Endoscopic features predictive of gastric cancer in superficial lesions with biopsy-proven high grade intraepithelial neoplasia

AIM:To investigate the macroscopic and clinicopathologic features of gastric cancer in patients with biopsy-suggested high grade intraepithelial neoplasia. METHODS:Patients with biopsy-confirmed gastric high grade intraepithelial neoplasia were reviewed from January 2001 to March 2008.Pathologic sections were re-evaluated by two senior pathologists. Patients with an en-bloc resection of the lesion within two months after the diagnosis of high grade intraepithelial neoplasia were enrolled in the study. Clinical manifestations,endoscopic features,biopsy and surgical pathology of all patients were collected and analyzed.The data acquired were subjected to univariate and multivariate analysis. RESULTS:Seventy-two superficial gastric lesions with a pathologic diagnosis of high grade intraepithelial neoplasia based on biopsy specimens were enrolled. True high grade intraepithelial neoplasia was finally proved in 16 lesions and gastric cancer in the rest 56 lesions,most of which(96.4%)were differentiated carcinomas.The result of univariate analysis indicatedthat the size and the presence of marked ulcer plaque or scar in a superficial lesion were independently associated with gastric cancer(P<0.05),when high grade intraepithelial neoplasia was diagnosed by biopsy pathology.The results of multivariate analysis revealed the size greater than 1.5 cm[odds ratio(OR)18.400,P<0.001]and the presence of 5-odd mm ulcer plaque or scar(OR 10.000,P=0.044)were associated with gastric cancer.Accordingly,the sensitivity,specificity and negative predictive value of multivariate analysis for predicting"true high grade intraepithelial neoplasia" was 87.5%,89.3%and 96.2%,respectively. CONCLUSION:Macroscopic findings are of value in differentiation between high grade intraepithelial neoplasia and superficial gastric cancer.This may simplify patient work-up and save costs for patients and healthcare system.

Optimized deployment of a radar network based on an improved firefly algorithm

The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.