This study examined the prognostic differences between radical prostatectomy (RP), transurethral resection of the prostate (TURP), laser ablation (LA), and cryoablation (CA) in early-stage prostate cancer (T1-2N0M0) and developed a survival prediction tool based on these four surgical modalities.

Using the SEER database (2010–2020), we identified T1-2N0M0 prostate cancer patients treated with RP, TURP, LA, or CA. To reduce baseline imbalances, 1:1 propensity score matching (PSM) with a caliper of 0.1 was applied. Multivariate Cox regression identified independent prognostic factors, which were used for stratification. Kaplan-Meier analysis assessed long-term survival across treatment groups. After PSM, patients were split into training and validation cohorts (7:3 ratio). Lasso and Cox regression were used to integrate independent prognostic factors for OS into a nomogram, evaluated using calibration curves, decision curve analysis (DCA), and risk stratification.

A total of 92,670 patients were included, with significant baseline differences reduced in 3,088 patients after PSM. Survival analysis revealed that patients in the TURP and LA groups had worse OS and CSS compared to CA. RP did not demonstrate a significant OS advantage over CA. Multivariate Cox regression identified age, Gleason score, and PSA as independent prognostic factors. Kaplan-Meier analysis demonstrated that RP and CA provided superior long-term survival outcomes. A nomogram based on these factors achieved good calibration and discrimination (C-index: 0.732 in the training set). Decision curve analysis confirmed its clinical utility. Risk stratification showed significant survival differences among low-, medium-, and high-risk groups, underscoring the nomogram’s clinical relevance.

RP remains the preferred treatment for T1-2N0M0 prostate cancer due to its significant survival benefits. CA is a viable alternative for selected patients, particularly those with PSA ≤4 ng/mL, age >72, or Gleason score ≤7. The nomogram facilitates personalized treatment planning, optimizing prognosis in early-stage prostate cancer.