The Association Between Metabolic Status and Risk of Cancer Among Patients With Obesity: Metabolically Healthy Obesity vs. Metabolically Unhealthy Obesity.

Background: Controversial evidence about the association between cancer risk and metabolic status among individuals with obesity has been reported, but pooled data remain absent. This study aims to present pooled data comparing cancer risk between patients with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). Methods: The current study systematically searched pieces of literature on January 4, 2021, of prospective cohorts that compare the incidence of cancer between MHO and MUO. The quality of included studies was assessed using Newcastle-Ottawa scale, and publication bias was evaluated using funnel plots. Results: Eleven high-quality studies were eventually selected. Quantitative analysis indicates that a lower cancer incidence exists for MHO phenotype than that for MUO (odds ratio [OR], 0.71; 95% confidential interval [CI], 0.61-0.84). Consistent outcomes are presented by subgroup analyses, which are grouped by cohort region (western population: [OR, 0.84; 95% CI, 0.75-0.93]; Asian population: [OR, 0.64; 95% CI, 0.54-0.77]); definition of metabolic unhealthiness (≥3 metabolic abnormalities: [OR, 0.62; 95% CI, 0.54-0.71]; ≥1 metabolic abnormality: [OR, 0.76; 95% CI, 0.62-0.94]); and definition of obesity (body mass index (BMI), ≥30 kg/m2: [OR, 0.84; 95% CI, 0.73-0.98]; BMI, ≥25 kg/m2: [OR, 0.53; 95% CI, 0.52-0.55]). Conclusion: In conclusion, this study suggests a reduced cancer risk for MHO compared to MUO regardless of population heterogeneity, or the definitions of obesity and metabolic status.

A robust approach to establish tolerance limits for the gamma passing rate-based patient-specific quality assurance using the heuristic control charts.

PURPOSE: Establishing the tolerance limits of patient-specific quality assurance (PSQA) processes based on the gamma passing rate (GPR) by using normal statistical process control (SPC) methods involves certain problems. The aim of this study was threefold: (a) to show that the heuristic SPC method can replace the quantile method for establishing tolerance limits in PSQA processes and is more robust, (b) to introduce an iterative procedure of "Identify-Eliminate-Recalculate" for establishing the tolerance limits in PSQA processes with unknown states based on retrospective GPRs, and (c) to recommend a workflow to define tolerance limits based on actual clinical retrospective GPRs. MATERIALS AND METHODS: A total of 1671 volumetric-modulated arc therapy (VMAT) pretreatment plans were measured on four linear accelerators (linacs) and analyzed by treatment sites using the GPRs under the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. Normality testing was performed using the Anderson-Darling (AD) statistic and the optimal distributions of GPRs were determined using the Fitter Python package. The iterative "Identify-Eliminate-Recalculate" procedure was used to identify the PSQA outliers. The tolerance limits of the initial PSQAs, remaining PSQAs after elimination, and in-control PSQAs after correction were calculated using the conventional Shewhart method, two transformation methods, three heuristic methods, and two quantile methods. The tolerance limits of PSQA processes with different states for the respective methods, linacs, and treatment sites were comprehensively compared and analyzed. RESULTS: It was found that 75% of the initial PSQA processes and 63% of the in-control processes were non-normal (AD test, p < 0.05). The optimal distributions of GPRs for the initial and in-control PSQAs varied with different linacs and treatment sites. In the implementation of the "Identify-Eliminate-Recalculate" procedure, the quantile methods could not identify the out-of-control PSQAs effectively due to the influence of outliers. The tolerance limits of the in-control PSQAs, calculated using the quantile of optimal fitting distributions, represented the ground truth. The tolerance limits of the in-control PSQAs and remaining PSQAs after elimination calculated using the heuristic methods were considerably close to the ground truth (the maximum average absolute deviations were 0.50 and 1.03%, respectively). Some transformation failures occurred under both transformation methods. For the in-control PSQAs at 3%/2 mm gamma criteria, the maximum differences in the tolerance limits for four linacs and different treatment sites were 3.10 and 5.02%, respectively. CONCLUSIONS: The GPR distributions of PSQA processes vary with different linacs and treatment sites but most are skewed. In applying SPC methodologies to PSQA processes, heuristic methods are robust. For in-control PSQA processes, the tolerance limits calculated by heuristic methods are in good agreement with the ground truth. For unknown PSQA processes, the tolerance limits calculated by the heuristic methods after the iterative "Identify-Eliminate-Recalculate" procedure are closest to the ground truth. Setting linac- and treatment site-specific tolerance limits for PSQA processes is necessary for clinical applications.

Efficacy and safety of abiraterone and enzalutamide for castration-resistant prostate cancer: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Previous evidence directly evaluating the efficacy and safety of abiraterone and enzalutamide treatment for castration-resistant prostate cancer (CRPC) is limited. We aim to include more randomized controlled trials (RCTs) to comprehensively assess the efficacy and safety of abiraterone and enzalutamide treatment. METHODS: PubMed, Embase, and ClinicalTrial.gov were systematically searched. Pooled hazard ratios (HRs) were calculated using Stata 12.0 software. The comparison of the prostate-specific antigen (PSA) response rate and adverse events (AEs) between the treatment and control groups were performed using RevMan 5.3 software. RESULTS: Eight eligible RCTs with 6,490 patients were selected. Pooled HRs were 0.72 for overall survival, 0.45 for radiographic progression-free survival (rPFS), and 0.36 for PSA PFS. abiraterone and enzalutamide could significantly increase the PSA response rate OR = 8.67, 95%CI 4.42-17.04) and any AE occurrence (OR = 1.98, 95%CI 1.46-2.68). The treatment group had more occurrence of fatigue (OR = 1.34, 95%CI 1.20-1.49), back pain (OR = 1.15, 95%CI 1.01-1.15), hot flush (OR = 1.76, 95%CI 1.50-2.06), diarrhea (OR=1.22, 95%CI 1.07-2.40) and arthralgia (OR = 1.34, 95%CI 1.16-1.54). Particularly, AEs of special interest including any grade hypertension (OR = 2.06, 95%CI 1.71-2.47), hypokalemia (OR = 1.80, 95%CI 1.42-2.30) and fluid retention or edema (OR = 1.38, 95%CI 1.17-1.63) also occurred less in the control group. Moreover, a higher incidence of high-grade hypertension (OR = 2.60, 95%CI 1.79-3.79) and extremity pain (OR = 4.46, 95%CI 2.81-7.07) was observed in the treatment group. CONCLUSION: The survival benefits of abiraterone and enzalutamide for CRPC were evident and promising, while the risk of AE occurrence was also acceptably higher in the treatment group than in the placebo group.