The proportion of population vaccinated cannot be directly translated into the herd immunity. We have to account for the age-stratified contact patterns to calculate the population immunity level, since not every individual gathers evenly. Here, we calculated the contact-adjusted population immunity against severe acute respiratory syndrome coronavirus 2 in South Korea using age-specific incidence and vaccine uptake rate. We further explored options to achieve the theoretical herd immunity with age-varying immunity scenarios. As of June 21, 2021, when a quarter of the population received at least one dose of a coronavirus disease 2019 (COVID-19) vaccine, the contact-adjusted immunity level was 12.5% under the social distancing level 1. When 80% of individuals aged 10 years and over gained immunity, we could achieve a 58.2% contact-adjusted immunity level. The pros and cons of vaccinating children should be weighed since the risks of COVID-19 for the young are less than the elderly, and the long-term safety of vaccines is still obscure.

The proportion of population vaccinated cannot be directly translated into the herd immunity. We have to account for the age-stratified contact patterns to calculate the population immunity level, since not every individual gathers evenly. Here, we calculated the contact-adjusted population immunity against severe acute respiratory syndrome coronavirus 2 in South Korea using age-specific incidence and vaccine uptake rate. We further explored options to achieve the theoretical herd immunity with age-varying immunity scenarios. As of June 21, 2021, when a quarter of the population received at least one dose of a coronavirus disease 2019 (COVID-19) vaccine, the contact-adjusted immunity level was 12.5% under the social distancing level 1. When 80% of individuals aged 10 years and over gained immunity, we could achieve a 58.2% contact-adjusted immunity level. The pros and cons of vaccinating children should be weighed since the risks of COVID-19 for the young are less than the elderly, and the long-term safety of vaccines is still obscure.

PURPOSE: The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. METHODS: This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. RESULTS: Logistic LASSO regression was superior (P < 0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P < 0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P < 0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). CONCLUSION: Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.
Area Under Curve ; Biopsy ; Breast Neoplasms* ; Breast* ; Diagnosis* ; Humans ; Information Systems ; Logistic Models ; Retrospective Studies ; Subject Headings ; Ultrasonography*

In Table 2 and 3, cutoff values of RANTES, ApoA2, TTR, Svcam-1 (and sensitivity and specificity values accordingly) were wrongly marked.

BACKGROUND: Despite major advances in lung cancer treatment, early detection remains the most promising way of improving outcomes. To detect lung cancer in earlier stages, many serum biomarkers have been tested. Unfortunately, no single biomarker can reliably detect lung cancer. We combined a set of 2 tumor markers and 4 inflammatory or metabolic markers and tried to validate the diagnostic performance in lung cancer. METHODS: We collected serum samples from 355 lung cancer patients and 590 control subjects and divided them into training and validation datasets. After measuring serum levels of 6 biomarkers (human epididymis secretory protein 4 [HE4], carcinoembryonic antigen [CEA], regulated on activation, normal T cell expressed and secreted [RANTES], apolipoprotein A2 [ApoA2], transthyretin [TTR], and secretory vascular cell adhesion molecule-1 [sVCAM-1]), we tested various sets of biomarkers for their diagnostic performance in lung cancer. RESULTS: In a training dataset, the area under the curve (AUC) values were 0.821 for HE4, 0.753 for CEA, 0.858 for RANTES, 0.867 for ApoA2, 0.830 for TTR, and 0.552 for sVCAM-1. A model using all 6 biomarkers and age yielded an AUC value of 0.986 and sensitivity of 93.2% (cutoff at specificity 94%). Applying this model to the validation dataset showed similar results. The AUC value of the model was 0.988, with sensitivity of 93.33% and specificity of 92.00% at the same cutoff point used in the validation dataset. Analyses by stages and histologic subtypes all yielded similar results. CONCLUSIONS: Combining multiple tumor and systemic inflammatory markers proved to be a valid strategy in the diagnosis of lung cancer.
Apolipoprotein A-II ; Area Under Curve ; Biomarkers ; Biomarkers, Tumor ; Carcinoembryonic Antigen ; Chemokine CCL5 ; Dataset ; Diagnosis ; Epididymis ; Humans ; Lung Neoplasms* ; Lung* ; Male ; Prealbumin ; Sensitivity and Specificity ; Vascular Cell Adhesion Molecule-1