Correlations Between Preoperative Indicators and Postoperative Histopathological Outcomes in Prostate Cancer Patients

Objective: Prostate cancer continues to pose a significant health challenge, with diverse prognoses influenced by preoperative and postoperative assessments. This study aims to elucidate the correlation between preoperative clinical indicators and postoperative histopathological outcomes to enhance prognostic models. The primary objective of this study is to investigate the predictive value of preoperative factors, such as age, Prostate-Specific Antigen (PSA) level, prostate volume (PV), and tumor volume (TV), on postoperative outcomes, specifically focusing on extracapsular invasion (ECI), seminal vesicle invasion (SVI), and positive surgical margins (PSM). Materials and Methods: We retrospectively analyzed the data of 63 patients with prostate cancer who underwent radical prostatectomy. Preoperative clinical data, including age, PSA level, PV, and TV, were collected. Postoperative histopathological data were gathered for ECI, SVI, and PSM. Statistical analyses, including correlation coefficients and median comparisons, were employed to identify significant predictors of postoperative outcomes. Results: The cohort had a mean age of 64.1 years, with PSA levels ranging from 3.65 to 112 ng/ml. Patients with ECI had a median PSA of 14.9 ng/ml, whereas those without had 8.2 ng/ml (p=0.001). Median PV and TV were significantly higher in patients with ECI (PV: 55 cc, TV: 8.07 cc) than in those without ECI (PV: 49 cc, TV: 4.25 cc, p=0.001). Similar significant differences were noted for SVI and PSM, with higher PSA, PV, and TV values in patients with these features (p≤0.042). Age did not significantly affect the outcomes. Conclusion: Preoperative PSA level, prostate volume, and tumor volume were significant predictors of adverse postoperative histopathological features in patients with prostate cancer. These findings highlight the need for a multifactorial approach in preoperative evaluation and advocate the development of enhanced predictive models for improved clinical decision-making and patient management.