Proposing an intelligent technique based on radial basis function neural network to forecast the energy spectrum of diagnostic X-ray imaging systems.

In digital subtraction angiography (digital subtraction total cerebral angiography), cardiac and macrovascular cardiography, anorectal radiology, fluoroscopy, and computed tomography (CT), a prior knowledge to X-ray energy spectrum is crucial for assessing the image quality and also calculating patient X-ray dosage. The present investigation's main objective is to propose an intelligent technique for faster calculating X-ray energy spectrum of medical imaging systems with different exposure settings of tube voltage, filter material, and thickness based on limited specific spectra. In this study, Monte Carlo N Particle (MCNP) simulation code was initially used to generate some limited X-ray spectra for tube voltages of 20, 30, 40, 50, 80, 100, 130, and 150 kV for two different filters of beryllium and aluminum with thicknesses of 0. 4, 0.8, 1.2, 1.6 and 2 mm. Tube voltage, type, and thickness of filter were used as the 3 inputs of 150 Radial Basis Function Neural Network (RBFNN) to forecast point by point of the X-ray spectrum. After training, the RBFNNs could forecast most of the X-ray spectra for tube voltages in the range of 20-150 kV and two various filters of aluminum and beryllium with thicknesses in the range of 0-2 mm.

The significance of OLGA and OLGIM staging systems in the risk assessment of gastric cancer: a systematic review and meta-analysis.

BACKGROUND: Despite extensive research on the criteria for the assessment of gastric cancer risk using the Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastritis/Intestinal-Metaplasia Assessment (OLGIM) systems, no comprehensive overview or systematic summary on their use is currently available. AIM: To perform a systematic review and meta-analysis to assess the efficacy of the OLGA and OLGIM staging systems in evaluating gastric cancer risk. METHODS: We searched various databases, including PubMed, EMBASE, Medline, and Cochrane's library, for articles published before March 2017 on the association between OLGA/OLGIM stages and risk of gastric cancer. Statistical analysis was performed using RevMan 5.30 and Stata 14.0, with the odds ratio, risk ratio, and 95% confidence interval as the effect measures. RESULTS: A meta-analysis of six case-control studies and two cohort studies, comprising 2700 subjects, was performed. The meta-analysis of prospective case-control studies demonstrated a significant association between the OLGA/OLGIM stages III/IV and gastric cancer. The Newcastle-Ottawa Scale (NOS) score reflected heterogeneity in the case-control studies on OLGA. Subgroup analysis of high-quality (NOS score ≥ 5) studies showed an association between OLGA stage III/IV and increased risk of gastric cancer; the association was also high in the remaining study with low NOS score. The association between higher stages of gastritis defined by OLGA and risk of gastric cancer was significant. CONCLUSIONS: This correlation implies that close and frequent monitoring of such high-risk patients is necessary to facilitate timely diagnosis of gastric cancer.