Prostate Cancer Nomograms Are Still Alive

Abstract

Prostate cancer (PCa) is the second most common cancer worldwide, with an estimated incidence of 30.7 new cases per 100,000 men in 2020 [1]. The highest incidence rates were reported in Northern and Western Europe, the Caribbean, Australia, and New Zealand [2]. PCa shows a steady increase with age but a non-neglectable number of men aged 20–50 years are currently diagnosed and die of PCa, with an estimated worldwide number of deaths of 2770 men in 2020 in this range of age [1]. Li et. al established nomograms to predict overall and cancer-specific survival in PCa patients aged <50 years at diagnosis [3]. The authors used data from The Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. 8259 patients were included and randomly divided into two groups (training and validation group) at a ratio of 7:3. In multivariate analysis, race, marital status, summary stage, American Joint Committee on Cancer (AJCC) stage, lymph node stage, biopsy Gleason score, and treatment were significantly associated with overall survival, whereas AJCC stage, lymph node stage, biopsy Gleason score, and treatment were associated with cancer-specific survival. The above variables were used to build nomograms aiming to predict 5-, 8-, and 10-year overall and cancer-specific survival. Using receiver operating characteristic curve analysis, the authors found that the discriminative ability of their nomograms was moderately accurate in predicting overall survival and highly accurate in predicting cancer-specific survival. The performance of nomograms was confirmed by the validation group. Nomograms are currently widely used in urologic oncology, particularly in the decision-making process and patient counseling [4]. In the diagnostic phase, nomograms using clinical parameters are useful to assess the risk of clinically significant PCa at biopsy [4], and nomograms that include multiparametric magnetic resonance imaging data have been recently introduced [5,6]. Post-diagnosis decision-making is a crucial aspect in PCa patients, mostly to stratify the risk of progression and to offer advice on the possible management of clinically localized disease [4]. Post-treatment prognostic nomograms were also developed for detecting patients at risk of lymph node invasion, presence of positive surgical, margin, extracapsular extension, and biochemical recurrence after radical prostatectomy [7]. Prognostic nomograms are more accurate than staging systems in predicting the progression of PCa. Being continuous prediction methods, nomograms are more appropriate to predict progression compared with staging systems, because grouping patients predisposes to reduce the predictive accuracy of a prognostic model [8]. In fact, Liu et al. confirmed that the constructed nomograms demonstrated to have a better predictive ability in predicting overall and cancer-specific survival than AJCC TNM and Gleason score [3]. Notably, their analysis failed to show any influence of baseline prostate-specific antigen (PSA) in predicting survival. This might be correlated to a large number of patients in the validation group who had a PSA ≥20 ng/ml at diagnosis (94%). Despite this, more than half of included patients were biopsy Gleason score ≤ 6 (55.6%), and a minority of cases were T3-4 (12.3%), had lymph node (19.2%), or systemic metastasis (2.0%). Still using the SEER database data between 2010 and 2015, Hu et al. found that PSA was significantly associated with poor prognosis in 23,730 men younger than 55 years [9]. The authors developed an effective prognostic nomogram that included PSA, marital status, AJCC stage, Gleason score, and surgery to predict 1-,3-, and 5-year overall survival. This discrepancy can be partially related to the lower number of included patients in Liu’s model which may lead to an overfitting model. Finally, Liu’s nomograms lack data on systemic therapy, making them inappropriate for metastatic patients requiring such a therapy. Despite the aforementioned limitations, the authors should be congratulated for providing a useful tool to assess PCa prognosis in very young men, covering a gap in the literature. We hope that the authors wish to implement their work by building a desktop or handheld software for a fast and easy application of their nomograms in daily clinical practice. The era of nomograms is not over, PCa nomograms are still alive.