Purpose: Identification of discrete sub-groups associated with treatment response and resistance in localized Ewing sarcoma (EWS) remains a challenge. The primary objective of the Children's Oncology Group biology study AEWS18B1-Q was to perform molecular characterization of a large cohort of patients with localized Ewing sarcoma treated on prospective trials with modern standard of care therapy.
Methods: We analyzed clinical and molecular features from patients with localized EWS enrolled on AEWS0031, AEWS1031, or INT-0154 frontline trials.
Background: Children living in poverty and those of marginalized race or ethnicity experience inferior disease outcomes across many cancers. Whether survival disparities exist in osteosarcoma is poorly defined. We investigated the association between race, ethnicity, and proxied poverty exposures and event-free and overall survival for children with nonmetastatic osteosarcoma receiving care on a cooperative group trial.
View Article and Find Full Text PDFBackground: Rates of long-term survival for children with pulmonary metastatic osteosarcoma are low, and complete surgical resection of all visible pulmonary metastases is necessary for long term survival. Surgical approaches for metastasectomy include thoracotomy and thoracoscopy, with the approach chosen influenced by training and institutional bias. Thoracotomy with manual palpation of lung surfaces can identify nodules not seen on preoperative imaging, but no clear survival benefit has been demonstrated compared to complete thoracoscopic resection of all visible nodules.
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December 2017
Background: Autism Spectrum Disorder (ASD) is one of the fastest-growing developmental disorders in the United States. It was hypothesized that variations in the placental chorionic surface vascular network (PCSVN) structure may reflect both the overall effects of genetic and environmentally regulated variations in branching morphogenesis within the conceptus and the fetus' vital organs. This paper provides sound evidences to support the study of ASD risks with PCSVN through a combination of feature-selection and classification algorithms.
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