NRAS is frequently mutated in hematologic malignancies. We generated Mx1-Cre, Lox-STOP-Lox (LSL) -

Background Preclinical evidence suggests that modulation of the gut microbiome could represent a new therapeutic target in nonalcoholic fatty liver disease (NAFLD). Objectives The aim of this study was to evaluate the most current evidence for liver-specific and metabolic effects of microbiome-targeted therapies (MTTs) in persons with NAFLD. Methods We searched multiple electronic databases for randomized controlled trials (RCTs) published from January 1, 2005 to December 1, 2018 that enrolled persons with NAFLD who received MTT rather than placebo or usual care. MTT was defined as antibiotics, probiotics, synbiotics, or fecal microbiota transplantation (FMT). Clinical outcomes were pooled with the use of random-effects models and heterogeneity was assessed with the I2 statistic. A random-effects meta-regression was performed to determine sources of heterogeneity in prevalence estimates between studies. Results Twenty-one RCTs (1252 participants) were included; 9 evaluated probiotics and 12 evaluated synbiotics, with treatment duration ranging from 8 to 28 wk. No RCTs examined the efficacy of antibiotics or FMT. Probiotics/synbiotics were associated with a significant reduction in alanine aminotransferase activity [ALT, weighted mean difference (WMD): −11.23 IU/L; 95% CI: −15.02, −7.44 IU/L] and liver stiffness measurement (LSM) by elastography (reflecting inflammation and fibrosis) (WMD: −0.70 kPa; 95% CI: −1.00, −0.40 kPa), although analyses showed heterogeneity (I2 = 90.6% and I2 = 93.4%, respectively). Probiotics/synbiotics were also associated with increased odds of improvement in hepatic steatosis, as graded by ultrasound (OR: 2.40; 95% CI: 1.50, 3.84; I2 = 22.4%). No RCTs examined sequential liver biopsy findings. Probiotics (WMD: −1.84; 95% CI: −3.30, −0.38; I2 = 23.6%), but not synbiotics (WMD: −0.85; 95% CI: −2.17, 0.47; I2 = 96.6%), were associated with a significant reduction in body mass index. Conclusions The use of probiotics/synbiotics was associated with improvement in liver-specific markers of hepatic inflammation, LSM, and steatosis in persons with NAFLD. Although promising, given the heterogeneity in pooled analyses, additional well-designed RCTs are needed to define the efficacy of probiotics/synbiotics for treatment of NAFLD. This study was registered with PROSPERO as CRD42018091455.

Summary Background Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational “radiomic” techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. Aims To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation. Methods We performed a systematic review of all full‐text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies. Results A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c‐statistics 0.66‐0.95), and to predict microvascular invasion (c‐statistic 0.76‐0.92), early recurrence after hepatectomy (c‐statistics 0.71‐0.86), and prognosis after locoregional or systemic therapies (c‐statistics 0.74‐0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard. Conclusions Quantitative image analysis demonstrates promise as a non‐invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.

Key Points Defective Ras oncoproteins initiate T-ALL. Murine T-ALLs lacking PTEN have gene expression profiles similar to human early T-cell precursor ALL and are resistant to MEK inhibition.

ObjectiveMedical billing data are an attractive source of secondary analysis because of their ease of use and potential to answer population-health questions with statistical power. Although these datasets have known susceptibilities to biases, the degree to which they can distort the assessment of quality measures such as colorectal cancer screening rates are not widely appreciated, nor are their causes and possible solutions.MethodsUsing a billing code database derived from our institution’s electronic health records, we estimated the colorectal cancer screening rate of average-risk patients aged 50–74 years seen in primary care or gastroenterology clinic in 2016–2017. 200 records (150 unscreened, 50 screened) were sampled to quantify the accuracy against manual review.ResultsOut of 4611 patients, an analysis of billing data suggested a 61% screening rate, an estimate that matches the estimate by the Centers for Disease Control. Manual review revealed a positive predictive value of 96% (86%–100%), negative predictive value of 21% (15%–29%) and a corrected screening rate of 85% (81%–90%). Most false negatives occurred due to examinations performed outside the scope of the database—both within and outside of our institution—but 21% of false negatives fell within the database’s scope. False positives occurred due to incomplete examinations and inadequate bowel preparation. Reasons for screening failure include ordered but incomplete examinations (48%), lack of or incorrect documentation by primary care (29%) including incorrect screening intervals (13%) and patients declining screening (13%).ConclusionsBilling databases are prone to substantial bias that may go undetected even in the presence of confirmatory external estimates. Caution is recommended when performing population-level inference from these data. We propose several solutions to improve the use of these data for the assessment of healthcare quality.