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Establishment of Local Diagnostic Reference Levels for Trunk Computed Tomography Examinations at Governmental Hospitals in the Gaza Strip: A Cross-Sectional Study

Ayyad, A.; Al Ajerami, Y.; Najim, A.; H. Mansour, H.; Abdul Karim, M. K.; Alghamdi, F.; Mokbel, K.

2025-12-30 radiology and imaging
10.64898/2025.12.30.25343210 medRxiv
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ObjectiveTo establish the local diagnostic reference levels (LDRLs) for trunk multi-slice CT (MSCT) examinations in the Gaza Strip, Palestine. MethodA cross-sectional study of adult oncology patients undergoing trunk MSCT at two governmental hospitals in Gaza Strip; Al Shifa Medical Complex (SMC) and Al Aqsa Hospital (AMH), using an adapted dose survey booklet. Data collected from July 2019 to March 2020 included patient characteristics, volumetric CT dose index (CTDIvol) and dose length product (DLP). Descriptive, univariate and multivariate analyses identified key factors affecting radiation dose, and the coefficient of variation between scanner and software-derived dose values was also determined. ResultsA total of 170 trunk CT examinations were analysed (57.1% SMC, 42.9% AMH). 73% were female; the mean age of the participants is 53.1{+/-}15.8 years, and the mean body mass index (BMI) was 30{+/-}6.1. The estimated LDRLs for trunk CT were 13 mGy for CTDIvol and 1010.4 mGy{middle dot}cm for DLP. There was notable variation between hospitals in CTDIvol and DLP (p<0.001). At SMC, factors such as tube current, peak kilovoltage, scan length, pitch and BMI significantly affected dose indices. In contrast, at AMH, the main influences were tube current and scan length. CTDIvol had a greater impact on DLP than scan length at both locations. ConclusionLDRLs for trunk CT scans in the Gaza Strip were established and found to be generally comparable to international benchmarks. Notable variation in doses between hospitals indicates potential for improvement through standardising protocols, managing scan lengths and using techniques tailored to patient size.

Published in Radiological Physics and Technology · not in our set (fewer than 10 published preprints to learn from) · training set

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