Hydatid cyst is a parasitic infection that primarily affects the liver but which can be found anywhere in the body. This case involves spontaneous dissemination of hydatid cyst disease, a rare occurrence in the absence of any intervention or trauma.

We sought to determine the influence of risk factors of chronic kidney disease (CKD) on cardiac calcification. We studied the correlation between coronary artery calcium score (CACS) and the type and duration of dialysis as well as the presence of diabetes mellitus and hypertension. The relation between calcium score and mortality was also analyzed. Patients with CKD attending the outpatient department or admitted in our hospital were included. They were subjected to high-resolution computerized tomography of the thorax to determine their CACS. Serum levels of intact parathyroid hormone (iPTH), highly sensitive C-reactive protein (hCRP), homocysteine, calcium, phosphorus, and calcium × phosphorus product were measured. Out of the 50 patients studied, 39 were hypertensive (78%), 32 were diabetic (64.4%), 20 were on hemodialysis, and 13 were on continuous ambulatory peritoneal dialysis. The mean CACS was 388.6. Twenty-nine patients had high iPTH levels and 92.9% of them had calcium score >400 (P = 0.013). Twenty-eight patients had high hCRP and 85.7% of these patients had calcium score >400 (P = 0.048). Patients on dialysis for more than two years had higher calcium score >400 (P = 0.035). 43% of diabetics had calcium score >400 (P = 0.008). All the six patients who died had calcium score >400 (P = 0). There was statistically no significant association noted between hypertension, high calcium x phosphorus product, and high homocysteine levels, and high calcium score. Our study suggests that higher values of iPTH, hCRP, and longer duration on dialysis are associated with accelerated cardiac calcification. Calcification scores >400 are associated with increased mortality.

TB and Lung cancer are major ailments of the lung.Patients with lung cancer are often misdiagnosed as pulmonary tuberculosis leading to delay in the correct diagnosis as well as exposure to inappropriate medication.The diagnosis of tuberculosis and lung cancer is difficult, as symptoms of both diseases are similar.Due to high TB prevalence and radiological similarities, a large number of lung cancer patients initially get wrongly treated for tuberculosis based on radiological picture alone. However, treating TB leads to inflammatory fibrosis in some of the patients. In all these cases, the diagnosis is confirmed only with a biopsy which is an invasive technique that is usually performed via Bronchoscopy or CT -guided biopsy. There comes the need of an efficient Computer Aided Diagnosis(CAD) of the fibrosis and adenocarcinoma diseases.The increased chance of characterizing tissues with the help of CAD and the achievable workload reduction for the radiologist demand the usage of these systems in CT screenings as well as daily hospital practice. Generally, the CAD is designed based on the Region of Interest(ROI) given by the radiologist which makes the system semi-automatic. Our work presents a fully automated method of characterization of carcinoma from other lung abnormalities namely fibrosis and suspicious of tuberculosis. A comparison study is also done by evaluating the performance of Neural Network Classifier with three set of features.

The diagnosis of tuberculosis and lung cancer can be difficult, as symptoms of both diseases are similar. Due to high TB prevalence and radiological similarities, a large number of lung cancer patients initially get wrongly treated for tuberculosis based on radiological picture alone. However, treating TB leads to inflammatory fibrosis in some of the patients. In all these cases, the diagnosis is confirmed only with a biopsy which is an invasive technique that is usually performed via Bronchoscopy or CT -guided biopsy. There comes the need of an efficient Computer Aided Diagnosis(CAD) of the fibrosis and adenocarcinoma diseases.The increased chance of characterizing tissues with the help of CAD and the achievable workload reduction for the radiologist demand the usage of these systems in CT screenings as well as daily hospital practice. Generally, the CAD is designed based on the Region of Interest(ROI) given by the radiologist which makes the system semi-automatic. Our work presents a fully automated method of characterization of carcinoma from other lung abnormalities namely fibrosis and suspicious of tuberculosis. The performance of NN classifier before and after factor analysis are evaluated by Receiver Operating Characteristics(ROC) curve. A comparison study is also done with three set of features. These feature set include entropy and parameters extracted by Gray Level Co-Occurrence Matrix(GLCM) and Gray Level Run Length Matrix(GLRLM).

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