Nomogram Predicting Prostate Cancer–specific Mortality for Men with Biochemical Recurrence After Radical Prostatectomy

Abstract

Background The natural history of prostate-specific antigen (PSA)-defined biochemical recurrence (BCR) of prostate cancer (PCa) after definitive local therapy is highly variable. Validated prediction models for PCa-specific mortality (PCSM) in this population are needed for treatment decision-making and clinical trial design. Objective To develop and validate a nomogram to predict the probability of PCSM from the time of BCR among men with rising PSA levels after radical prostatectomy. Design, setting, and participants Between 1987 and 2011, 2254 men treated by radical prostatectomy at one of five high-volume hospitals experienced BCR, defined as three successive PSA rises (final value >0.2 ng/ml), single PSA >0.4 ng/ml, or use of secondary therapy administered for detectable PSA >0.1 ng/ml. Clinical information and follow-up data were modeled using competing-risk regression analysis to predict PCSM from the time of BCR. Intervention Radical prostatectomy for localized prostate cancer and subsequent PCa BCR. Outcome measurements and statistical analysis PCSM. Results and limitations The 10-yr PCSM and mortality from competing causes was 19% (95% confidence interval [CI] 16–21%) and 17% (95% CI 14–19%), respectively. A nomogram predicting PCSM for all patients had an internally validated concordance index of 0.774. Inclusion of PSA doubling time (PSADT) in a nomogram based on standard parameters modestly improved predictive accuracy (concordance index 0.763 vs 0.754). Significant parameters in the models were preoperative PSA, pathological Gleason score, extraprostatic extension, seminal vesicle invasion, time to PCa BCR, PSA level at PCa BCR, and PSADT (all p < 0.05). Conclusions We constructed and validated a nomogram to predict the risk of PCSM at 10 yr among men with PCa BCR after radical prostatectomy. The nomogram may be used for patient counseling and the design of clinical trials for PCa. Patient summary For men with biochemical recurrence of prostate cancer after radical prostatectomy, we have developed a model to predict the long-term risk of death from prostate cancer.

Subclassification of high-risk clinically organ-confined prostate cancer for early cancer-specific mortality after radical prostatectomy

“…In addition to the shorter postoperative survivals, patients with very high-risk PCa had significantly earlier clinical progression and PCSM from the time of BCR, which is consistent with previous reports showing a significant association between shorter time to BCR and post-BCR clinical progression (25,26) or PCSM (27)(28)(29). However, our subclassification is clearly different from previously proposed ones in that it provides predictive information preoperatively, which yields strong advantages as described above.…”

Impact of a preoperatively estimated prostate volume using transrectal ultrasonography on surgical and oncological outcomes in a single surgeon’s experience with robot-assisted radical prostatectomy

“…On the other hand, regarding oncological outcomes, other single-institution experiences of RARP including several surgeons' experiences found that EPE and PSM were less likely to be found in patients with large prostates [4]. The tumor's EPE and PSM are well-established prognostic factors that impact BCR and long-term survival after radical prostatectomy [15][16][17][18][19]. In their RARP series, Skolarus et al [20] also reported that patients with an enlarged prostate had lower PSMs and Gleason scores.…”

Risk Stratification of Prostate Cancer: Integrating Multiparametric MRI, Nomograms and Biomarkers

“…Locally advanced disease includes extracapsular extension (ECE), seminal vesicle invasion (SVI) or local pelvic structures, whereas distant disease reflects lymph node involvement (LNI), bone or visceral organ metastasis [73][74][75]. Predictive staging nomograms allow the urologist to prognosticate clinical outcomes for the patient [76]. The Partin tables are the classic algorithm that combines clinical stage, preoperative serum PSA and biopsy Gleason grade to predict pathological stage.…”