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Background Stunted growth and development is a serious global public health problem. A limited number of field measures exist that can be used to evaluate stunting and its underlying biologic mechanisms. Purpose To assess bone age using tablet-based US in young children living in a rural community in Ecuador, where stunting is prevalent, and to evaluate the associations between bone age, anthropometry, and diet. Materials and Methods From June through August 2017, tablet-based US was used to assess bone age in young children within their homes in rural Cotopaxi, Ecuador. Bone age z scores (BAZs) were assigned using the standards of Greulich and Pyle. Anthropometric data were collected using international protocols; z scores were generated from World Health Organization Child Growth Standards. Groups were compared using the Student t test. Univariate analyses and generalized linear regression modeling were applied to test the association between bone age and anthropometry, adjusting for covariates including age, sex, dietary intake, and morbidities. Results A total of 128 children (mean age, 33.9 months ± 1.8 [standard deviation]; 59 girls, 69 boys) were evaluated. Mean BAZ was -1.20 ± 1.16. Mean BAZ was lower in children with stunted growth (-1.42 ± 1.18) than in children without stunted growth (-0.98 ± 1.10, P = .04). In adjusted analysis, BAZ was associated with the following variables: height-for-age z score (β coefficient, 0.26; 95% confidence interval [CI]: 0.05, 0.46; P = .01), female sex (β coefficient, 0.51; 95% CI: 0.15, 0.88; P = .006), number of times eggs were consumed in the previous 24 hours (β coefficient, 0.22; 95% CI: 0.05, 0.38; P = .009), number of times savory or salty snacks were consumed in the previous 24 hours (β coefficient, 0.42; 95% CI: 0.15, 0.68; P = .002), and ownership of pig livestock, which was a binary variable (β coefficient, -0.46; 95% CI: -0.82, -0.09; P = .01). Conclusion Bone age determined using tablet-based US was lower in children who had stunted growth and was associated with diet in a cohort of children living in rural Ecuador. © RSNA, 2020 See also the editorial by Dillman and Ayyala in this issue.

Purpose: To evaluate the interobserver variability in descriptions of breast masses by dedicated breast imagers and radiology residents and determine how any differences in lesion description affect the performance of a computer-aided diagnosis (CAD) computer classification system., Materials and Methods: Institutional review board approval was obtained for this HIPAA-compliant study, and the requirement to obtain informed consent was waived. Images of 50 breast lesions were individually interpreted by seven dedicated breast imagers and 10 radiology residents, yielding 850 lesion interpretations. Lesions were described with use of 11 descriptors from the Breast Imaging Reporting and Data System, and interobserver variability was calculated with the Cohen κ statistic. Those 11 features were selected, along with patient age, and merged together by a linear discriminant analysis (LDA) classification model trained by using 1005 previously existing cases. Variability in the recommendations of the computer model for different observers was also calculated with the Cohen κ statistic., Results: A significant difference was observed for six lesion features, and radiology residents had greater interobserver variability in their selection of five of the six features than did dedicated breast imagers. The LDA model accurately classified lesions for both sets of observers (area under the receiver operating characteristic curve = 0.94 for residents and 0.96 for dedicated imagers). Sensitivity was maintained at 100% for residents and improved from 98% to 100% for dedicated breast imagers. For residents, the computer model could potentially improve the specificity from 20% to 40% (P < .01) and the κ value from 0.09 to 0.53 (P < .001). For dedicated breast imagers, the computer model could increase the specificity from 34% to 43% (P = .16) and the κ value from 0.21 to 0.61 (P < .001)., Conclusion: Among findings showing a significant difference, there was greater interobserver variability in lesion descriptions among residents; however, an LDA model using data from either dedicated breast imagers or residents yielded a consistently high performance in the differentiation of benign from malignant breast lesions, demonstrating potential for improving specificity and decreasing interobserver variability in biopsy recommendations., (© RSNA, 2010)