Assessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorization

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dc.contributor.advisorBrink, Anitaen_ZA
dc.contributor.advisorMann, Michael Den_ZA
dc.contributor.authorYunusa, Garba Harunaen_ZA
dc.date.accessioned2016-02-05T07:18:53Z
dc.date.available2016-02-05T07:18:53Z
dc.date.issued2015en_ZA
dc.descriptionIncludes bibliographical referencesen_ZA
dc.description.abstractObjectives: To assess initial experience with the use of a new SPECT-CT in the evaluation of lesions. Methods: The folder number, radiopharmaceutical used and type of scan of patients examined with a new Siemens T6 SPECT-CT between 2 April and 31 December 2013 were retrieved. The number of 99m Tc -MDP bone scans was sufficient for a detailed analysis. The scans were re-processed and reported by the observer before he was given any clinical information. Whole body planar, whole body planar plus SPECT and whole body planar plus SPECT-CT images were assessed successively in three separate sessions at least two weeks apart. At each session, the certainties of detection, localisation, and categorisation of each lesion were recorded. Results: A total of 539 lesions were seen on the whole body, SPECT and CT images in 133 patients. The whole body images showed no lesions in three patients and 378 lesions in 130 patients, 117(31%) lesions in areas not covered by the SPECT. SPECT detected 122 additional lesions in 79 patients. Thirty-nine (12.2%) lesions were seen only on CT in 32 (24.1%) patients. F or the 261 lesions seen on the planar images in the SPECT FOV, lesion detection was definite in 233 (89.3%), localisation definite in 151(57.9%) and categorisation definite in 123 (47.1%) lesions. On the SPECT, definite lesion detection, localisation and categorisation were recorded respectively for 259 (99.2%), 228 (87.4%) and 176 (67.4%) of the 261 lesions. Lesion detection, localisation and categorisation certainties were definite for 100%, 99.1% and 94.7% of the SPECT-CT lesions respectively. Conclusion: Whole body planar scintigraphy is essential in lesion detection. SPECT markedly improves lesion detection and localisation and CT enhances lesion categorisation.en_ZA
dc.identifier.apacitationYunusa, G. H. (2015). <i>Assessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorization</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Division of Nuclear Medicine. Retrieved from http://hdl.handle.net/11427/16777en_ZA
dc.identifier.chicagocitationYunusa, Garba Haruna. <i>"Assessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorization."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Division of Nuclear Medicine, 2015. http://hdl.handle.net/11427/16777en_ZA
dc.identifier.citationYunusa, G. 2015. Assessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorization. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Yunusa, Garba Haruna AB - Objectives: To assess initial experience with the use of a new SPECT-CT in the evaluation of lesions. Methods: The folder number, radiopharmaceutical used and type of scan of patients examined with a new Siemens T6 SPECT-CT between 2 April and 31 December 2013 were retrieved. The number of 99m Tc -MDP bone scans was sufficient for a detailed analysis. The scans were re-processed and reported by the observer before he was given any clinical information. Whole body planar, whole body planar plus SPECT and whole body planar plus SPECT-CT images were assessed successively in three separate sessions at least two weeks apart. At each session, the certainties of detection, localisation, and categorisation of each lesion were recorded. Results: A total of 539 lesions were seen on the whole body, SPECT and CT images in 133 patients. The whole body images showed no lesions in three patients and 378 lesions in 130 patients, 117(31%) lesions in areas not covered by the SPECT. SPECT detected 122 additional lesions in 79 patients. Thirty-nine (12.2%) lesions were seen only on CT in 32 (24.1%) patients. F or the 261 lesions seen on the planar images in the SPECT FOV, lesion detection was definite in 233 (89.3%), localisation definite in 151(57.9%) and categorisation definite in 123 (47.1%) lesions. On the SPECT, definite lesion detection, localisation and categorisation were recorded respectively for 259 (99.2%), 228 (87.4%) and 176 (67.4%) of the 261 lesions. Lesion detection, localisation and categorisation certainties were definite for 100%, 99.1% and 94.7% of the SPECT-CT lesions respectively. Conclusion: Whole body planar scintigraphy is essential in lesion detection. SPECT markedly improves lesion detection and localisation and CT enhances lesion categorisation. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Assessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorization TI - Assessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorization UR - http://hdl.handle.net/11427/16777 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/16777
dc.identifier.vancouvercitationYunusa GH. Assessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorization. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Division of Nuclear Medicine, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/16777en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Nuclear Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherNuclear Medicineen_ZA
dc.titleAssessment of the impact of the application single photon emission computerized tomography and SPECT-CT on lesion catergorizationen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMMeden_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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