Background: The relapse rate in patients with clinical stage I (CSI) seminomatous germ cell tumor of the testis (SGCTT) who were undergoing surveillance after radical orchidectomy is 4-30%, depending on tumor size and rete testis invasion (RTI). However, the level of evidence supporting the use of both risk factors in clinical decision-making is low.
Objective: We aimed to identify the most important prognostic factors for relapse in CSI SGCTT patients.
Design, setting, and participants: Individual patient data for 1016 CSI SGCTT patients diagnosed between 1994 and 2019 with normal postorchidectomy serum tumor marker levels and undergoing surveillance were collected from nine institutions.
Outcome measurements and statistical analysis: Multivariable Cox proportional hazard regression models were fit to identify the most important prognostic factors. The primary endpoint was the time to first relapse by imaging and/or markers. Relapse probabilities were estimated by the Kaplan-Meier method.
Results and limitations: After a median follow-up of 7.7 yr, 149 (14.7%) patients had relapsed. Categorical tumor size (≤2, >2-5, and >5 cm), presence of RTI, and lymphovascular invasion were used to form three risk groups: low (56.4%), intermediate (41.3%), and high (2.3%) risks with 5-yr cumulative relapse probabilities of 8%, 20%, and 44%, respectively. The model outperformed the currently used model with tumor size ≤4 versus >4 cm and presence of RTI (Harrell’s C index 0.65 vs 0.61). The low- and intermediate-risk groups were validated successfully in an independent cohort of 285 patients.
Conclusions: The risk of relapse after radical orchidectomy in CSI SGCTT patients under surveillance is low. We propose a new risk stratification model that outperformed the current model and identified a small subgroup with a high risk of relapse.
Patient summary: The risk of relapse after radical orchidectomy in patients with clinical stage I seminomatous germ cell tumor of the testis is low. We propose a new risk stratification model that outperformed the current model and identified a small subgroup with a high risk of relapse.