Abstract
This was a descriptive and analytic study, the main objective of this study was to
Characterize Renal Lesions using computed tomography. The study was conduct at
the Al zytuona Hospital and altamyouz center for emergency During the period
from February 2018 to march 2018, Data was collected from patients referred to
the computed tomography scan department . the study included (50) patients from
different ages , male and female having different renal lesions . study has come out
with many result including that The renal lesions most common in male (60%)
than female (40%), The most effected group between (41-60) years ,the most
common lesions to be found was stone (46%), cyst (36%) , mass
(10%)cyst+mass(8%) respectively, Common site of renal lesion at the region of
lower pole (50%) , Cortex (22%) , Upper pole (16%) ,
Middle pole (12%) respectively . Therefore Radiological examination alone is
most suitable in order to diagnose the normal and abnormal appearance and
structure.The study recommended that Computed tomography should applied as a
best tool to diagnosis renal lesion. So it is provide measurement ct number,teture,
and shape of the lesion .
Hassan, R (2021). Characterization of Renal Lesions Using Computed Tomography Imaging. Afribary. Retrieved from https://afribary.com/works/characterization-of-renal-lesions-using-computed-tomography-imaging
Hassan, Roaa "Characterization of Renal Lesions Using Computed Tomography Imaging" Afribary. Afribary, 19 May. 2021, https://afribary.com/works/characterization-of-renal-lesions-using-computed-tomography-imaging. Accessed 21 Nov. 2024.
Hassan, Roaa . "Characterization of Renal Lesions Using Computed Tomography Imaging". Afribary, Afribary, 19 May. 2021. Web. 21 Nov. 2024. < https://afribary.com/works/characterization-of-renal-lesions-using-computed-tomography-imaging >.
Hassan, Roaa . "Characterization of Renal Lesions Using Computed Tomography Imaging" Afribary (2021). Accessed November 21, 2024. https://afribary.com/works/characterization-of-renal-lesions-using-computed-tomography-imaging