Construction and evaluation of a nomogram prognostic model for patients of prostate cancer with high tumor load bone metastases / 现代泌尿外科杂志

【Objective】 To identify the risk factors of patients of bone metastatic prostate cancer with high tumor load progressed to castration resistant prostate cancer (CRPC), establish a nomogram prediction model and evaluate its consistency and accuracy. 【Methods】 A total of 164 patients diagnosed by puncture and imaging during 2012 and 2022 were included.The general characteristics were analyzed with IBM SPSS software; the variables were screened with Cox regression; the multivariate risk factors with P<0.05 were included in the nomogram prediction model.The consistency and prediction accuracy of the model were evaluated with C-index, receiver operating characteristic (ROC) curve and calibration chart. 【Results】 In univariate analysis, initial prostate-specific antigen (PSA), prostate-specific antigen density (PSAD), Gleason score, T stage, alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) were correlated with CRPC (P<0.05).Multivariate analysis showed that initial PSA, Gleason score, T stage, ALP and LDH were independent risk factors of CRPC (P<0.05).Based on the above five risk factors, a nomogram prediction model was constructed.The C-index was 0.801, the area under ROC curve (AUC) of 1-year progression-free survival (PFS) was 0.701 (0.608-0.794), and the AUC of 2-year PFS was 0.857 (0.767-0.947).The calibration chart showed that the prediction probability of the model was in good agreement with the actual probability. 【Conclusion】 Initial PSA, Gleason score, T stage, ALP and LDH are independent risk factors of CRPC.The predictive model may be an effective tool for the initial diagnosis of high tumor load bone metastatic prostate cancer, but more data are needed for internal and external validation.

Metabolic syndrome and metastatic prostate cancer correlation study, a real-world study in a prostate cancer clinical research center, Xinjiang, China.

Objective: The aim of this study was to investigate the relevance of metabolic syndrome (MetS) and metabolic scores to the occurrence, progression and prognosis of metastatic prostate cancer (mPCA), assessing the definition of the variables of metabolic syndrome, and the potential mechanisms of MetS and mPCA. Methods: Data were obtained from the database of prostate cancer follow-up at the Urology Centre of the First Affiliated Hospital of Xinjiang Medical University (N=1303). After screening by inclusion and exclusion criteria, clinical data of 190 patients diagnosed with mPCA by pathology and imaging from January 2010 to August 2021 were finally included, including 111 cases in the MetS group and 79 cases in the Non-MetS group. Results: The MetS group was higher than the Non-MetS group: T stage, Gleasson score, initial PSA, tumor load, PSA after 7 months of ADT (P<0.05),with a shorter time to progression to CRPC stage(P<0.05)[where the time to progression to CRPC was relatively shorter in the high metabolic score subgroup of the MetS group than in the low subgroup (P<0.05)].Median survival time was significantly shorter in the MetS group than in the Non-MetS group (P<0.05),and there was a correlation with metabolic score, with the higher metabolic score subgroup having a lower survival time than the lower metabolic score subgroup (P<0.05). Conclusion: Those with mPCA combined with MetS had lower PSA remission rates, more aggressive tumors, shorter time to progression to CRPC and shorter median survival times than those with mPCA without MetS.Tumour progression and metabolic score showed a positive correlation, predicting that MetS may promote the progression of mPCA, suggesting that MetS may be a risk factor affecting the prognosis of mPCA.

The relationship between urinary iodine concentration and papillary thyroid cancer: A systematic review and meta-analysis.

Background: The effect of iodine on papillary thyroid cancer (PTC) has been controversial for many years. Since urinary iodine is an effective indicator of iodine intake, some recent epidemiological studies have described the relationship between urinary iodine concentration (UIC) and PTC. Methods: We searched PubMed, Embase, Cochrane Library, and Web of Science for case-control studies about UIC and PTC published before September 2022. Results are presented as the overall odds ratio (OR) and 95% confidence intervals (CI). Results: According to the analysis of the included studies, excessive iodine intake (UIC≥300ug/L) was positively associated with the occurrence of PTC patients compared with healthy controls (OR4.05, 95%CI 1.64-10.02, P=0.002). Meanwhile, adequate iodine exposure (100≤UIC<200ug/L) may play a protective role in the occurrence of PTC compared with healthy individuals (OR 0.36, 95%CI 0.14-0.91, P=0.03) while the difference in the prevalence of insufficient iodine intake (UIC<100ug/L) and iodine above requirements (200≤UIC<300ug/L) among the two groups were not significant (deficiency: OR 0.38, 95%CI 0.13-1.16, P=0.09; above requirements: OR 0.92, 95%CI 0.40-2.10, P=0.84). After comparing the UIC levels of PTC patients with those of other thyroid diseases, we found that there was also no significant difference in the incidence of different levels of UIC in the two groups (excessive: OR 1.25, 95%CI 0.87-1.80, P=0.22; above requirements: OR 0.93, 95%CI 0.77-1.14, P=0.49; adequate: OR 0.96, 95%CI 0.78-1.17, P=0.67; deficiency: OR 1.02, 95%CI 0.86-1.22, P=0.80). The result of this meta-analysis also did not support the relationship between UIC and the BRAF mutation and lymph node metastasis (LNM) of PTC patients. Besides, we also found that studies on the relationship between urinary iodine and PTC may be influenced by the way UIC was measured. Conclusion: The 10 case-control included studies involved a total of 6,544 participants. The results of this meta-analysis showed excessive iodine intake, that is, UIC≥300ug/L was associated with the occurrence of PTC but not with BRAF mutation and LNM while adequate iodine intake (100≤UIC<200ug/L) may be one of the protective factors for PTC.