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Impact of PI-RADS Score Combined with SII on Pathological Upgrading in Patients with Localized Prostate Cancer After Radical Prostatectomy

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Abstract Purpose To investigate the combined effect of PI-RADS v2.1 scores and the systemic immune-inflammation index (SII) on predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy. Methods The study collected various clinical indicators, imaging examination results, serum inflammation marker levels, and pathological examination results of 76 patients with localized prostate cancer who underwent prostate biopsy followed by radical prostatectomy. Patients were divided into two subgroups based on changes in their International Society of Urological Pathology (ISUP) grades before and after surgery: the pathological upgrading group and the non-upgrading group. Univariate and multivariate logistic regression analyses were used to assess independent risk factors for pathological upgrading. The effectiveness of single indicators, imaging inflammation indicators, traditional clinical pathology indicators, and multi-indicator combined prediction models in predicting pathological upgrading was evaluated using receiver operating characteristic (ROC) curves. Results Univariate and multivariate logistic regression analyses revealed that PI-RADS scores, SII, percentage of free PSA (%PSA), and the proportion of tumor tissue in biopsy samples were independent predictors of ISUP grade upgrading after radical prostatectomy. The area under the curve (AUC) values for single indicators PI-RADS, SII, %PSA, and biopsy tumor tissue proportion were 0.607, 0.711, 0.618, and 0.778, respectively. The AUC for the model combining imaging inflammation indicators (PI-RADS and SII) was 0.914. Conclusions The PI-RADS v2.1 score and SII are valuable in predicting pathological upgrading after radical prostatectomy in prostate cancer patients. Combining these indicators with traditional clinical pathology indicators significantly improves predictive performance.
Title: Impact of PI-RADS Score Combined with SII on Pathological Upgrading in Patients with Localized Prostate Cancer After Radical Prostatectomy
Description:
Abstract Purpose To investigate the combined effect of PI-RADS v2.
1 scores and the systemic immune-inflammation index (SII) on predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy.
Methods The study collected various clinical indicators, imaging examination results, serum inflammation marker levels, and pathological examination results of 76 patients with localized prostate cancer who underwent prostate biopsy followed by radical prostatectomy.
Patients were divided into two subgroups based on changes in their International Society of Urological Pathology (ISUP) grades before and after surgery: the pathological upgrading group and the non-upgrading group.
Univariate and multivariate logistic regression analyses were used to assess independent risk factors for pathological upgrading.
The effectiveness of single indicators, imaging inflammation indicators, traditional clinical pathology indicators, and multi-indicator combined prediction models in predicting pathological upgrading was evaluated using receiver operating characteristic (ROC) curves.
Results Univariate and multivariate logistic regression analyses revealed that PI-RADS scores, SII, percentage of free PSA (%PSA), and the proportion of tumor tissue in biopsy samples were independent predictors of ISUP grade upgrading after radical prostatectomy.
The area under the curve (AUC) values for single indicators PI-RADS, SII, %PSA, and biopsy tumor tissue proportion were 0.
607, 0.
711, 0.
618, and 0.
778, respectively.
The AUC for the model combining imaging inflammation indicators (PI-RADS and SII) was 0.
914.
Conclusions The PI-RADS v2.
1 score and SII are valuable in predicting pathological upgrading after radical prostatectomy in prostate cancer patients.
Combining these indicators with traditional clinical pathology indicators significantly improves predictive performance.

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