A PSM after radical prostatectomy is a factor that contributes to adverse clinical outcomes, which can be affected by preoperative factors and surgical process.[3–6] Therefore, practical tools are of utmost importance so that best candidates for surgeries can be selected and proper surgical process can be planned. Previous studies have identified several predictive factors for positive margins. The most widely recognized one was the pathologic stage. In a meta-analysis of oncologic outcomes after robot-assisted radical prostatectomy, the mean PSM rate was 9% in pT2 cancers, 37% in pT3 cancers, and 50% in pT4 cancers, suggesting that a more extensive tumor has a higher risk of PSM. Other factors have also been identified as risk factors, including a higher BMI, serum PSA level, PSA density, percentage of cancer in the biopsy specimens,[19,20] Gleason Scores, and a lower prostate weight and volume. These observations indicated that a larger tumor and smaller prostate, which lead to a higher tumor-to-prostate ratio, have a positive association with a PSM. In addition, the surgical technique and surgical experience are also important. In the meta-analysis, several surgeon-related characteristics (e.g., prior surgical experience) or procedure-related issues (e.g., type of nerve-sparing approach) may play a major role in the PSM rates. Unfortunately, these studies failed to give a predictive model for the PSM, which may confuse surgeons making clinical decisions. A nomogram is a user-friendly tool with a graphic interface, which is widely used for clinical decisions. Herein, we developed one from a logistic regression model based on the preoperative information, which can be helpful in clinical settings.
In our research, the PSM rate was 34.0%, which was higher than those reported in Western countries. In the meta-analysis conducted by Novara et al, the mean PSM rate was 15%. However, Xu et al and Yang et al reported PSM rates of 44.2% and 29.1% in the Chinese population, which were comparable to our study. There are several possible reasons. First, all patients in our study were treated by LRP without robot assistance, while other study only included robot-assisted laparoscopic LP, and it had been proved that robot assisted prostatectomy may reduce the incidence of a PSM.[3,26,27] In a study carried out by Porcaro et al, which included many open surgery patients, the PSM rate was comparable with ours; these may also demonstrate this hypothesis. Second, in China, patients usually get diagnosed at a later stage and as a result, patients in Chinese cohorts always tend to have a higher PSA as well.[24,29] Differences in the genetic and racial make-up between the Chinese cohort and Western population may also lead to different pathologic outcomes.
After selection with the backward method, the percentage of positive cores, clinical stage, fPSA/tPSA, and age were included in the final regression model, which approximately conformed to the contemporary practice.
As mentioned before, the pathologic stage is a well-recognized predictor of a PSM. However, it is not a preoperative factor. A factor similar to it is the clinical stage. Coelho et al evaluated preoperative factors associated with PSM and concluded that clinical stage was the only independent predictive factor for PSM. In our study, clinical stage was also a powerful predictor because it had the highest AUC in all variables. We also noticed that portion of clinical stage T1 was much less than the Western studies. The first reason might be that we used both magnetic resonance imaging (MRI) and digital rectal examination to determine the clinical stage, while many studies only used digital rectal examination.[31,32] In our study, a patient would be considered to have T1 cancer with neither palpable abnormalities nor abnormal MRI findings. As mentioned above, late diagnose and racial difference may also contribute to this situation, as a result, studies conducted in China shared a similar portion of clinical stages.
Because percentage of positive cores is directly associated with the tumor proportion, it is not surprising to find it is a strong predictor in both our univariate and multivariate regression models. There are a few studies supporting this point. Tuliao et al's study suggested that the number of preoperative positive biopsy cores was a predictor of PSM. Yang et al reported that PSM was more common in patients with a more positive core number and bilateral positive cores.
In addition, we found that fPSA/tPSA was a strong predictor for a PSM and it was included in the final model. Although evidence to prove this relationship was insufficient, the work of Sfoungaristos et al showed that those with a lower fPSA/tPSA tended to yield a PSM (P = 0.138). One possible explanation was that the fPSA/tPSA indirectly reflected the proportion of the tumor tissue and, therefore, had a negative influence on surgical margins.
Unfortunately, there were also several limitations to our study. First, because of incomplete radiology, it was difficult to record the prostate volume and the clinical staging was mainly based on the existing report. Second, the nomogram was calibrated with 1000 bootstrap resamples, which might result in overfitting. As a result, further studies to validate our model with external data are needed. Third, because our study was a retrospective one, selection bias was inevitable.
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