Background: Pleural effusion is a common clinical
problem.
Objective: The aim of the study was to evaluate the
diagnostic utility of Carcino embryonic antigen
(CEA), CA 15- 3, and alpha-feto protein ( AFP ) as
a tumor markers in serum and pleural effusion and
evaluate the value of combining them as a diagnostic
tools that are complementary to cytology in the
diagnosis of malignancies .
Methods: Forty patients (18 malignant and 22 benign
pleural effusion) were included in this study .The
serum and effusion levels of CEA, CA 15 – 3 and
AFP were measured using immunoradiometric assay
Results: from the 40 effusions studied 26 were
exudates and 14 were transudates. The level of
pleural effusions of CEA, CA 15 – 3 and AFP were
increased above the cutoffs in 72.5%, 94.4 % and 5.5
% of tested samples with malignancies respectively.
A direct strong significant correlation between serum
and pleural fluid CEA, CA 15 – 3 and AFP was
noted.
Conclusion: Pleural effusion CEA is the most
accurate marker for the diagnostic separation of
malignant and benign. The combination of both CEA,
CA 15 – 3 improves the sensitivity by up to 11 %.
AFP has no role in the process
Background: Parotid gland tumors account for 80% of all salivary gland neoplasms, 20% of these are malignant, but in daily clinical practice most parotid masses are operated on before obtaining the final histological diagnosis. This clinical setting further complicates the critical point of parotid surgery, which is the management of the facial nerve. Materials and methods: 45 patients underwent parotidectomy for benign and malignant neoplasms. A complete history is collected from the patients with the duration and the site of the tumor, the facial nerve examined and its associations, a medical consultation done for opinion and management. Clinical examination with facial nerve was mandatory to avoid any mistakes that may occur. The most si
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Seventy exudative lymphocytic pleural fluid specimens of patients with suspected tuberculous pleural effusion submitted to the National Reference Laboratory of tuberculosis/Baghdad from October 2012 to February 2013. These effusions were due to tuberculosis pleuritis (n=12) and non-tuberculosis pleuritis (n=58). The following parameters were analyzed: protein concentration, glucose concentration, lactate dehydrogenase (LDH) concentration and adenosine deaminase activity (ADA). As a result, the protein concentration was higher in TPE patients (8.80 ± 0.89 g/dl) than it's concentration in non-TPE patients (7.61 ± 0.54 g/dl), as well as LDH concentration was (3366.58 ± 284.28 U/L) in TPE patients and (3024.12 ± 116.84 U/L) in non-TPE pa
... Show MoreTest method was developed radioimmunotherapy to appoint in two groups of patients infected with a uterine tumor Great conditions in tumor tissue benign and malignant Ddh teacher radioactive iodine isotope
the sera levels of luteninizing hormone were investgaited prior tq surgery in 10 postmenopaisal women with benign and 10 postmenopausal women with maliganant healthy
kinetic studies were carried out the uterine homogenate time course of the association of with LH in benign and malignant uterine
Background: Characterization of the ovarian masses preoperatively is important to inform the surgeon about the possible management strategies. MRI may be of great help in identifying malignant lesion before surgery. Diffusion Weighted Imaging (DWI) is a sensitive method for changes in proton of water mobility caused by pathological alteration of tissue cellularity, cellular membrane integrity, extracellular space perfusion, and fluid viscosity.
Objective: to study the diagnostic accuracy of DWI in differentiation between benign and malignant ovarian masses.
Type of the study:Cross-sectional study.
Methods: this study included 53with complex
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This work is aimed to design a system which is able to diagnose two types of tumors in a human brain (benign and malignant), using curvelet transform and probabilistic neural network. Our proposed method follows an approach in which the stages are preprocessing using Gaussian filter, segmentation using fuzzy c-means and feature extraction using curvelet transform. These features are trained and tested the probabilistic neural network. Curvelet transform is to extract the feature of MRI images. The proposed screening technique has successfully detected the brain cancer from MRI images of an almost 100% recognition rate accuracy.
People may believe that tissue of normal brain and brain with benign tumor
have the same statistical descriptive measurements that are significantly different
from the of brain with malignant tumor. Thirty brain tumor images were collected
from thirty patients with different complains (10 normal brain images, 10 images
with benign brain tumor and 10 images with malignant brain tumor). Pixel
intensities are significantly different for all three types of images and the F-test was
measured and found equal to 25.55 with p-value less than 0.0001. The means of
standard deviations and coefficients of variation showed that pixel intensities from
normal and benign tumors images are almost have the same behavior whereas the
Breast cancer has got much attention in the recent years as it is a one of the complex diseases that can threaten people lives. It can be determined from the levels of secreted proteins in the blood. In this project, we developed a method of finding a threshold to classify the probability of being affected by it in a population based on the levels of the related proteins in relatively small case-control samples. We applied our method to simulated and real data. The results showed that the method we used was accurate in estimating the probability of being diseased in both simulation and real data. Moreover, we were able to calculate the sensitivity and specificity under the null hypothesis of our research question of being diseased o
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