Update on central precocious puberty: from etiologies to outcomes

Introduction: Precocious puberty (PP) is one of the most common reasons for referral to pediatric endocrinologists. Gonadotropin-releasing hormone analogs (GnRHas) are the gold standard for the treatment of central precocious puberty (CPP) and have an impressive record of safety and efficacy. However, ongoing refinements in diagnosis and management continue to lead to important advancements in clinical care. Areas covered: The aim of this review is to cover current considerations and controversies regarding the diagnosis of CPP, as well as new findings in regards to etiology and treatment modalities. Expert opinion: There is emerging evidence of monogenic etiologies of CPP and significant progress in the expansion of newer formulations of GnRHas. Despite these exciting developments, areas of uncertainty in the diagnosis and treatment of CPP remain. While long-term outcomes of patients treated for CPP are encouraging, only short-term follow-up is available with respect to the newer extended release GnRHa preparations, and how they compare with historically used formulations is unknown. A particular shortage of information exists pertaining to CPP in boys and regarding the psychological implications of PP in girls, and more research is needed. Continued investigation will yield new insights into the underlying genetics and optimal treatment strategies for CPP

Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data

Purpose: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods: 4D texture analysis was performedon DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with α = 0.05. The results show that an area under the ROC curve (Az) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted. © 2007 Wiley-Liss, Inc