This study assesses the value of the Greulich and Pyle method in determining the skeletal ages of healthy American children of European and African descent born after the year 1980. The hand and wrist radiographs of 534 children (265 boys, 269 girls; 260 European-Americans [EA], 274 African-Americans [AA]), ages 0 to 19 y, were analyzed by two experienced pediatric radiologists blinded to the chronological age of the subjects. A difference score was calculated for each subject by subtracting chronological age from the mean bone ages scores provided by the two raters. One group t-tests were performed to verify the hypothesis that the mean difference score was equal to zero. Skeletal age determinations by the two radiologists showed a high degree of agreement by intraclass correlation coefficient (r ϭ 0.994). The range of values for differences in skeletal and chronological ages was very wide, indicating great individual variability. Comparisons between skeletal and chronological age only reached statistical significance in EA prepubertal girls, whose skeletal ages were delayed, on average, by three months (t ϭ Ϫ2.9; p ϭ 0.005). Mean difference between skeletal and chronological age in prepubertal children of African descent was 0.09 Ϯ 0.66 y, while that in children of European descent was Ϫ0.17 Ϯ 0.67 y; (t ϭ 3.13; p ϭ 0.0019). On average, the bone ages of 10% of all prepubertal AA children were 2 SD above the normative data in the Greulich and Pyle atlas, while the bone ages of 8% of all prepubertal EA children were 2 SD below. In contrast to the racial differences observed in prepubertal children, EA postpubertal males had significantly greater values for bone age than AA postpubertal males (t ϭ 2.03; p ϭ 0.05). In conclusion, variations in skeletal maturation in prepubertal children are greater than those reflected in the Greulich and Pyle atlas; prepubertal American children of European descent have significantly delayed skeletal maturation when compared with those of African descent; and, postpubertal EA males have significantly advanced skeletal maturation when compared with postpubertal AA males. New standards are needed to make clinical decisions that require reliable bone ages and to accurately represent a multiethnic pediatric population. Skeletal age is a frequently used diagnostic tool for the evaluation of endocrine, orthopedic, genetic, and renal disorders, to monitor response to medical therapy and to determine the growth potential of children (1). The method most widely used for bone age determination is the reference atlas of Greulich and Pyle, consisting of radiologic examinations of the left hand and wrist from subjects at different stages of skeletal maturation (2). While this method has the advantages of simplicity and availability of multiple ossification centers for the evaluation of maturity, it is, however, qualitative and was compiled solely from Caucasian children who lived in the 1930s. Nevertheless, the Greulich and Pyle standards are, at present, the most commonly used method for ske...

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We have developed an automated method to assess bone age of children using a digital hand atlas. The hand atlas consists of two components. The first component is a database which is comprised of a collection of 1400 digitized left hand radiographs from evenly distributed normally developed children of Caucasian (CA), Asian (AS), African-American (AA) and Hispanic (HI) origin, male (M) and female (F), ranged from 1- to 18-year-old; and relevant patient demographic data along with pediatric radiologists' readings of each radiograph. This data is separate into eight categories: CAM, CAF, AAM, AAF, HIM, HIF, ASM, and ASF. In addition, CAM, AAM, HIM, and ASM are combined as one male category; and CAF, AAF, HIF, and ASF are combined as one female category. The male and female are further combined as the F & M category. The second component is a computer-assisted diagnosis (CAD) module to assess a child bone age based on the collected data. The CAD method is derived from features extracted from seven regions of interest (ROIs): the carpal bone ROI, and six phanlangeal PROIs. The PROIs are six areas including the distal and middle regions of three middle fingers. These features were used to train the 11 category fuzzy classifiers: one for each race and gender, one for the female, one male, and one F & M, to assess the bone age of a child. The digital hand atlas is being integrated with a PACS for validation of clinical use.

BackgroundThe purpose of this study is to validate a new five-tiered prognostic classification system to better discriminate cancer-specific mortality in men diagnosed with primary non-metastatic prostate cancer.MethodsWe applied a recently described five-strata model, the Cambridge Prognostic Groups (CPGs 1-5), in two international cohorts and tested prognostic performance against the current standard three-strata classification of low-, intermediate- or high-risk disease. Diagnostic clinico-pathological data for men obtained from the Prostate Cancer data Base Sweden (PCBaSe) and the Singapore Health Study were used. The main outcome measure was prostate cancer mortality (PCM) stratified by age group and treatment modality.ResultsThe PCBaSe cohort included 72,337 men, of whom 7162 died of prostate cancer. The CPG model successfully classified men with different risks of PCM with competing risk regression confirming significant intergroup distinction (p < 0.0001). The CPGs were significantly better at stratified prediction of PCM compared to the current three-tiered system (concordance index (C-index) 0.81 vs. 0.77, p < 0.0001). This superiority was maintained for every age group division (p < 0.0001). Also in the ethnically different Singapore cohort of 2550 men with 142 prostate cancer deaths, the CPG model outperformed the three strata categories (C-index 0.79 vs. 0.76, p < 0.0001). The model also retained superior prognostic discrimination in the treatment sub-groups: radical prostatectomy (n = 20,586), C-index 0.77 vs. 074; radiotherapy (n = 11,872), C-index 0.73 vs. 0.69; and conservative management (n = 14,950), C-index 0.74 vs. 0.73. The CPG groups that sub-divided the old intermediate-risk (CPG2 vs. CPG3) and high-risk categories (CPG4 vs. CPG5) significantly discriminated PCM outcomes after radical therapy or conservative management (p < 0.0001).ConclusionsThis validation study of nearly 75,000 men confirms that the CPG five-tiered prognostic model has superior discrimination compared to the three-tiered model in predicting prostate cancer death across different age and treatment groups. Crucially, it identifies distinct sub-groups of men within the old intermediate-risk and high-risk criteria who have very different prognostic outcomes. We therefore propose adoption of the CPG model as a simple-to-use but more accurate prognostic stratification tool to help guide management for men with newly diagnosed prostate cancer.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1019-5) contains supplementary material, which is available to authorized users.

For the past 30 y, data have suggested that unique islet populations exist, based on morphology and glucose sensitivity. Yet little has been done to determine the mechanism of these functional differences. The purpose of this study was to determine whether human islets were comprised functionally unique populations, and to elucidate a possible mechanism. Islets or pancreatic sections from 29 human donors were analyzed. Islets were isolated and measured for insulin secretion, cell composition and organization, insulin and glucagon granule density and insulin content. Insulin secretion was significantly greater in small compared with large islets. In sectioned human pancreata, β-cells comprised a higher proportion of the total endocrine cells in small islets (63%) than large islets (39%). A higher percentage of β-cells in small islets contacted blood vessels (44%) compared with large islets (31%). Total insulin content of isolated human islets was significantly greater in the small (1323 ± 512 μIU/IE) compared with large islets (126 ± 48 μIU/IE). There was less immunostaining for insulin in the large islets from human pancreatic sections, especially in the core of the islet, compared with small islets. The results suggest that differences in insulin secretion between large and small islets may be due to a higher percentage of β-cells in small islets with more β-cells in contact with blood vessels and a higher concentration of insulin/β-cell in small islets.