Peter M Graffy1, Jiamin Liu2, Stacy O'Connor3, Ronald M Summers2, Perry J Pickhardt4. 1. E3/311 Clinical Science Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA. 2. Radiology & Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA. 3. Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA. 4. E3/311 Clinical Science Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA. ppickhardt2@uwhealth.org.
Abstract
OBJECTIVE: To investigate an automated aortic calcium segmentation and scoring tool at abdominal CT in an adult screening cohort. METHODS: Using instance segmentation with convolutional neural networks (Mask R-CNN), a fully automated vascular calcification algorithm was applied to a data set of 9914 non-contrast CT scans from 9032 consecutive asymptomatic adults (mean age, 57.5 ± 7.8 years; 4467 M/5447F) undergoing colonography screening. Follow-up scans were performed in a subset of 866 individuals (mean interval, 5.4 years). Automated abdominal aortic calcium volume, mass, and Agatston score were assessed. In addition, comparison was made with a separate validated semi-automated approach in a subset of 812 cases. RESULTS: Mean values were significantly higher in males for Agatston score (924.2 ± 2066.2 vs. 564.2 ± 1484.2, p < 0.001), aortic calcium mass (222.2 ± 526.0 mg vs. 144.5 ± 405.4 mg, p < 0.001) and volume (699.4 ± 1552.4 ml vs. 426.9 ± 1115.5 HU, p < 0.001). Overall age-specific Agatston scores increased an average of 10%/year for the entire cohort; males had a larger Agatston score increase between the ages of 40 to 60 than females (91.2% vs. 75.1%, p < 0.001) and had significantly higher mean Agatston scores between ages 50 and 80 (p < 0.001). For the 812-scan subset with both automated and semi-automated methods, median difference in Agatston score was 66.4 with an r2 agreement value of 0.84. Among the 866-patient cohort with longitudinal follow-up, the average Agatston score change was 524.1 ± 1317.5 (median 130.9), reflecting a mean increase of 25.5% (median 73.6%). CONCLUSION: This robust, fully automated abdominal aortic calcification scoring tool allows for both individualized and population-based assessment. Such data could be automatically derived at non-contrast abdominal CT, regardless of the study indication, allowing for opportunistic assessment of cardiovascular risk.
OBJECTIVE: To investigate an automated aortic calcium segmentation and scoring tool at abdominal CT in an adult screening cohort. METHODS: Using instance segmentation with convolutional neural networks (Mask R-CNN), a fully automated vascular calcification algorithm was applied to a data set of 9914 non-contrast CT scans from 9032 consecutive asymptomatic adults (mean age, 57.5 ± 7.8 years; 4467 M/5447F) undergoing colonography screening. Follow-up scans were performed in a subset of 866 individuals (mean interval, 5.4 years). Automated abdominal aortic calcium volume, mass, and Agatston score were assessed. In addition, comparison was made with a separate validated semi-automated approach in a subset of 812 cases. RESULTS: Mean values were significantly higher in males for Agatston score (924.2 ± 2066.2 vs. 564.2 ± 1484.2, p < 0.001), aortic calcium mass (222.2 ± 526.0 mg vs. 144.5 ± 405.4 mg, p < 0.001) and volume (699.4 ± 1552.4 ml vs. 426.9 ± 1115.5 HU, p < 0.001). Overall age-specific Agatston scores increased an average of 10%/year for the entire cohort; males had a larger Agatston score increase between the ages of 40 to 60 than females (91.2% vs. 75.1%, p < 0.001) and had significantly higher mean Agatston scores between ages 50 and 80 (p < 0.001). For the 812-scan subset with both automated and semi-automated methods, median difference in Agatston score was 66.4 with an r2 agreement value of 0.84. Among the 866-patient cohort with longitudinal follow-up, the average Agatston score change was 524.1 ± 1317.5 (median 130.9), reflecting a mean increase of 25.5% (median 73.6%). CONCLUSION: This robust, fully automated abdominal aortic calcification scoring tool allows for both individualized and population-based assessment. Such data could be automatically derived at non-contrast abdominal CT, regardless of the study indication, allowing for opportunistic assessment of cardiovascular risk.
Authors: Perry J Pickhardt; Alberto A Perez; John W Garrett; Peter M Graffy; Ryan Zea; Ronald M Summers Journal: AJR Am J Roentgenol Date: 2021-08-18 Impact factor: 6.582
Authors: Daniel T Huff; Peter Ferjancic; Mauro Namías; Hamid Emamekhoo; Scott B Perlman; Robert Jeraj Journal: Biomed Phys Eng Express Date: 2021-09-30
Authors: Perry J Pickhardt; Peter M Graffy; Ryan Zea; Scott J Lee; Jiamin Liu; Veit Sandfort; Ronald M Summers Journal: Lancet Digit Health Date: 2020-03-02
Authors: Ronald M Summers; Daniel C Elton; Sungwon Lee; Yingying Zhu; Jiamin Liu; Mohammedhadi Bagheri; Veit Sandfort; Peter C Grayson; Nehal N Mehta; Peter A Pinto; W Marston Linehan; Alberto A Perez; Peter M Graffy; Stacy D O'Connor; Perry J Pickhardt Journal: Acad Radiol Date: 2020-09-18 Impact factor: 3.173
Authors: Perry J Pickhardt; Peter M Graffy; Benjamin Weigman; Nimrod Deiss-Yehiely; Cesare Hassan; Jennifer M Weiss Journal: Radiology Date: 2020-08-11 Impact factor: 11.105
Authors: Perry J Pickhardt; Peter M Graffy; Ryan Zea; Scott J Lee; Jiamin Liu; Veit Sandfort; Ronald M Summers Journal: Radiology Date: 2020-08-11 Impact factor: 11.105
Authors: Peter M Graffy; Jiamin Liu; Perry J Pickhardt; Joseph E Burns; Jianhua Yao; Ronald M Summers Journal: Br J Radiol Date: 2019-06-24 Impact factor: 3.039
Authors: Perry J Pickhardt; Peter M Graffy; Alberto A Perez; Meghan G Lubner; Daniel C Elton; Ronald M Summers Journal: Radiographics Date: 2021 Mar-Apr Impact factor: 5.333