2019.03.005.

    Article  PubMed  Google Scholar 

  14. Goldberg H, Ahmad AE, Chandrasekar T, Klotz L, Emberton M, Haider MA, et al. Comparison of magnetic resonance imaging and transrectal ultrasound informed prostate biopsy for prostate cancer diagnosis in biopsy naïve men: a systematic review and meta-analysis. J Urol. 2020;203:1085–93. https://doi.org/10.1097/JU.0000000000000595.

    Article  PubMed  Google Scholar 

  15. Connor MJ, Eldred-Evans D, van Son M, Hosking-Jervis F, Bertoncelli Tanaka M, Reddy D, et al. A multicenter study of the clinical utility of nontargeted systematic transperineal prostate biopsies in patients undergoing pre-biopsy multiparametric magnetic resonance imaging. J Urol. 2020;204:1195–201. https://doi.org/10.1097/JU.0000000000001184.

    CAS  Article  PubMed  Google Scholar 

  16. Hou Y, Jiang KW, Zhang J, Bao ML, Shi HB, Qu JR et al. A clinical available decision support scheme for optimizing prostate biopsy based on mpMRI. Prostate Cancer Prostatic Dis. 2022. https://doi.org/10.1038/s41391-021-00489-z.

  17. Bass EJ, Pantovic A, Connor MJ, Loeb S, Rastinehad AR, Winkler M, et al. Diagnostic accuracy of magnetic resonance imaging targeted biopsy techniques compared to transrectal ultrasound guided biopsy of the prostate: a systematic review and meta-analysis. Prostate Cancer Prostatic Dis. 2021. https://doi.org/10.1038/s41391-021-00449-7.

  18. Porpiglia F, Bertolo R, Manfredi M, De Luca S, Checcucci E, Morra I, et al. Total anatomical reconstruction during robot-assisted radical prostatectomy: implications on early recovery of urinary continence. Eur Urol 2016;69:485–95. https://doi.org/10.1016/j.eururo.2015.08.005.

    Article  PubMed  Google Scholar 

  19. Manfredi M, Checcucci E, Fiori C, Garrou D, Aimar R, Amparore D, et al. Total anatomical reconstruction during robot-assisted radical prostatectomy: focus on urinary continence recovery and related complications after 1000 procedures. BJU Int. 2019;124:477–86. https://doi.org/10.1111/bju.14716.

    Article  PubMed  Google Scholar 

  20. Barentsz JO, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G, et al. European Society of Urogenital Radiology. ESUR prostate MR guidelines 2012. Eur Radiol. 2012;22:746–57. https://doi.org/10.1007/s00330-011-2377-y.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Barentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, et al. Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol. 2016;69:41–9. https://doi.org/10.1016/j.eururo.2015.08.038.

    Article  PubMed  Google Scholar 

  22. Porpiglia F, De Luca S, Passera R, De Pascale A, Amparore D, Cattaneo G, et al. Multiparametric magnetic resonance/ultrasound fusion prostate biopsy: number and spatial distribution of cores for better index tumor detection and characterization. J Urol. 2017;198:58–64. https://doi.org/10.1016/j.juro.2017.01.036.

    Article  PubMed  Google Scholar 

  23. Moore CM, Kasivisvanathan V, Eggener S, Emberton M, Fütterer JJ, Gill IS, et al. Standards of reporting for MRI-targeted biopsy studies (START) of the prostate: recommendations from an International Working Group. Eur Urol. 2013;64:544–52. https://doi.org/10.1016/j.eururo.2013.03.030.

    Article  PubMed  Google Scholar 

  24. Montironi R, Lopez-Beltran A, Mazzucchelli R, Scarpelli M, Bollito E. Assessment of radical prostatectomy specimens and diagnostic reporting of pathological findings. Pathologica. 2001;93:226–32.

    CAS  PubMed  Google Scholar 

  25. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–9. https://doi.org/10.1016/s0895-4356(96)00236-3.

    CAS  Article  PubMed  Google Scholar 

  26. Long JS. Regression Models for categorical and limited dependent variables. Thousand Oaks, CA: Sage Publications; 1997.

    Google Scholar 

  27. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26:565–74. https://doi.org/10.1177/0272989X06295361.

    Article  Google Scholar 

  28. Peirce CS. The numerical measure of the success of predictions. Science. 1884;4:453–4. https://doi.org/10.1126/science.ns-4.93.453-a.

    CAS  Article  PubMed  Google Scholar 

  29. Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom J. 2008;50:419–30. https://doi.org/10.1002/bimj.200710415.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol. 2016;74:167–76. https://doi.org/10.1016/j.jclinepi.2015.12.005.

    Article  PubMed  Google Scholar 

  31. Cimino S, Reale G, Castelli T, Favilla V, Giardina R, Russo GI, et al. Comparison between Briganti, Partin and MSKCC tools in predicting positive lymph nodes in prostate cancer: a systematic review and meta-analysis. Scand J Urol. 2017;51:345–50. https://doi.org/10.1080/21681805.2017.1332680.

    Article  PubMed  Google Scholar 

  32. De Nunzio C, Lombardo R, Baldassarri V, Cindolo L, Bertolo R, Minervini A, et al. Rotterdam mobile phone app including MRI data for the prediction of prostate cancer: a multicenter external validation. Eur J Surg Oncol. 2021;47:2640–5. https://doi.org/10.1016/j.ejso.2021.04.033.

    Article  PubMed  Google Scholar 

  33. Gallagher KM, Christopher E, Cameron AJ, Little S, Innes A, Davis G, et al. Four-year outcomes from a multiparametric magnetic resonance imaging (MRI)-based active surveillance programme: PSA dynamics and serial MRI scans allow omission of protocol biopsies. BJU Int. 2019;123:429–38. https://doi.org/10.1111/bju.14513.

    Article  PubMed  Google Scholar 

  34. Klotz L, Chin J, Black PC, Finelli A, Anidjar M, Bladou F, et al. Comparison of multiparametric magnetic resonance imaging-targeted biopsy with systematic transrectal ultrasonography biopsy for biopsy-naive men at risk for prostate cancer: a phase 3 randomized clinical trial. JAMA Oncol. 2021. https://doi.org/10.1001/jamaoncol.2020.7589.

  35. Briganti A, Larcher A, Abdollah F, Capitanio U, Gallina A, Suardi N, et al. Updated nomogram predicting lymph node invasion in patients with prostate cancer undergoing extended pelvic lymph node dissection: the essential importance of percentage of positive cores. Eur Urol. 2012;61:480–7.

    Article  Google Scholar 

  36. Gandaglia G, Fossati N, Zaffuto E, Bandini M, Dell’Oglio P, Bravi CA, et al. Development and internal validation of a novel model to identify the candidates for extended pelvic lymph node dissection in prostate cancer. Eur Urol. 2017;72:632–40.

    Article  Google Scholar 

  37. Memorial Sloan Kettering Cancer Center. Dynamic prostate cancer nomogram: coefficients. www.mskcc.org/nomograms/prostate/pre-op/coefficients.

  38. De Luca S, Passera R, Fiori C, Garrou D, Manfredi M, Aimar R, et al. The role of side-specific biopsy and dominant tumor location at radical prostatectomy in predicting the side of nodal metastases in organ confined prostate cancer: is lymphatic spread really unpredictable? Minerva Urol Nefrol. 2019;71:146–53. https://doi.org/10.23736/S0393-2249.18.03286-1.

    Article  PubMed  Google Scholar 

  39. Morozov A, Barret E, Veneziano D, Grigoryan V, Salomon G, Fokin I, In collaboration with ESUT-YAUWP Group, et al. A systematic review of nerve-sparing surgery for high-risk prostate cancer. Minerva Urol Nefrol. 2021. https://doi.org/10.23736/S0393-2249.20.04178-8.

Download references

Author information

Authors and Affiliations

Authors

Contributions

CF: Conceptualization, Methodology, Review. EC: Data Analysis, Manuscript Writing. IS: Methodology, Statistical Analysis. DA: Manuscript writing, Data Analysis. SDC: Data Collection. AP: Data Collection. SG: Data Collection. GV: Data Collection, Language review. MS: Data Collection. FP: Data Collection. PV: Data Collection, Language review. MM: Review, Supervision. SDL: Supervision. RA: Review. GM: Methodology, Statistical Analysis. FP: Conceptualization, Methodology, Review

Corresponding author

Correspondence to Enrico Checcucci.

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

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fiori, C., Checcucci, E., Stura, I. et al. Development of a novel nomogram to identify the candidate to extended pelvic lymph node dissection in patients who underwent mpMRI and target biopsy only. Prostate Cancer Prostatic Dis (2022). https://doi.org/10.1038/s41391-022-00565-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41391-022-00565-y

Search

Quick links