Establishing a model predicting Gleason grade group upgrading in prostate cancer

• Published in: Translational andrology and urology Vol. 13; no. 8; pp. 1378 - 1387
• Main Authors: Chen, Jian, Chen, Qiming, Wang, Ze, Yan, Xuzhi, Wang, Yapeng, Zhang, Yao, Zhang, Jun, Xu, Jing, Ma, Qiang, Zhong, Peng, Zhang, Dianzheng, Liu, Qiuli, Lan, Weihua, Jiang, Jun
• Format: Journal Article
• Language: English
• Published: China AME Publishing Company 31.08.2024
• Subjects: Original; systemic immune-inflammation index (SII); prediction model; Prostate cancer (PCa); Gleason grade group upgrading
• ISSN: 2223-4691; 2223-4683; 2223-4691
• DOI: 10.21037/tau-24-155

Gleason grade group (GG) upgrading is associated with increased biochemical recurrence (BCR), local progression, and decreased cancer-specific survival (CSS) in prostate cancer (PCa). However, descriptions of the risk factors of GG upgrading are scarce. The objective of this study was to identify risk factors and establish a model to predict GG upgrading. There were 361 patients with PCa who underwent radical prostatectomy between May 2011 and February 2022 enrolled. Univariate and multivariate logistic regression analyses were identified and nomogram further narrowed down the contributing factors in GG upgrading. The correction curve and decision curve were used to assess the model. In the overall cohort, 141 patients had GG upgrading. But the subgroup cohort (GG ≤2) showed that 68 patients had GG upgrading. Multivariate logistic regression analysis showed that in the overall cohort, total prostate-specific antigen (tPSA) ≥10 ng/mL, systemic immune-inflammation index (SII) >379.50, neutrophil-lymphocyte ratio (NLR) >2.13, the GG of biopsy ≥3, the number of positive cores >3 were independent risk factors in GG upgrading. In the cohort of biopsy GG ≤2, multivariate logistic regression showed that the tPSA ≥10 ng/mL, SII >379.50 and the number of positive cores >3 were independent risk factors in GG upgrading. A novel model predicting GG upgrading was established based on these three parameters. The area under the curve (AUC) of the prediction model was 0.759. The C-index of the nomogram was 0.768. The calibration curves of the model showed good predictive performance. Clinical decision curves indicated clinical benefit in the interval of 20% to 90% of threshold probability and good clinical utility. Combined levels of tPSA, SII and the positive biopsy cores distinguish patients with high-risk GG upgrading in the group of biopsy GG ≤2 and are helpful in the decision of treatment plans.