Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period
We investigated the utility of geometric features for future AAA growth prediction. Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth. Computerized tomog...
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| Published in: | Annals of surgery Vol. 277; no. 1; p. e175 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.01.2023
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| ISSN: | 1528-1140, 1528-1140 |
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| Abstract | We investigated the utility of geometric features for future AAA growth prediction.
Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.
Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients.
There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases.
Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway. |
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| AbstractList | We investigated the utility of geometric features for future AAA growth prediction.
Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.
Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients.
There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases.
Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway. We investigated the utility of geometric features for future AAA growth prediction.OBJECTIVEWe investigated the utility of geometric features for future AAA growth prediction.Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.BACKGROUNDNovel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients.METHODSComputerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients.There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases.RESULTSThere was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases.Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.CONCLUSIONSGeometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway. |
| Author | Shivakumar, Natesh Lapolla, Pierfrancesco Ngetich, Elisha Chandrashekar, Anirudh Regent Lee Handa, Ashok Grau, Vicente |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33630463$$D View this record in MEDLINE/PubMed |
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| Snippet | We investigated the utility of geometric features for future AAA growth prediction.
Novel methods for growth prediction of AAA are recognized as a research... We investigated the utility of geometric features for future AAA growth prediction.OBJECTIVEWe investigated the utility of geometric features for future AAA... |
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| SubjectTerms | Aortic Aneurysm, Abdominal - epidemiology Aortic Rupture - epidemiology Humans Predictive Value of Tests ROC Curve Tomography, X-Ray Computed |
| Title | Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period |
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