Predicting the probability of pT3 or higher pathological stage at radical prostatectomy: COVID19-specific considerations

Background We tested whether a model identifying prostate cancer (PCa) patients at risk of pT3-4/pN1 can be developed for use during COVID19 pandemic, in order to guarantee appropriate treatment to patients harboring advanced disease patients without compromising sustainability of care delivery. Methods Within the Surveillance, Epidemiology and End Results database 2010-2016, we identified 27,529 patients with localized PCa and treated with radical prostatectomy. A multivariable logistic regression model predicting presence of pT3-4/pN1 disease was fitted within a development cohort (n=13,977, 50.8%). Subsequently, external validation (n=13,552, 49.2%) and head-to-head comparison with NCCN risk group stratification was performed. Results In model development, age, PSA, biopsy Gleason Grade Group (GGG) and percentage of positive biopsy cores were independent predictors of pT3-4/pN1 stage. In external validation, prediction of pT3-4/pN1 with novel nomogram was 74% accurate versus 68% for NCCN risk group stratification. Nomogram achieved better calibration and showed net-benefit over NCCN risk group stratification in decision curve analyses. The use of nomogram cut-off of 49% resulted in pT3-4/pN1 rate of 65%, instead of the average 35%. Conclusion The newly developed, externally validated nomogram predicts presence of pT3-4/pN1 better than NCCN risk group stratification and allows to focus radical prostatectomy treatment on individuals at highest risk of pT3-4/pN1.

[1]  A. Villers,et al.  Nomogram predicting adverse pathology outcome on radical prostatectomy in low-risk prostate cancer men. , 2022, Urology.

[2]  S. Martinenghi,et al.  A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors , 2022, Frontiers in Medicine.

[3]  F. Saad,et al.  Improving the stratification of intermediate risk prostate cancer. , 2021, Minerva urology and nephrology.

[4]  M. Graefen,et al.  Improving the Stratification of Patients With Intermediate-risk Prostate Cancer. , 2020, Clinical genitourinary cancer.

[5]  S. Loeb,et al.  A Systematic Review on Guidelines and Recommendations for Urology Standard of Care During the COVID-19 Pandemic , 2020, European Urology Focus.

[6]  M. Babjuk,et al.  European Association of Urology Guidelines Office Rapid Reaction Group: An Organisation-wide Collaborative Effort to Adapt the European Association of Urology Guidelines Recommendations to the Coronavirus Disease 2019 Era☆ , 2020, European Urology.

[7]  P. Dasgupta,et al.  Global challenges to urology practice during the COVID‐19 pandemic , 2020, BJU international.

[8]  Fabricio Rodrigues Torres de Carvalho,et al.  Impact of the COVID-19 Pandemic on the Urologist’s clinical practice in Brazil: a management guideline proposal for low- and middle-income countries during the crisis period , 2020, International braz j urol : official journal of the Brazilian Society of Urology.

[9]  A. Moinzadeh,et al.  Considerations in the Triage of Urologic Surgeries During the COVID-19 Pandemic , 2020, European Urology.

[10]  Lara S. MacLachlan,et al.  Triaging Office-Based UrologY Procedures During the COVID-19 Pandemic , 2020, The Journal of urology.

[11]  G. Haber,et al.  Recommendations for Tiered Stratification of Urological Surgery Urgency in the COVID-19 Era , 2020, The Journal of urology.

[12]  R. Fisher,et al.  A War on Two Fronts: Cancer Care in the Time of COVID-19 , 2020, Annals of Internal Medicine.

[13]  T. Burki Cancer care in the time of COVID-19 , 2020, The Lancet Oncology.

[14]  V. Ficarra,et al.  Urology practice during COVID-19 pandemic. , 2020, Minerva urologica e nefrologica = The Italian journal of urology and nephrology.

[15]  F. Saad,et al.  Development and Validation of a Lookup Table for the Prediction of Metastatic Prostate Cancer According to Prostatic-specific Antigen Value, Clinical Tumor Stage, and Gleason Grade Groups. , 2020, European urology oncology.

[16]  F. Montorsi,et al.  The Key Combined Value of Multiparametric Magnetic Resonance Imaging, and Magnetic Resonance Imaging-targeted and Concomitant Systematic Biopsies for the Prediction of Adverse Pathological Features in Prostate Cancer Patients Undergoing Radical Prostatectomy. , 2020, European urology.

[17]  P. Stattin,et al.  Predicting Prostate Cancer Death with Different Pretreatment Risk Stratification Tools: A Head-to-head Comparison in a Nationwide Cohort Study. , 2020, European urology.

[18]  A. D'Amico,et al.  Prostate Cancer, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology. , 2019, Journal of the National Comprehensive Cancer Network : JNCCN.

[19]  K. Hoffman,et al.  Risk of Upgrading and Upstaging Among 10 000 Patients with Gleason 3+4 Favorable Intermediate-risk Prostate Cancer. , 2017, European urology focus.

[20]  F. Montorsi,et al.  Relative Contribution of Sampling and Grading to the Quality of Prostate Biopsy: Results from a Single High-volume Institution. , 2020, European urology oncology.

[21]  A. Tewari,et al.  Development and internal validation of a side‐specific, multiparametric magnetic resonance imaging‐based nomogram for the prediction of extracapsular extension of prostate cancer , 2018, BJU international.

[22]  A. Giuliani,et al.  Clinical Utility of Multiparametric Magnetic Resonance Imaging as the First-line Tool for Men with High Clinical Suspicion of Prostate Cancer. , 2018, European urology oncology.

[23]  D. Margolis,et al.  MRI‐Targeted or Standard Biopsy for Prostate‐Cancer Diagnosis , 2018, The New England journal of medicine.

[24]  M. Terris,et al.  Modified risk stratification grouping using standard clinical and biopsy information for patients undergoing radical prostatectomy: Results from SEARCH , 2017, The Prostate.

[25]  F. Montorsi,et al.  Development and Internal Validation of a Novel Model to Identify the Candidates for Extended Pelvic Lymph Node Dissection in Prostate Cancer. , 2017, European urology.

[26]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[27]  O. Alfieri,et al.  TCT-682 Comparison of conventional surgery and transapical transcatheter approach for paravalvular leak closure in high risk patients: results from a single high volume institution. , 2013 .

[28]  J. Epstein,et al.  The value of mandatory second opinion pathology review of prostate needle biopsy interpretation before radical prostatectomy. , 2010, The Journal of urology.

[29]  E. Elkin,et al.  Decision Curve Analysis: A Novel Method for Evaluating Prediction Models , 2006, Medical decision making : an international journal of the Society for Medical Decision Making.

[30]  M. Kattan,et al.  Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. , 2006, Journal of the National Cancer Institute.

[31]  M. Kattan,et al.  Predicting the presence and side of extracapsular extension: a nomogram for staging prostate cancer. , 2004, The Journal of urology.

[32]  J. Sengupta The Nonparametric Approach , 1989 .

[33]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.