Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation Analysis Article Swipe
Mohammad AlShurbaji
,
Sara El Haout
,
Akchunya Chanchal
,
Salam Dhou
,
Entesar Z. Dalah
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14031071
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14031071
Computed tomography (CT) is a widely utilized diagnostic imaging modality in medicine. However, the potential risks associated with radiation exposure necessitate investigating CT exams to minimize unnecessary radiation. The objective of this study is to evaluate how patient-related parameters impact the CT dose indices for different CT exams. In this study, a dataset containing CT dose information for a cohort of 333 patients categorized into four CT exams, chest, cardiac angiogram, cardiac calcium score and abdomen/pelvis, was collected and retrospectively analyzed. Regression analysis and Pearson correlation were applied to estimate the relationships between patient-related factors, namely body mass index (BMI), weight and age as input variables, and CT dose indices, namely the volume CT dose index (CTDIvol), dose length product (DLP), patient effective dose (ED) and size-specific dose estimate (SSDE), as output variables. Moreover, the study investigated the correlation between the different CT dose indices. Using linear regression models and Pearson correlation, the study found that all CT dose indices correlate with BMI and weight in all CT exams with varying degrees as opposed to age, which did not demonstrate any significant correlation with any of the CT dose indices across all CT exams. Moreover, it was found that using multiple regression models where multiple input variables are considered resulted in a higher correlation with the output variables than when simple regression was used. Investigating the relationships between the different dose indices, statistically significant relationships were found between all dose indices. A stronger linear relationship was noticed between CTDIvol and DLP compared to the relationships between each pair of the other dose indices. The findings of this study contribute to understanding the relationships between patient-related parameters and CT dose indices, aiding in the development of optimized CT exams that ensure patient safety while maintaining the diagnostic efficacy of CT imaging.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14031071
- https://www.mdpi.com/2076-3417/14/3/1071/pdf?version=1706278308
- OA Status
- gold
- Cited By
- 3
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391263551
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391263551Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app14031071Digital Object Identifier
- Title
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Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation AnalysisWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-26Full publication date if available
- Authors
-
Mohammad AlShurbaji, Sara El Haout, Akchunya Chanchal, Salam Dhou, Entesar Z. DalahList of authors in order
- Landing page
-
https://doi.org/10.3390/app14031071Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/14/3/1071/pdf?version=1706278308Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/14/3/1071/pdf?version=1706278308Direct OA link when available
- Concepts
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Computed tomography, Correlation, Regression analysis, Statistics, Medical physics, Medicine, Psychology, Mathematics, Radiology, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2562439620, https://openalex.org/W3046581697, https://openalex.org/W4315491084, https://openalex.org/W4383878482, https://openalex.org/W4289173231, https://openalex.org/W3133242499, https://openalex.org/W4377565707, https://openalex.org/W2126269515, https://openalex.org/W3005380481, https://openalex.org/W3128715140, https://openalex.org/W3195083582, https://openalex.org/W6769839464, https://openalex.org/W3182067631, https://openalex.org/W2197880141, https://openalex.org/W2502759836, https://openalex.org/W3182706339, https://openalex.org/W2504353110, https://openalex.org/W3155519782, https://openalex.org/W2990698977, https://openalex.org/W2135658961, https://openalex.org/W2765972405, https://openalex.org/W3105675965, https://openalex.org/W2605978655, https://openalex.org/W4207074198, https://openalex.org/W2899689654, https://openalex.org/W2903674506, https://openalex.org/W3041088044, https://openalex.org/W6842658609, https://openalex.org/W4317039739, https://openalex.org/W4313559544, https://openalex.org/W4327541347, https://openalex.org/W4224291364, https://openalex.org/W4286203331, https://openalex.org/W3100542382, https://openalex.org/W2765599824, https://openalex.org/W2982553281, https://openalex.org/W4293549161 |
| referenced_works_count | 37 |
| abstract_inverted_index.A | 241 |
| abstract_inverted_index.a | 4, 51, 58, 211 |
| abstract_inverted_index.CT | 22, 41, 46, 54, 66, 107, 113, 142, 157, 167, 187, 192, 277, 286, 298 |
| abstract_inverted_index.In | 48 |
| abstract_inverted_index.as | 103, 130, 172 |
| abstract_inverted_index.in | 10, 165, 210, 281 |
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| abstract_inverted_index.BMI | 162 |
| abstract_inverted_index.DLP | 250 |
| abstract_inverted_index.The | 28, 263 |
| abstract_inverted_index.age | 102 |
| abstract_inverted_index.all | 156, 166, 191, 238 |
| abstract_inverted_index.and | 74, 78, 83, 101, 106, 125, 149, 163, 249, 276 |
| abstract_inverted_index.any | 180, 184 |
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| abstract_inverted_index.did | 177 |
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| abstract_inverted_index.(ED) | 124 |
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| abstract_inverted_index.body | 96 |
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| abstract_inverted_index.index | 98, 115 |
| abstract_inverted_index.input | 104, 205 |
| abstract_inverted_index.other | 260 |
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| abstract_inverted_index.which | 176 |
| abstract_inverted_index.while | 292 |
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| abstract_inverted_index.aiding | 280 |
| abstract_inverted_index.chest, | 68 |
| abstract_inverted_index.cohort | 59 |
| abstract_inverted_index.ensure | 289 |
| abstract_inverted_index.exams, | 67 |
| abstract_inverted_index.exams. | 47, 193 |
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| abstract_inverted_index.models | 148, 202 |
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| abstract_inverted_index.simple | 220 |
| abstract_inverted_index.study, | 50 |
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| abstract_inverted_index.widely | 5 |
| abstract_inverted_index.(SSDE), | 129 |
| abstract_inverted_index.CTDIvol | 248 |
| abstract_inverted_index.Pearson | 84, 150 |
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| abstract_inverted_index.calcium | 72 |
| abstract_inverted_index.cardiac | 69, 71 |
| abstract_inverted_index.dataset | 52 |
| abstract_inverted_index.degrees | 171 |
| abstract_inverted_index.imaging | 8 |
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| abstract_inverted_index.opposed | 173 |
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| abstract_inverted_index.varying | 170 |
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| abstract_inverted_index.However, | 12 |
| abstract_inverted_index.analysis | 82 |
| abstract_inverted_index.compared | 251 |
| abstract_inverted_index.efficacy | 296 |
| abstract_inverted_index.estimate | 89, 128 |
| abstract_inverted_index.evaluate | 35 |
| abstract_inverted_index.exposure | 19 |
| abstract_inverted_index.factors, | 94 |
| abstract_inverted_index.findings | 264 |
| abstract_inverted_index.imaging. | 299 |
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| abstract_inverted_index.stronger | 242 |
| abstract_inverted_index.utilized | 6 |
| abstract_inverted_index.Moreover, | 133, 194 |
| abstract_inverted_index.analyzed. | 80 |
| abstract_inverted_index.collected | 77 |
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| abstract_inverted_index.different | 45, 141, 229 |
| abstract_inverted_index.effective | 122 |
| abstract_inverted_index.medicine. | 11 |
| abstract_inverted_index.objective | 29 |
| abstract_inverted_index.optimized | 285 |
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| abstract_inverted_index.radiation | 18 |
| abstract_inverted_index.variables | 206, 217 |
| abstract_inverted_index.(CTDIvol), | 116 |
| abstract_inverted_index.Regression | 81 |
| abstract_inverted_index.angiogram, | 70 |
| abstract_inverted_index.associated | 16 |
| abstract_inverted_index.considered | 208 |
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| abstract_inverted_index.contribute | 268 |
| abstract_inverted_index.diagnostic | 7, 295 |
| abstract_inverted_index.parameters | 38, 275 |
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| abstract_inverted_index.demonstrate | 179 |
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| abstract_inverted_index.necessitate | 20 |
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| abstract_inverted_index.unnecessary | 26 |
| abstract_inverted_index.correlation, | 151 |
| abstract_inverted_index.investigated | 136 |
| abstract_inverted_index.relationship | 244 |
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| abstract_inverted_index.investigating | 21 |
| abstract_inverted_index.relationships | 91, 226, 234, 254, 272 |
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| abstract_inverted_index.statistically | 232 |
| abstract_inverted_index.understanding | 270 |
| abstract_inverted_index.abdomen/pelvis, | 75 |
| abstract_inverted_index.patient-related | 37, 93, 274 |
| abstract_inverted_index.retrospectively | 79 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5002516404 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I199440890 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.81409856 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |