|
| |
 |
|
| ORIGINAL ARTICLE |
|
| Year : 2019 | Volume
: 2
| Issue : 3 | Page : 153-156 |
|
Patterns of computed tomographic findings in patients from Maiduguri, Nigeria, diagnosed with a brain tumor
Alhaji Modu Ali1, Maryam Abdullahi Buji2, Ahmed Abubakar2
1 Department of Radiography and Radiological Sciences, College of Health Sciences and Technology, Nnamdi Azikwe University, Nnewi; Department of Radiology, Federal Neuro-Psychiatric Hospital, Maiduguri, Nigeria
2 Department of Radiology, Federal Neuro-Psychiatric Hospital, Maiduguri, Nigeria
| Date of Submission | 22-Mar-2019 |
| Date of Decision | 24-Apr-2019 |
| Date of Acceptance | 09-Jul-2019 |
| Date of Web Publication | 26-Sep-2019 |
Correspondence Address:
Dr. Alhaji Modu Ali
Department of Radiography and Radiological Sciences, College of Health Sciences and Technology, Nnamdi Azikwe University, Nnewi Campus, P.M.B 5001, Nnewi, Anambra State
Nigeria
 Source of Support: None, Conflict of Interest: None  | 2 |
DOI: 10.4103/glioma.glioma_11_19
Background and Aim: Brain tumors are a fairly common neurological problem in Nigeria and associated with a relatively low morbidity and mortality rate. Magnetic resonance imaging is the best imaging modality revealing precisely the tumor's location, patterns, and to some extent, the tumor characterization; however, only computed tomography (CT) is readily available in the study locality. In this study, we assessed the patterns of CT findings among patients diagnosed with a brain tumor using CT.
Materials and Methods: This was a retrospective study, in which brain CT records of 40 cases of brain tumor diagnosed between January 2016 and August 2018 were reviewed, irrespective of patient age, sex, or clinical information. This study was approved by the Human Research Ethical Committee of the Federal Neuro-Psychiatric Hospital, Maiduguri (approval No. FNPH/GEN/092/VOLII) on December 22, 2015.
Results: Of the 40 brain tumors diagnosed during the study, 17 (42%) cases were male and 23 (58%) were female. Their age range was 2–70 years (28.4 ± 20.2 years). About 22% of cases were extra-axial, whereas 31 (78%) were intra-axial. Twenty-seven (68%) patients had definitive diagnosis, with eight (20%) cases being meningioma, whereas 13 (32%) had nonspecific findings (a longer differential diagnosis).
Conclusion: Meningioma was the most common type of brain tumor in this study despite the limitation of histopathology facility within the immediate locality. The low rate of glioma was probably due to few old adults included in the study.
Keywords: Brain tumor, computed tomography, meningioma, neuroimaging, Nigeria
How to cite this article:
Ali AM, Buji MA, Abubakar A. Patterns of computed tomographic findings in patients from Maiduguri, Nigeria, diagnosed with a brain tumor. Glioma 2019;2:153-6 |
How to cite this URL:
Ali AM, Buji MA, Abubakar A. Patterns of computed tomographic findings in patients from Maiduguri, Nigeria, diagnosed with a brain tumor. Glioma [serial online] 2019 [cited 2023 Oct 2];2:153-6. Available from: http://www.jglioma.com/text.asp?2019/2/3/153/267915 |
| Introduction | |  |
Brain tumors are a group of intracranial solid primary tumors of the central nervous system (CNS) and secondary neoplasms originating from hematogenous spread from remote sites.[1] Basically, there are two types of brain tumors, namely benign (noncancerous) and malignant (cancerous).[2] Malignant brain tumors are regarded as one of the most deadly diseases.[3] Malignant brain tumors are further subdivided into primary and secondary brain tumors. Primary brain tumors are the tumors that originated in the brain and named after the cell types from which they originated. The secondary brain tumors are those that have spread to the brain from somewhere in the body (metastasis). According to the World Health Organization classification, brain tumors are classified into 120 types based on the locations and cells involved. Gliomas, meningiomas, schwannomas, CNS lymphomas, and pituitary tumors are the most common primary brain tumors found in the adult.[4] The global incidence of primary malignant intracranial tumors is approximately 3.7/100,000 population for males and 2.6/100,000 population for females.[5],[6] Brain tumor is the second major cause of death from neurological diseases.[7]
A brain tumor may increase the intracranial pressure and may cause damage to the brain.[8] Some of the major symptoms of this disease include headaches, dizziness, seizures, blurred vision, nausea, vomiting, amnesia, and mental changes.[9],[10] The cause of most brain tumors is unknown.[11] Rare risk factors include inherited neurofibromatosis, exposure to vinyl chloride, Epstein–Barr virus, genetic factors, and ionizing radiation.[12]
Neuroimaging plays a key role in the clinical management of patients with brain tumors. A neuroimaging technique gives an opportunity to integrate functional, hemodynamic, and anatomic information into the evaluation of brain tumor patients. These imaging modalities are being used for presurgical diagnosis and grading of brain tumors, to monitor and to assess the treatment response and prognosis.[13] Computed tomography (CT) and magnetic resonance imaging (MRI) play a very important role in the diagnosis of intracranial tumors because of their high sensitivity. Their high-positive and low-negative predictive values also make them reliable noninvasive diagnostic tools, especially when the tumor is difficult to access for biopsy.[14] MRI is considered to be the primary imaging modality in brain tumor patients because of its superior contrast resolution, high sensitivity, and lack of ionizing radiation. However, MRI is costly and not readily available in developing countries such as Nigeria. Conversely, CT is sensitive, relatively cheap, and readily available. We sought to determine the patterns of CT findings in patients diagnosed with brain tumor using CT in the study locality which will help in the treatment planning for the diagnosed patients.
| Materials and Methods | | |
Study design
Brain CT records of 40 patients referred to the Department of Radiology, Federal Neuro-Psychiatric Hospital, Maiduguri, Nigeria, from January 2016 to August 2018 with a CT diagnosis of “brain tumor” based on CT appearances coupled with clinical information were retrospectively reviewed. Data were retrieved from radiological information systems and a CT archive where all the CT reports and the images, respectively, were stored. All the CT examinations were carried out on a 16-detector CT (Brightspeed ®, General Electric, Waukesha, WI, USA).
Ethical considerations
In line with the Helsinki Declaration, ethical approval for this study was obtained from the Human Research Ethical Committee of the Federal Neuro-Psychiatric Hospital, Maiduguri (approval No. FNPH/GEN/092/VOLII) on December 22, 2015. In addition, written informed consent was obtained from each patient/relative after the interpretation of the images.
Data collection
The reports and images were reviewed by two radiologists and an experienced radiographer.
Data analysis
Descriptive statistics (frequency and percentage) were used to analyze the data, and statistical analysis was performed using the Statistical Package for the Social Sciences software version 22.0 (IBM, Armonk, NY, USA).
| Results | | |
In total, 40 brain tumor cases were reviewed, comprising 17 cases (42%) who were male, with a male-to-female ratio of 1:1.5. Their age range was 2–70 years (28.4 ± 20.2 years).
Tumor location
Extra-axial and intra-axial tumors were numbered 9 (22%) and 31 (78%), respectively [Figure 1]. Out of the nine (22%) extra-axial tumors, one (2%) was infratentorial, whereas the remaining eight (20%) were supratentorial. For the intra-axial tumors, 11 were in the frontal lobe (28%), whereas the parietal lobe and cerebellum had four (10%) each. There was one frontoparietal and one ventricular mass (2%) as shown in [Table 1]. | Figure 1: Coronal images showing intracranial masses. An image showing extra-axial masses extending into both orbits (arrows) in a 22-year-old woman (A). An image showing intra-axial masses in the left basal ganglia (arrow) in a 6-year-old boy (B). R: Right
Click here to view |
Tumor density
Hypodensity and isodensity were found in 15 (38%) and 3 (8%) cases, respectively, whereas hyperdensity and mixed density were in 11 (28%) cases each [Table 2].
A mass effect with ventricular compression was observed in 37 (93%) cases, of which 15 (40%) cases had mild compression of ventricles and midline shift (midline shift of <5 mm), 10 (27%) showed moderate mass effect (midline shift from 5 to 10 mm), and 12 (32%) with severe compression of the ventricle and midline shift (midline shift of >5 mm) [Figure 2] and [Figure 3]. | Figure 2: A noncontrast-enhanced computed tomography image of a 6-year-old boy with a huge well-circumscribed mass with heterogeneous density, involving the left basal ganglia of 7.4 cm × 6.5 cm × 5.8 cm for length, breadth, and height, respectively (arrow). R: Right
Click here to view |
 | Figure 3: Pie chart showing the different levels of mass effect and compression caused by the brain tumor with mild mass effect constituting the greatest proportion
Click here to view |
Diagnosis based on computed tomography finding
Of the 40 cases, 27 (68%) had specific findings and/or diagnosis, with eight (20%) meningioma comprising the highest proportion of intracranial tumor, whereas 13 (32%) of the findings were nonspecific (a longer differential diagnosis), as shown in [Table 3].
| Discussion | | |
CT is very sensitive in detecting brain lesions with high-positive predictive value and low-negative predictive value. This makes CT a very reliable diagnostic tool, especially when a tumor is difficult to access for biopsy.[10] However, CT scans have low specificity for brain tumors, thus definitive diagnosis can only be confirmed by histological examination of the brain tissue samples obtained either through a brain biopsy or surgery.[14]
In this study, the mean age of the patients was 28.4 ± 20.2 years, with a male-to-female ratio of 1:1.5. Therefore, the finding that young and middle age groups had a higher prevalence of risk factors for brain tumor was probably due to the use of advanced technology.[15] This finding is in agreement with that of Soyemi and Oyewole [16] who reported a mean age of 36 ± 20.4 years, with a male-to-female ratio of 1:1.1 among brain tumor patients. A similar finding has been reported by Tuly [17] whose patients were at a mean age of 35.71 ± 1.09 years; however, the male-to-female ratio of 1.5:1 was contrary to the current study.
In this study, intra-axial tumors (78%) were more common than extra-axial tumors (22%). This finding correlates with the results of Karpagam and Vadanika [18] who reported 68% and 32% as intra-axial and extra-axial, respectively. A similar finding has been documented by Gowri and Anil [19] who reported 78% and 22% as intra-axial and extra-axial brain tumors, respectively.
In this study, intra-axial tumors affected the frontal lobe more than any other part of the brain. Up to 11 (28%) cases were located in the frontal lobe, while 4 (10%) were found in the parietal lobe and 4 (10%) in the cerebellum. Conversely, Tuly [17] reported that the pituitary gland as being the most affected part (55.4%), followed by the parietal lobe (18.2%) among brain tumor adult patients in the National Institute of Neurosciences and Hospital, Dhaka, in Bangladesh. In another study, the occipital lobe was the most affected part (10.8%) among brain tumor patients.[20] This variation could be due to racial and environmental variations in addition to the limited sample size used in this study.
In this study, hypodense tumors accounted for the highest proportion (38%) of cases, followed by hyperdense and mixed density, each constituting 28% of cases. This finding correlates well with the results of earlier studies by Preethi and Mariappan [21] and Haque et al.[22] who found that hypodense tumors constituted a greater proportion (38% and 53%, respectively) of cases.
In the present study, mass effect with various degrees of compression was observed in 37 (92%) cases. Out of the 37 cases, the number of mild, moderate, and significant compression was 15, 10, and 12, respectively. These findings are contrary to the results of the earlier studies that reported 68% and 51% cases, respectively.[23],[24] The reason may be likely to be because of the relatively small sample size (n = 40) compared with other studies (n = 100) and geographical variation.
| Conclusion | | |
In summary, the most common intracranial tumor in this study was meningioma, followed by astrocytomas. These findings are close to the results of earlier studies that reported astrocytomas as the most common finding followed by meningioma.[14],[16]
A limitation of this study is the absence of histopathology facility within the hospital to confirm the CT findings. Despite this limitation, we are able to describe some of the patterns of a brain tumor using a CT scanner. It can be concluded from this study that among the different varieties of brain tumors in our study population, meningioma is the most common, the location of the tumor is commonly in the frontal lobe, and brain tumors are more common in females than males.
Acknowledgements
The authors wish to acknowledge the management and staff in Department of Radiology, Federal Neuro-Psychiatric Hospital, Maiduguri, Nigeria for the permission and their efforts respectively during data collection.
Financial support and sponsorship
Nil.
Institutional review board statement
In line with the Helsinki Declaration, ethical approval for this study was obtained from the Human Research Ethical Committee (HREC) of the Federal Neuro-Psychiatric Hospital, Maiduguri (approval No. FNPH/GEN/092/VOLII) on December 22, 2015.
Declaration of patient consent
The authors certify that they have obtained the appropriate patient consent form. In the forms, the patients or their legal guardians have given their consent for the patients' images and other clinical information to be reported in the journal. The patients or their legal guardians understood that the patients' names and initials would not be published and due efforts would be made to conceal their identity.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Idowu O, Akang E, Malomo A. Symptomatic primary intracranial neoplasms in Nigeria, West Africa. J Neurol Sci Turk 2007;24:212-8.  |
| 2. | Kothari A, Indira B. A study on classification and detection of brain tumor techniques. Int J Comput Eng Inf Technol 2015;6:30-5. |
| 3. | Vijay Kumar G, Raju GV. A real-time approach to brain tumor detection implementing wavelets and ANN. Int J Comput Sci Eng 2015;3:89-93. |
| 4. | Hamoud Al-Tamimi MS, Sulong G. Tumor brain detection through MR images: A review of literature. J Theor Appl Inf Technol 2014;62:387-403. |
| 5. | GLOBOCAN. Worldwide Incidence and Mortality of Cancer. Lyon, France: IARC Press; 2002. |
| 6. | Parkin DM, Whelan SL, Ferlay J, Teppo L, Thomas DB. Cancer in Five Continents. Lyon, France: IARC Press; 2002. |
| 7. | Lewis PR, Timothy AP. General considerations. In: Deangelis LM, Rosenfeld SS, editors. Merritt's Neurology. Philadelphia, USA: Lippincott Williams and Wilkins; 2010. p. 369-77. |
| 8. | Moitra D, Mandal R. Review of brain tumor detection using pattern recognition techniques. Int J Comput Sci Eng 2017;5:121-3. |
| 9. | Rana R, Singh P. Brain tumor detection through MR images: A review of literature. IOSR J Comput Eng 2015;17:7-18. |
| 10. | Lowenstein DH. Seizures and epilepsy. In: Fauci AS, Kasper DL, Jameson JL, Stephen LH, Dan LL, Joseph L, eds. Harrison's Principles of Internal Medicine. 18 th ed., Ch. 369. New York, USA: The McGraw Hill Companies; 2012. p. 3258. |
| 11. | William PH. Intracranial Tumors: Neurology in Africa. Bergen, Norway: Bodoni; 2012. |
| 12. | |
| 13. | Bangiyev L, Rossi Espagnet MC, Young R, Shepherd T, Knopp E, Friedman K, et al. Adult brain tumor imaging: State of the art. Semin Roentgenol 2014;49:39-52. |
| 14. | Taghipour Zahir SH, Rezaei Sadrabadi M, Dehghani F. Evaluation of diagnostic value of CT scan and MRI in brain tumors and comparison with biopsy. Iran J Pediatr Hematol Oncol 2011;1:121-5. |
| 15. | Naeem Z. Health risks associated with mobile phones use. Int J Health Sci (Qassim) 2014;8:V-VI. |
| 16. | Soyemi SS, Oyewole OO. Spectrum of intracranial tumours in a tertiary health carefacility: Our findings. Pan Afr Med J 2015;20:24. |
| 17. | Tuly TI. The Pattern of Brain Tumour among Admitted Patients in a Specialized Center of Dhaka City: A Cross-Sectional Study. Bangladesh: BRAC University; 2012. |
| 18. | Karpagam B, Vadanika V. Brain space occupying lesions by magnetic resonance imaging: A prospective study. Int J Sci Stud 2015;3:123-7. |
| 19. | Gowri SK, Anil KK. Magnetic resonance imaging in the evaluation of supratentorial tumours. Int J Health Res Mod Integr Med Sci 2015;2:16. |
| 20. | Majid H, Ali Z, Rahman MR, Akhter A, Rajib R, Saha U. Histological subtypes of brain tumor: Pattern in a hospital in Dhaka, Bangladesh. Chatt Maa Shi Hosp Med Coll J 2013;12:9-11. |
| 21. | Preethi GR, Mariappan M. Computed tomography evaluation of supratentorial brain tumours. Ann Int Med Dent Res 2017;3:RD22-7. |
| 22. | Haque MZ, Karim ME, Al-Azad S, Mahmood-Uz-Jahan. Role of computed tomography in the evaluation of pediatric brain tumor. Bangladesh Med Res Counc Bull 2010;36:89-92. |
| 23. | Obajimi MO, Ogbole GI, Adeniji-Sofoluwe AT, Adeleye AO, Elumelu TN, Oluwasola AO, et al. Cranial computed tomographic findings in Nigerian women with metastatic breast cancer. Niger Med J 2013;54:123-8. [ PUBMED] [Full text] |
| 24. | Trivedi A, Thakkar J, Maulik J, Ishita V. CT & MRI evaluation of brain tumour & tumour like conditions. Int J Sci Res 2017;6:730-4. |
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3]
|
Search Pubmed for