Quiz Ref ID Use of computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound in the United States increased rapidly from 2000 to 2006.^1,2 Among Medicare beneficiaries, the increase in utilization was higher for medical imaging than other physician-provided services.^3,4 Steep increases in imaging can be attributed to technical improvements, physician and patient demand, and strong financial incentives. Medical imaging contributes to accurate disease diagnosis and improved treatment; however, imaging can also increase costs and patient harms, such as incidental findings, overdiagnosis, anxiety, and radiation exposure associated with increased risk of cancer. It has been estimated that 30% or more of imaging examinations may be unnecessary, costing approximately $30 billion annually in the United States.⁵ In addition, in a recent study that compared the United States with 10 other countries on various metrics, the United States ranked 1 or 2 in the number of CT and MRI scans performed per 1000 individuals.⁶

Purpose: To uncover the genetic events leading to transformation of pediatric low-grade glioma (PLGG) to secondary high-grade glioma (sHGG). Patients and Methods: We retrospectively identified patients with sHGG from a population-based cohort of 886 patients with PLGG with long clinical follow-up. Exome sequencing and array CGH were performed on available samples followed by detailed genetic analysis of the entire sHGG cohort. Clinical and outcome data of genetically distinct subgroups were obtained. Results: sHGG was observed in 2.9% of PLGGs (26 of 886 patients). Patients with sHGG had a high frequency of nonsilent somatic mutations compared with patients with primary pediatric high-grade glioma (HGG; median, 25 mutations per exome; P = .0042). Alterations in chromatin-modifying genes and telomere-maintenance pathways were commonly observed, whereas no sHGG harbored the BRAF-KIAA1549 fusion. The most recurrent alterations were BRAF V600E and CDKN2A deletion in 39% and 57% of sHGGs, respectively. Importantly, all BRAF V600E and 80% of CDKN2A alterations could be traced back to their PLGG counterparts. BRAF V600E distinguished sHGG from primary HGG (P = .0023), whereas BRAF and CDKN2A alterations were less commonly observed in PLGG that did not transform (P < .001 and P < .001 respectively). PLGGs with BRAF mutations had longer latency to transformation than wild-type PLGG (median, 6.65 years [range, 3.5 to 20.3 years] v 1.59 years [range, 0.32 to 15.9 years], respectively; P = .0389). Furthermore, 5-year overall survival was 75% ± 15% and 29% ± 12% for children with BRAF mutant and wild-type tumors, respectively (P = .024). Conclusion: BRAF V600E mutations and CDKN2A deletions constitute a clinically distinct subtype of sHGG. The prolonged course to transformation for BRAF V600E PLGGs provides an opportunity for surgical interventions, surveillance, and targeted therapies to mitigate the outcome of sHGG.

BACKGROUND: The determinants of outcomes for adult survivors of pediatric low‐grade glioma (PLGG) are largely unknown.METHODS: This study collected population‐based follow‐up information for all PLGG patients diagnosed in Ontario, Canada from 1985 to 2012 (n = 1202) and determined factors affecting survival. The impact of upfront radiation treatment on overall survival (OS) was determined for a cohort of Ontario patients and an independent reference cohort from the Surveillance, Epidemiology, and End Results database.RESULTS: At a median follow‐up of 12.73 years (range, 0.02‐33 years), only 93 deaths (7.7%) were recorded, and the 20‐year OS rate was 90.1% ± 1.1%. Children with neurofibromatosis type 1 had excellent survival and no tumor‐related deaths during adulthood. Adverse risk factors included pleomorphic xanthoastrocytoma (P < .001) and a thalamic location (P < .001). For patients with unresectable tumors surviving more than 5 years after the diagnosis, upfront radiotherapy was associated with an approximately 3‐fold increased risk of overall late deaths (hazard ratio [HR], 3.3; 95% confidence interval [CI], 1.6‐6.6; P = .001) and an approximately 4‐fold increased risk of tumor‐related deaths (HR, 4.4; 95% CI, 1.3‐14.6; P = .013). In a multivariate analysis, radiotherapy was the most significant factor associated with late all‐cause deaths (HR, 3.0; 95% CI, 1.3‐7.0; P = .012) and tumor‐related deaths (HR, 4.4; 95% CI, 1.3‐14.6; P = 0.014). A similar association between radiotherapy and late deaths was observed in the independent reference cohort (P < .001). In contrast to early deaths, late mortality was associated not with PLGG progression but rather with tumor transformation and non‐oncological causes.CONCLUSIONS: The course of PLGG is associated with excellent long‐term survival, but this is hampered by increased delayed mortality in patients receiving upfront radiotherapy. These observations should be considered when treatment options are being weighed for these patients. Cancer 2016;122:1261–9. © 2016 American Cancer Society.

While low socioeconomic status (SES) has been associated with inferior cancer outcome among adults, its impact in pediatric oncology is unclear. Our objective was therefore to conduct a systematic review to determine the impact of SES upon outcome in children with cancer. We searched Ovid Medline, EMBASE and CINAHL from inception to December 2012. Studies for which survival-related outcomes were reported by socioeconomic subgroups were eligible for inclusion. Two reviewers independently assessed articles and extracted data. Given anticipated heterogeneity, no quantitative meta-analyses were planned a priori. Of 7,737 publications, 527 in ten languages met criteria for full review; 36 studies met final inclusion criteria. In low- and middle-income countries (LMIC), lower SES was uniformly associated with inferior survival, regardless of the measure chosen. The majority of associations were statistically significant. Of 52 associations between socioeconomic variables and outcome among high-income country (HIC) children, 38 (73.1%) found low SES to be associated with worse survival, 15 of which were statistically significant. Of the remaining 14 (no association or high SES associated with worse survival), only one was statistically significant. Both HIC studies examining the effect of insurance found uninsured status to be statistically associated with inferior survival. Socioeconomic gradients in which low SES is associated with inferior childhood cancer survival are ubiquitous in LMIC and common in HIC. Future studies should elucidate mechanisms underlying these gradients, allowing the design of interventions mediating socioeconomic effects. Targeting the effect of low SES will allow for further improvements in childhood cancer survival.

Background: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set–based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. Objective: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. Results: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. Conclusions: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

Population-based cancer registries generate estimates of incidence and survival that are essential for cancer surveillance, research, and control strategies. Although data on cancer stage allow meaningful assessments of changes in cancer incidence and outcomes, stage is not recorded by most population-based cancer registries. The main method of staging adult cancers is the TNM classification. The criteria for staging paediatric cancers, however, vary by diagnosis, have evolved over time, and sometimes vary by cooperative trial group. Consistency in the collection of staging data has therefore been challenging for population-based cancer registries. We assembled key experts and stakeholders (oncologists, cancer registrars, epidemiologists) and used a modified Delphi approach to establish principles for paediatric cancer stage collection. In this Review, we make recommendations on which staging systems should be adopted by population-based cancer registries for the major childhood cancers, including adaptations for low-income countries. Wide adoption of these guidelines in registries will ease international comparative incidence and outcome studies.