Introduction:

Active surveillance (AS) is a validated strategy for managing small renal masses (SRMs), yet predicting delayed intervention (DI) remains challenging. Tumor size and growth rate are routinely assessed, but their combined effect on DI risk is unclear. Prior studies have evaluated these parameters independently; however, we hypothesize that an interaction between static (size) and dynamic (growth) tumor characteristics may better capture biologic aggressiveness and clinical decision points. We aimed to evaluate the impact of this interaction on the hazard of DI among patients enrolled in the DISSRM registry.

Methods:

We analyzed 511 patients with SRMs managed under AS in the DISSRM registry. Cox proportional hazards models were used to assess predictors of DI, including tumor size, growth rate (GR), age, comorbidity (CCI), and sex. An interaction term between size and GR was included. Smoothed hazard functions were generated to visualize temporal risk patterns across size-growth strata.

Results:

Of 511 patients, 79 (15.5%) underwent DI. Tumors 2–3 cm (HR 2.03, 95% CI: 1.19–3.47) and >3 cm (HR 2.78, CI: 1.41–5.45) were associated with higher hazard versus <2 cm. GR ≥5 cm/year (HR 4.21, CI: 1.82–9.75) was the strongest predictor. The size × GR interaction significantly improved model fit. Smoothed hazard plots showed that tumors ≥2 cm with rapid growth exhibited the steepest and earliest risk trajectories. Conversely, slow-growing tumors (<0.5 cm/year) maintained low hazard regardless of size.

Conclusion:

Tumor size and growth rate interact synergistically to predict delayed intervention among AS-managed SRMs. Patients with tumors ≥2 cm and GR ≥5 cm/year represent the highest-risk group and may benefit from earlier intervention. These findings support using combined tumor kinetics and burden for risk-adapted surveillance protocols.

Funding: N/A