A preoperative magnetic resonance imaging-based model to predict biochemical failure after radical prostatectomy.

To investigate if a magnetic resonance imaging (MRI)-based model reduced postoperative biochemical failure (BF) incidence in patients with prostate cancer (PCa). From June 2018 to January 2020, we retrospectively analyzed 967 patients who underwent prostate bi-parametric MRI and radical prostatectomy (RP). After inclusion criteria were applied, 446 patients were randomized into research (n = 335) and validation cohorts (n = 111) at a 3:1 ratio. In addition to clinical variables, MRI models also included MRI parameters. The area under the curve (AUC) of receiver operating characteristic and decision curves were analyzed. The risk of postoperative BF, defined as persistently high or re-elevated prostate serum antigen (PSA) levels in patients with PCa with no clinical recurrence. In the research (age 69 [63-74] years) and validation cohorts (age 69 [64-74] years), the postoperative BF incidence was 22.39% and 27.02%, respectively. In the research cohort, the AUC of baseline and MRI models was 0.780 and 0.857, respectively, with a significant difference (P < 0.05). Validation cohort results were consistent (0.753 vs. 0.865, P < 0.05). At a 20% risk threshold, the false positive rate in the MRI model was lower when compared with the baseline model (31% [95% confidence interval (CI): 9-39%] vs. 44% [95% CI: 15-64%]), with the true positive rate only decreasing by a little (83% [95% CI: 63-94%] vs. 87% [95% CI: 75-100%]). 32 of 100 RPs can been performed, with no raise in quantity of patients with missed BF. We developed and verified a MRI-based model to predict BF incidence in patients after RP using preoperative clinical and MRI-related variables. This model could be used in clinical settings.

Identification and validation of obesity related genes signature based on microenvironment phenotypes in prostate adenocarcinoma.

BACKGROUND: The role of obesity related genes (ORGs) in the immune checkpoint inhibitors (ICIs) treatment of prostate adenocarcinoma (PRAD) has not yet been proved by research. METHODS: We comprehensively evaluated the ORGs patterns in PRAD based on tumor microenvironment (TME) phenotypes and immunotherapy efficacies. Then we constructed a ORGs risk score for prognosis and a ORGs signature for accurate prediction of TME phenotype and immunotherapy efficacy in order to evaluate individual patients. RESULTS: Two distinct ORGs patterns were generated. The two ORGs patterns were consistent with inflammatory and non-inflammatory TME phenotypes. ORGs patterns had an important role for predicting immunotherapy efficacies. Next, we constructed a ORGs risk score for predicting each patient's prognosis with high performance in TCGA-PRAD. The ORGs risk score could be well verified in the external cohorts including GSE70769 and GSE21034. Then, we developed a ORGs signature and found it was significantly positively correlated with tumor-infiltrating lymphocytes in TCGA-PRAD. We found that each patient in the high-risk ORGs signature group represented a non-inflamed TME phenotype on the single cell level. The patients with high ORGs signature had more sensitivity to immunotherapy. And those ORGs were verified. CONCLUSIONS: ORGs pattern depicts different TME phenotypes in PRAD. The ORGs risk score and ORGs signature have an important role for predicting prognosis and immunotherapy efficacies.