Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.     

The role of endoscopic ultrasound for portal hypertension in liver cirrhosis

Journal of Medical Ultrasonics - Chronic liver disease is still a major problem because disease progression will ultimately lead to liver cirrhosis. Portal hypertension is the hallmark in advanced...     

Effects of catastrophic financial loss on suicide risk: evidence from Korean stock market crash in October 2008

Social Psychiatry and Psychiatric Epidemiology - The negative effect of catastrophic financial loss on suicide risk is widely perceived but hardly studied in-depth because of various difficulties...     

A systematic review of the methodological quality of economic evaluations in genetic screening and testing for monogenic disorders

PurposeUnderstanding the value of genetic screening and testing for monogenic disorders requires high-quality, methodologically robust economic evaluations. This systematic review sought to assess the methodological quality among such studies and examined opportunities for improvement.MethodsWe searched PubMed, Cochrane, Embase, and Web of Science for economic evaluations of genetic screening/testing (2013-2019). Methodological rigor and adherence to best practices were systematically assessed using the British Medical Journal checklist.ResultsAcross the 47 identified studies, there were substantial variations in modeling approaches, reporting detail, and sophistication. Models ranged from simple decision trees to individual-level microsimulations that compared between 2 and >20 alternative interventions. Many studies failed to report sufficient detail to enable replication or did not justify modeling assumptions, especially for costing methods and utility values. Meta-analyses, systematic reviews, or calibration were rarely used to derive parameter estimates. Nearly all studies conducted some sensitivity analysis, and more sophisticated studies implemented probabilistic sensitivity/uncertainty analysis, threshold analysis, and value of information analysis.ConclusionWe describe a heterogeneous body of work and present recommendations and exemplar studies across the methodological domains of (1) perspective, scope, and parameter selection; (2) use of uncertainty/sensitivity analyses; and (3) reporting transparency for improvement in the economic evaluation of genetic screening/testing.