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Predictors of malignancy in chronic calcific pancreatitis with head mass

World Journal of Gastrointestinal Surgery 2013年第4期5卷 97-103页

作者： senthilkumar perumal Ravichandran Palaniappan Sastha Ahanatha Pillai Vimalraj Velayutham Jeswanth Sathyanesan Institute of Surgical Gastroenterology and Liver Transplantation Government Stanley Medical College

AIM: To prospectively analyse the clinical, biochemical and radiological characteristics of the mass lesions arising in a background of chronic calcific pancreatitis (CCP). METHODS: Eighty three patients, who presented with chronic pancreatitis (CP) and a mass lesion in the head of pancreas between February 2005 and December 2011, were included in the study. Patients who were identified to have malignancy underwent Whipple' s procedure and patients whose investigations were suggestive of a benign lesion underwent Frey's procedure. Student t-test was used to compare the mean values of imaging findings [common bile duct (CBD), main pancreatic duct (MPD) size] and laboratory data [Serum bilirubin, carbohydrate antigen 19-9 (CA 19-9)] between the groups. Receiver operating characteristic curve (ROC curve) analysis was done to calculate the cutoff valves of serum bilirubin, CA 19-9, MPD and CBD size. The sensitivity, specificity, positive predictive valve (PPV) and negative predictive value (NPV) were calculated using these cut off points. Multivariate analysis was performed using logistic regression model. RESULTS: The study included 56 men (67.5%) and 27 women (32.5%). Sixty (72.3%) patients had tropical calcific pancreatitis and 23 (27.7%) had alcohol related CCP. Histologically, it was confirmed that 55 (66.3%) of the 83 patients had an inflammatory head mass and 28 (33.7%) had a malignant head mass. The mean age of individuals with benign inflammatory mass and those with malignant mass was 38.4 years and 45 years respectively. Significant clinical features that predicted a malignant head mass in CP were presence of a head mass in CCP of tropics, old age, jaundice, sudden worsening abdominal pain, gastric outlet obstruction and significant weight loss (P ≤ 0.05). The ROC curve analysis showed a cut off value of 5.8 mg/dL for serum bilirubin, 127 U/mL for CA 19-9, 11.5 mm for MPD size and 14.5 mm for CBD size. CONCLUSION: Elevated Serum bilirubin and CA 19-9, and dila

关键词： Chronic calcific pancreatitis Pancreatic head mass Carcinoma head of pancreas Risk factors of malignancy

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BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

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Computer Systems Science & Engineering 2023年第10期47卷 777-790页

作者： A.Richard William J.senthilkumar Y.Suresh V.Mohanraj Er perumal Manimekalai College of Engineering Hosur636352Tamil NaduIndia Sona College of Technology Salem636005Tamil NaduIndia

In cloud computing Resource allocation is a very complex *** the customer demand makes the challenges of on-demand resource *** challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of *** solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research *** accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the *** similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using *** an unexpected circumstance SVM model can make a resource allocation *** efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an *** proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.

关键词： Bat algorithm with feature selection(BFS) support vector machine(SVM) multiple-input multiple output(MIMO) quality of service(QoS) classifier cloud computing

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