Genetic risk assessment based on association and prediction studies

Magnetic resonance imaging-based biomarkers for knee osteoarthritis outcomes: A narrative review of prediction but not association studies

BACKGROUND

Magnetic Resonance Imaging (MRI) is frequently used in recent studies on knee osteoarthritis (KOA), focusing on developing innovative MRI-based biomarkers to predict KOA outcomes. The growing volume of publications devoted to this subject highlights the need for an up-to-date review.

METHODS

In this narrative review, we utilized the PubMed database to identify studies examining MRI-based biomarkers for the prediction of knee osteoarthritis (KOA), focusing on those reporting relevant prediction, not association, metrics. The identified articles were subsequently categorized into three distinct outcomes: Prediction of KOA incidence (KOAi), KOA progression (KOAp) and total knee arthroplasty risk (TKAr). Within each category, results were organized by the nature of biomarker(s) used, as either quantitative, semi-quantitative or compound.

RESULTS

Due to the lack of predictive metrics such as the area under the ROC curve (AUC) scores, sensitivity or specificity, 27 studies were excluded. A final set of 23 studies were deemed eligible for our analysis. The mean AUC scores reported ranged from 0.67 to 0.83 for predicting KOAi, 0.54 to 0.84 for KOAp and 0.55 to 0.94 for TKAr. Excellent predictive performance (AUC>0.8) was observed for the prediction of radiographic KOAi, KOAp and TKAr when using cartilage and meniscal-based measures, osteophyte scores and infrapatellar fat pad texture, and bone marrow lesions, respectively.

CONCLUSION

The results showed that numerous studies highlighted the importance of MRI-based biomarkers as promising predictors of the three key outcomes. In addition, this narrative review also emphasized the necessity for KOA prediction studies to include adequate reporting of predictive metrics.

Temporal Trends in the Completeness of Epidemiological Variables in a Hospital-Based Cancer Registry of a Pediatric Oncology Center in Brazil

This ecological time series study aimed to examine the temporal trends in the completeness of epidemiological variables from a hospital-based cancer registry (HbCR) of a reference center for pediatric oncology in Brazil from 2010 to 2016. Completeness categories were based on the percentage of missing data, with the categories excellent (<5%), good (5-10%), regular (11-20%), poor (21-50%), and very poor (>50%). Descriptive and bivariate analyses were performed using R.4.1.0; a Mann-Kendall trend test was performed to examine the temporal trends. Variables with the highest incompleteness included race/color (17.24% in 2016), level of education (51.40% in 2015), TNM (56.88% in 2012), disease status at the end of the first treatment (12.09% in 2013), cancer family history (79.12% in 2013), history of alcoholic consumption (39.25% in 2015), history of tobacco consumption (38.32% in 2015), and type of admission clinic (10.28% in 2015). Nevertheless, most variables achieved 100% completeness and were classified as excellent across the time series. A significant trend was observed for race/color, TNM, and history of tobacco consumption. While most variables maintained excellent completeness, the increasing incompleteness trend in race/color and decreasing trend in TNM underscore the importance of reliable and complete HbCRs for personalized cancer care, for planning public policies, and for conducting research on cancer control.