Locally advanced prostate cancer treated with neoadjuvant therapy combined with surgery: a multicenter retrospective cohort analysis.

Abstract

Recent data from clinical trials have shown that neoadjuvant therapies significantly improve the pathological outcomes of prostate cancer patients. This study aimed to assess the specific pathological and prognostic effects of these therapies in a real-world, multicenter cohort. Additionally, we explored how factors such as the duration of neoadjuvant therapy and pretreatment imaging modality impact overall treatment outcomes within this therapeutic framework. Data were collected from 407 patients with locally advanced prostate cancer (LAPC) who underwent radical prostatectomy following neoadjuvant therapy. Kaplan‒Meier estimates were used to evaluate the four primary clinical endpoints. The log-rank test was used to assess whether significant differences existed between patients grouped according to neoadjuvant therapy duration and pretreatment imaging modality. After a median follow-up period of 36 months, the median progression-free survival (PFS) for the entire cohort was 19 months. An analysis of different durations of neoadjuvant therapy revealed that compared with a 3-month regimen, a 6-month regimen was significantly associated with a greater extent of pathological downstaging and more favorable values for drug response indicators (Pearson test, P = 0.018). Additionally, the 6-month regimen significantly improved the clinical endpoints of PFS (log-rank test, P = 0.0075) and metastasis-free survival (MFS; log-rank test, P = 0.0069). Kaplan‒Meier analysis of patients grouped according to preoperative imaging modality revealed that the use of 68Ga-labeled prostate-specific membrane antigen-directed positron emission tomography/computed tomography (68Ga-PSMA PET/CT) before treatment, as opposed to traditional imaging, led to significant improvements in the clinical endpoints of PFS (log-rank test, P = 0.0059) and radiographic progression-free survival (rPFS; log-rank test, P = 0.016).