A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection.

The partial area under the receiver operating characteristic curve (PAUC) is a well-established performance measure to evaluate biomarker combinations for disease classification. Because the PAUC is defined as the area under the ROC curve within a restricted interval of false positive rates, it enables practitioners to quantify sensitivity rates within pre-specified specificity ranges. This issue is of considerable importance for the development of medical screening tests. Although many authors have highlighted the importance of PAUC, there exist only few methods that use the PAUC as an objective function for finding optimal combinations of biomarkers. In this paper, we introduce a boosting method for deriving marker combinations that is explicitly based on the PAUC criterion. The proposed method can be applied in high-dimensional settings where the number of biomarkers exceeds the number of observations. Additionally, the proposed method incorporates a recently proposed variable selection technique (stability selection) that results in sparse prediction rules incorporating only those biomarkers that make relevant contributions to predicting the outcome of interest. Using both simulated data and real data, we demonstrate that our method performs well with respect to both variable selection and prediction accuracy. Specifically, if the focus is on a limited range of specificity values, the new method results in better predictions than other established techniques for disease classification

Letter: A Note on Neurosurgical Resection and Why We Need to Rethink Cutting

Peer Reviewe

Survival and predictors of breast cancer mortality in South Ethiopia: A retrospective cohort study

Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in over 100 countries. In March 2021, the World Health Organization called on the global community to decrease mortality by 2.5% per year. Despite the high burden of the disease, the survival status and the predictors for mortality are not yet fully determined in many countries in Sub-Saharan Africa, including Ethiopia. Here, we report the survival status and predictors of mortality among breast cancer patients in South Ethiopia as crucial baseline data to be used for the design and monitoring of interventions to improve early detection, diagnosis, and treatment capacity. Methods: A hospital-based retrospective cohort study was conducted among 302 female breast cancer patients diagnosed from 2013 to 2018 by reviewing their medical records and telephone interviews. The median survival time was estimated using the Kaplan-Meier survival analysis method. A log-rank test was used to compare the observed differences in survival time among different groups. The Cox proportional hazards regression model was used to identify predictors of mortality. Results are presented using the crude and adjusted as hazard ratios along with their corresponding 95% confidence intervals. Sensitivity analysis was performed with the assumption that loss to follow-up patients might die 3 months after the last hospital visit. Results: The study participants were followed for a total of 4,685.62 person-months. The median survival time was 50.81 months, which declined to 30.57 months in the worst-case analysis. About 83.4% of patients had advanced-stage disease at presentation. The overall survival probability of patients at two and three years was 73.2% and 63.0% respectively. Independent predictors of mortality were: patients residing in rural areas (adjusted hazard ratio = 2.71, 95% CI: 1.44, 5.09), travel time to a health facility ≥7 hours (adjusted hazard ratio = 3.42, 95% CI: 1.05, 11.10), those who presented within 7–23 months after the onset of symptoms (adjusted hazard ratio = 2.63, 95% CI: 1.22, 5.64), those who presented more than 23 months after the onset of symptoms (adjusted hazard ratio = 2.37, 95% CI: 1.00, 5.59), advanced stage at presentation (adjusted hazard ratio = 3.01, 95% CI: 1.05, 8.59), and patients who never received chemotherapy (adjusted hazard ratio = 6.69, 95% CI: 2.20, 20.30). Conclusion: Beyond three years after diagnosis, patients from southern Ethiopia had a survival rate of less than 60% despite treatment at a tertiary health facility. It is imperative to improve the early detection, diagnosis, and treatment capacities for breast cancer patients to prevent premature death in these women