Clinical and Anatomical Characteristics of Ischaemic Stroke Patients Undergoing Non-Contrast CT in a Nigerian Multicentre Cohort: Evidence of a Prolonged Stroke Onset-to-CT Scan Interval.

Authors

Ugochukwu Celestine Opara

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.|CT/MRI Department, Barnsley Hospital NHS Foundation Trust, United Kingdom. (United kingdom)

Anthony Chukwuka Ugwu

CT/MRI Department, Barnsley Hospital NHS Foundation Trust, United Kingdom. (Nigeria)

Christopher Chukwuemeka Ohagwu

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. (Nigeria)

Umeh, Emeka Chukwumuanya

CT Scanning Department, Humber Health Partnership (Hull University Teaching NHS Trust, Hull) (united kingdom)

Bestman Izuchukwu Oriaku

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.|Department of Interventional Radiology, University of Liverpool NHS Foundation Trust, United Kingdom. (United Kingdom)

Uche Andrew George

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.Department of Ultrasound, Northern Lincolnshire and Goole NHS Foundation Trust. Grimsby (United Kingdom)

Ifesie, Lawrence Daberechi

Department of Radiography and Radiological Sciences, Nnamdi Azikiwe University, Anambra state. (Nigeria)

Stanley Olisa Nwefuru

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.CT/MRI department North East Lincolnshire and Goole NHS Foundation Trust (United Kingdom)

Emmanuel Ayuba Buba

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.|MRI Department, Nottingham University Hospital NHS Trust, Nottingham, United Kingdom. (Nigeria)

Emeka Chukwumuanya Umeh

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.|CT department, Humber Health Partnership, Hull University Teaching NHS Trust, Hull, United Kingdom. (Nigeria)

Victor Kelechi Nwodo

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. (Nigeria)

Emeka Emmanuel Ezugwu

Department of Radiography and Radiological Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. (Nigeria)

Ikechukwu Kingsley Onuorah

Image Diagnostic Centre, Port Harcourt, Rivers State, Nigeria (Nigeria)

Article Information

DOI: 10.51244/IJRSI.2026.1309000002

Subject Category: Medical imaging.

Volume/Issue: 13/9 | Page No: 14-22

Publication Timeline

Submitted: 2026-09-09

Accepted: 2026-09-14

Published: 2026-10-05

Abstract

Background: Ischaemic stroke is a major cause of death and long-term neurological disability, and clinical outcomes are strongly affected by the interval between symptom onset and definitive assessment. Non-contrast computed tomography (NCCT) remains the principal initial neuroimaging examination for suspected acute stroke because it is rapid, widely available, relatively inexpensive, and effective for excluding intracranial haemorrhage. However, in resource-constrained settings, patients may reach diagnostic facilities several hours or days after stroke onset, thereby reducing opportunities for acute reperfusion therapy. This study describes the demographic, temporal and anatomical characteristics of patients with ischaemic stroke who underwent NCCT in a Nigerian cohort, with particular emphasis on evidence of delayed presentation.
Methods
A prospective cross-sectional study was done among patients with clinically diagnosed ischaemic stroke who underwent NCCT at three diagnostic centres in southeastern and south-southern Nigeria: St. Charles Medical Diagnostics Limited, Awka; Image Diagnostics, Owerri; and Image Diagnostics, Port Harcourt. The study period extended from December 2025 to May 2026. A total of 197 patients with radiologically confirmed ischaemic stroke were included. Demographic characteristics, post-ictal imaging interval, and anatomical distribution of cerebral infarction were evaluated. Stroke lesions were classified according to the documented interval between ictus and CT examination. Descriptive statistics were used to characterize the cohort.
Results
The study population comprised 197 patients, including 98 males (49.7%), 98 females (49.7%) and one patient with unrecorded sex. The mean age was 61.3 ± 12.6 years, with an age range of 27–90 years. Patients aged 60 years or older constituted 56.3% of the cohort. The mean post-ictal interval between stroke onset and CT imaging was 77.5 ± 34.8 hours, demonstrating substantial delay between symptom onset and neuroimaging assessment. Anatomically, extensive or multiple cerebral involvement was the most frequent CT finding, occurring in 49 patients (24.9%), followed by basal ganglia infarction in 44 patients (22.3%). General or unspecified cerebral involvement accounted for 36 cases (18.3%), while parietal lobe infarction occurred in 25 cases (12.7%). Other sites included the occipital lobe (5.6%), thalamus (5.1%), periventricular/intraventricular regions (3.0%), frontal lobe (3.0%), insular cortex (1.5%), cerebellum (1.5%) and temporal lobe (0.5%). Conclusion
This Nigerian NCCT cohort demonstrated a substantial burden of delayed presentation with the mean imaging interval occurring well beyond the conventional early treatment windows for acute reperfusion therapy. Older adults constituted the majority of the study population, while extensive/multifocal and basal ganglia involvement were the predominant anatomical patterns. The findings underscore the importance of improving stroke recognition, referral pathways, transportation, emergency imaging access, and early presentation in Nigerian stroke care. Routine NCCT remains an important and practical imaging tool in this setting and may provide a foundation for quantitative imaging approaches aimed to improving the characterization of infarct evolution.

Keywords

Ischaemic stroke; non-contrast computed tomography; delayed presentation; cerebral infarction; basal ganglia; Nigeria; stroke imaging.

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References

1. Aderinto, N., Ukoaka, B.M., Moradeyo, H., Wagwula, P.M., Udam, N.G., Muntaqim, O., Babalola, A.V., Ogieuhi, I.J., Wali, T.A., Oluwakorede, A.T., Babalola, A.E., Abraham, I.C., Olatunji, G. and Kokori, E. (2025) ‘Prevalence, characteristics, and treatment outcomes of stroke in Nigeria: a systematic review’, Next Research, 2(3), 100410. doi: 10.1016/j.nexres.2025.100410. [Google Scholar] [Crossref]

2. Alkali, N.H., Bwala, S.A., Akano, A.O., Osi-Ogbu, O., Alabi, P. and Ayeni, O.A. (2013) ‘Stroke risk factors, subtypes, and 30-day case fatality in Abuja, Nigeria’, Nigerian Medical Journal, 54(2), pp. 129–135. doi: 10.4103/0300-1652.110051. [Google Scholar] [Crossref]

3. Armi, L. and Fekri-Ershad, S. (2019) ‘Texture image classification based on improved local quinary patterns’, Multimedia Tools and Applications, 78(14), pp. 18995–19018. doi: 10.1007/s11042-019-7207-2. [Google Scholar] [Crossref]

4. Beishon, L.C. and Minhas, J.S. (2021) ‘Cerebral autoregulation and neurovascular coupling in acute and chronic stroke’, Frontiers in Neurology, 12, 720770. doi: 10.3389/fneur.2021.720770. [Google Scholar] [Crossref]

5. Blumenfeld, H. (2021) Neuroanatomy through Clinical Cases. 3rd edn. Oxford: Oxford University Press. [Google Scholar] [Crossref]

6. Campbell, B.C.V., De Silva, D.A., Macleod, M.R., Coutts, S.B., Schwamm, L.H., Davis, S.M. and Donnan, G.A. (2019) ‘Ischaemic stroke’, Nature Reviews Disease Primers, 5(1), 70. doi: 10.1038/s41572-019-0118-8. [Google Scholar] [Crossref]

7. Csutak, C., Ursu, D., Lebovici, A., Lenghel, M., Roman, A., Schiau, C., Stan, A., Stefan, R.A., Nicoara, M.I. and Stefan, P.A. (2026) ‘CT texture analysis of acute ischemic stroke: prediction of hemorrhagic transformation after thrombolysis’, Brain Sciences, 16(6), 556. doi: 10.3390/brainsci16060556. [Google Scholar] [Crossref]

8. Davies, E.R. (2008) ‘Introduction to texture analysis’. In: Mirmehdi, M., Xie, X. and Suri, J. (eds.) Handbook of Texture Analysis. London: Imperial College Press, pp. 1–31. doi: 10.1142/9781848161160_0001. [Google Scholar] [Crossref]

9. Feigin, V.L., Brainin, M., Norrving, B., Martins, S., Sacco, R.L., Hacke, W., Fisher, M., Pandian, J. and Lindsay, P. (2022) ‘World Stroke Organization (WSO): global stroke fact sheet 2022’, International Journal of Stroke, 17(1), pp. 18–29. doi: 10.1177/17474930211065917. [Google Scholar] [Crossref]

10. Flohr, T. and Ohnesorge, B. (2007) ‘Multi-slice CT technology’. In: Ohnesorge, B., Becker, C.R., Flohr, T., Knez, A. and Reiser, M.F. (eds.) Multi-slice and Dual-source CT in Cardiac Imaging: Principles, Protocols, Indications, Outlook. 2nd edn. Berlin: Springer, pp. 41–69. doi: 10.1007/978-3-540-49546-8_3. [Google Scholar] [Crossref]

11. Galloway, M.M. (1975) ‘Texture analysis using grey level run lengths’, Computer Graphics and Image Processing, 4(2), pp. 172–179. doi: 10.1016/S0146-664X(75)80008-6. [Google Scholar] [Crossref]

12. Imarhiagbe, F.A. and Ogbeide, E. (2011) ‘Clinical-imaging dissociation in strokes in a southern Nigerian tertiary hospital: review of 123 cases’, Nigerian Journal of Hospital Practice, 8(1–2), pp. 3–7. [Google Scholar] [Crossref]

13. Jäger, H.R. (2000) ‘Diagnosis of stroke with advanced CT and MR imaging’, British Medical Bulletin, 56(2), pp. 318–333. doi: 10.1258/0007142001903247. [Google Scholar] [Crossref]

14. Keris, V., Rudnicka, S., Vorona, V., Enina, G., Tilgale, B. and Fricbergs, J. (2001) ‘Combined intra-arterial/intravenous thrombolysis for acute ischaemic stroke’, American Journal of Neuroradiology, 22(2), pp. 352–358. [Google Scholar] [Crossref]

15. Kidwell, C.S., Villablanca, J.P. and Saver, J.L. (2000) ‘Advances in neuroimaging of acute stroke’, Current Atherosclerosis Reports, 2(2), pp. 126–135. doi: 10.1007/s11883-000-0107-z. [Google Scholar] [Crossref]

16. Komolafe, M.A., Komolafe, E.O., Fatoye, F., Adetiloye, V., Asaleye, C., Famurewa, O., Mosaku, S. and Amusa, Y. (2007) ‘Profile of stroke in Nigerians: a prospective clinical study’, African Journal of Neurological Sciences, 26(1), pp. 5–13. doi: 10.4314/ajns. v26i1.7588. [Google Scholar] [Crossref]

17. Lev, M.H. and Nichols, S.J. (2000) ‘Computed tomographic angiography and computed tomographic perfusion imaging of hyperacute stroke’, Topics in Magnetic Resonance Imaging, 11(5), pp. 273–287. doi: 10.1097/00002142-200010000-00004. [Google Scholar] [Crossref]

18. Ogun, S.A., Ojini, F.I., Ogungbo, B., Kolapo, K.O. and Danesi, M.A. (2005) ‘Stroke in southwest Nigeria: a 10-year review’, Stroke, 36(6), pp. 1120–1122. doi: 10.1161/01.STR.0000166182.50840.31. [Google Scholar] [Crossref]

19. Ohagwu, C.C. (2016) ‘Texture-based classification of brain tissues in non-contrast computed tomography images of stroke patients’, Journal of Neurology & Neurophysiology, 7(4), Supplement, p. 118. doi: 10.4172/2155-9562.C1.032. [Google Scholar] [Crossref]

20. Rao, C.R. (1973) Linear Statistical Inference and Its Applications. 2nd edn. New York: John Wiley & Sons. doi: 10.1002/9780470316436. [Google Scholar] [Crossref]

21. Saba, L. and Santoro, M. (2010) ‘Advanced imaging in acute stroke: perfusion and diffusion’, The Neuroradiology Journal, 23(3), pp. 155–164. [Google Scholar] [Crossref]

22. Sheskin, D.J. (2004) Handbook of Parametric and Nonparametric Statistical Procedures. 3rd edn. Boca Raton, FL: Chapman & Hall/CRC. doi: 10.4324/9780203489536. [Google Scholar] [Crossref]

23. Sundararajan, V. and Mahadevan, S. (2012) ‘Medical image texture analysis: methods and applications’, Journal of Medical Imaging and Health Informatics, 2(4), pp. 401–410. [Google Scholar] [Crossref]

24. Wardlaw, J.M., Smith, C. and Dichgans, M. (2013) ‘Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging’, The Lancet Neurology, 12(5), pp. 483–497. doi: 10.1016/S1474-4422(13)70060-7 [Google Scholar] [Crossref]

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