In the current study, we evaluated the association of tumor size with CSM as well as lymph node metastasis based on the SEER database. The PSCC patients with tumors > 30 mm were more likely to succumb to CSM than their counterparts with tumors ≤ 30 mm. Additionally, tumor size > 30 mm was significantly associated with an increased risk of lymph node metastasis.
Due to the rarity of PSCC, only scarce retrospective studies were published to investigate the prognostic value of tumor size. The existing limited literature revealed that tumor size was a prognostic predictor for overall survival, disease-free survival, and cancer recurrence [12–14]. Escande et al identified 201 patients with invasive penile carcinoma to examine the association between brachytherapy and long-term clinical outcomes. Patients with tumors greater than 4 cm had worse overall survival (HR = 2.2, 95% CI: 1.1–4.4, P = 0.028) and disease-free survival (HR = 2.4, 95% CI: 1.2–4.8, P = 0.01) than those with tumors at most 4 cm . Mao and colleagues reported that tumors larger than 30 mm predicted worse overall survival relative to those less than 30 mm for node-positive penile cancer (HR = 1.71, 95% CI: 1.28–2.28, P < 0.001) . However, to date, the association of tumor size and cancer-specific survival has not yet been fully explored. In a recent study, Cox regression models were used to identify the risk factors of cancer-specific survival for PSCC patients with node-positive disease. On multivariable analysis, tumor size ≥ 30 mm was significantly associated with worse cancer-specific survival . Of note, lymph node-negative patients were not involved in this study, and thus, we did not know the effect of tumor size on CSM for lymph node-negative patients. Additionally, the Cox regression model used was not suitable for analysis of competing events, which might overestimate the cumulative incidence of each event and lead to competitive risk bias . The primary interest of our study was CSM. The other causes leading to death, such as cardiovascular disease, suicide, and accident, were regarded as competing events, which could hinder the occurrence of the primary interest. In this case, the competing-risks model was more appropriate to deal with multiple end events. Subgroup analyses were also carried out to verify the robustness of the findings. The patients with tumors ≤ 30 mm had survival benefits than those with tumors > 30 mm among T1 and T3 subgroups. However, the statistical significance did not reach in T2 subgroup, which might be attributed to the relatively small sample size that reduced statistical power to detect small effects.
As we knew, the involvement of regional lymph nodes was a valuable pathological factor for predicting the prognosis of PSCC patients [15, 18]. Therefore, it was consequential to accurately recognize the risk factors associated with regional lymph node metastasis. Previous studies showed that tumor size was associated with an increased likelihood of regional lymph node metastasis for breast cancer , thyroid cancer , and lung cancer . However, this topic did not reach a consensus on penile cancer. A study from Italy investigated the predictors of lymph node metastasis using 175 PSCC patients. The authors firstly used the Chi-square test to screen the clinical variables. Because of no statistical significance in univariable analysis, tumor size was not incorporated and analyzed in the multivariable analysis . Another study based on the Ontario Cancer Registry was performed to evaluate the predictive variables of lymph node metastasis. The cohort was composed of 380 PSCC patients, of whom 63 patients had pathologically confirmed lymph node metastasis. In univariable analysis, tumor size (> 3 cm vs. ≤3 cm) was associated with an increased risk of lymph node metastasis (P = 0.040). However, it was not statistically significant at the conventional level (5%) in multivariable analysis . Commonly, the reliable estimation of predictor effects needed at least 100 events. The requirement of sample size was at least 10 events per variable (EPV), and preferably 20 . In the two studies, the number of events was only 71 and 63, and the values of EPV were respectively 15 and 9, less than 20. The relatively small sample size and low values of EPV might decrease the statistical power and the reliability of these results to some extent. In our study, 271 of 1365 patients were confirmed with lymph node-positive disease, and the value of EPV in multivariable analysis exceeded 30. Therefore, our study had sufficient sample size and statistical power to obtain stable and reliable estimations of predictor effects. Our study showed tumors > 30 mm was significantly associated with an increased likelihood of lymph node metastasis compared to tumors ≤ 30 mm (OR = 1.46, 95%CI: 1.03–2.07, P = 0.034). Consistent with our findings, Chalya and colleagues identified 236 penile cancer patients from a medical center in Tanzania, of whom 154 patients had lymph node metastasis at diagnosis. The multivariable logistic regression analysis showed that tumor size was an independent predictor for lymph node metastasis (≥ 20 mm vs. <20 mm, OR = 2.9, 95% CI: 1.1–6.4, P = 0.011) . Kearns and collaborators reported that increasing tumor size was significantly associated with lymph node metastasis after adjusting T classification, tumor grade, and lymphovascular invasion (≥ 40 mm vs. <15 mm, OR = 2.9, 95% CI: 1.31–6.41, P = 0.009) as well .
The current study had its own advantages. The population-based SEER database was employed in this study, providing us a relatively large sample size, which was beneficial for the investigations on cancers with low incidence. In addition, this study systemically investigated the prognostic value of tumor size for PSCC by using multiple statistical methods, including restricted cubic splines, competing-risks models, and logistic regression models. These reliable results exhibited the potential prognostic value of tumor size in the development of a staging system for penile cancer. However, some limitations should be noted. First of all, the selection bias was inevitable due to the retrospective nature of this study. Second, although we had adjusted for many known patient factors, the estimation would be more accurate if more valuable covariables which were not recorded in the SEER database could be incorporated. Third, since the eligible patients were diagnosis in 2004–2015, the relatively short follow-up might influence the estimation of cumulative incidence of CSM.