Multi-factor decision-making strategy for better coronary plaque burden increase prediction: a patient-specific 3D FSI study using IVUS follow-up data.
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| Title: | Multi-factor decision-making strategy for better coronary plaque burden increase prediction: a patient-specific 3D FSI study using IVUS follow-up data. |
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| Authors: | Wang, Liang1,2 (AUTHOR), Tang, Dalin1,2 (AUTHOR) dtang@wpi.edu, Maehara, Akiko3 (AUTHOR), Molony, David4 (AUTHOR), Zheng, Jie5 (AUTHOR), Samady, Habib4 (AUTHOR), Wu, Zheyang2 (AUTHOR), Lu, Wenbin6 (AUTHOR), Zhu, Jian6 (AUTHOR), Ma, Genshan6 (AUTHOR) magenshan@hotmail.com, Giddens, Don P.4,7 (AUTHOR), Stone, Gregg W.3 (AUTHOR), Mintz, Gary S.3 (AUTHOR) |
| Source: | Biomechanics & Modeling in Mechanobiology. Oct2019, Vol. 18 Issue 5, p1269-1280. 12p. |
| Subjects: | Intravascular ultrasonography, Fluid-structure interaction, Coronary vasospasm, Shearing force, Shear walls |
| Abstract: | Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find a simple method to combine multiple risk factors for better plaque development prediction. Intravascular ultrasound data at baseline and follow-up were acquired from nine patients, and fluid–structure interaction models were constructed to obtain plaque wall stress/strain (PWS/PWSn) and wall shear stress (WSS). Two hundred fifty-four slices with noticeable change in plaque burden were selected for analyses. Data of six key morphological and biomechanical factors were extracted from each slice at baseline to predict plaque development measured by plaque burden increase (PBI) from baseline to follow-up. A multi-factor decision-making strategy was proposed to assign a binary predictive outcome YW (W represents any combination of these six factors) based on simple "threshold value" idea to predict the ground truth YPBI: YPBI = 1 if PBI > 0; YPBI = 0 otherwise. A fivefold cross-validation procedure was employed to identify the optimal predictor among all possible combinations. The results showed that PWS was the best single-factor predictor for PBI with a prediction accuracy of 63.0%. Among all 63 combinations, combining lipid percent, PWS and WSS gave the optimal predictor, achieving a prediction accuracy of 68.1%. This demonstrated that compared to single factor alone, integrating morphological and biomechanical factors would lead to higher prediction accuracy. The simple method could be extended to combine factors from different sources to improve prediction accuracy. Efforts in mechanical analysis and modeling automation are needed to bring this strategy closer to potential clinical applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Biomechanics & Modeling in Mechanobiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 138666942 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-factor decision-making strategy for better coronary plaque burden increase prediction: a patient-specific 3D FSI study using IVUS follow-up data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Liang%22">Wang, Liang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Dalin%22">Tang, Dalin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> dtang@wpi.edu</i><br /><searchLink fieldCode="AR" term="%22Maehara%2C+Akiko%22">Maehara, Akiko</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Molony%2C+David%22">Molony, David</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Jie%22">Zheng, Jie</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Samady%2C+Habib%22">Samady, Habib</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Zheyang%22">Wu, Zheyang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Wenbin%22">Lu, Wenbin</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Jian%22">Zhu, Jian</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Genshan%22">Ma, Genshan</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> magenshan@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Giddens%2C+Don+P%2E%22">Giddens, Don P.</searchLink><relatesTo>4,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stone%2C+Gregg+W%2E%22">Stone, Gregg W.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mintz%2C+Gary+S%2E%22">Mintz, Gary S.</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biomechanics+%26+Modeling+in+Mechanobiology%22">Biomechanics & Modeling in Mechanobiology</searchLink>. Oct2019, Vol. 18 Issue 5, p1269-1280. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Intravascular+ultrasonography%22">Intravascular ultrasonography</searchLink><br /><searchLink fieldCode="DE" term="%22Fluid-structure+interaction%22">Fluid-structure interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Coronary+vasospasm%22">Coronary vasospasm</searchLink><br /><searchLink fieldCode="DE" term="%22Shearing+force%22">Shearing force</searchLink><br /><searchLink fieldCode="DE" term="%22Shear+walls%22">Shear walls</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find a simple method to combine multiple risk factors for better plaque development prediction. Intravascular ultrasound data at baseline and follow-up were acquired from nine patients, and fluid–structure interaction models were constructed to obtain plaque wall stress/strain (PWS/PWSn) and wall shear stress (WSS). Two hundred fifty-four slices with noticeable change in plaque burden were selected for analyses. Data of six key morphological and biomechanical factors were extracted from each slice at baseline to predict plaque development measured by plaque burden increase (PBI) from baseline to follow-up. A multi-factor decision-making strategy was proposed to assign a binary predictive outcome YW (W represents any combination of these six factors) based on simple "threshold value" idea to predict the ground truth YPBI: YPBI = 1 if PBI > 0; YPBI = 0 otherwise. A fivefold cross-validation procedure was employed to identify the optimal predictor among all possible combinations. The results showed that PWS was the best single-factor predictor for PBI with a prediction accuracy of 63.0%. Among all 63 combinations, combining lipid percent, PWS and WSS gave the optimal predictor, achieving a prediction accuracy of 68.1%. This demonstrated that compared to single factor alone, integrating morphological and biomechanical factors would lead to higher prediction accuracy. The simple method could be extended to combine factors from different sources to improve prediction accuracy. Efforts in mechanical analysis and modeling automation are needed to bring this strategy closer to potential clinical applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Biomechanics & Modeling in Mechanobiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10237-019-01143-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1269 Subjects: – SubjectFull: Intravascular ultrasonography Type: general – SubjectFull: Fluid-structure interaction Type: general – SubjectFull: Coronary vasospasm Type: general – SubjectFull: Shearing force Type: general – SubjectFull: Shear walls Type: general Titles: – TitleFull: Multi-factor decision-making strategy for better coronary plaque burden increase prediction: a patient-specific 3D FSI study using IVUS follow-up data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Liang – PersonEntity: Name: NameFull: Tang, Dalin – PersonEntity: Name: NameFull: Maehara, Akiko – PersonEntity: Name: NameFull: Molony, David – PersonEntity: Name: NameFull: Zheng, Jie – PersonEntity: Name: NameFull: Samady, Habib – PersonEntity: Name: NameFull: Wu, Zheyang – PersonEntity: Name: NameFull: Lu, Wenbin – PersonEntity: Name: NameFull: Zhu, Jian – PersonEntity: Name: NameFull: Ma, Genshan – PersonEntity: Name: NameFull: Giddens, Don P. – PersonEntity: Name: NameFull: Stone, Gregg W. – PersonEntity: Name: NameFull: Mintz, Gary S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 16177959 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Biomechanics & Modeling in Mechanobiology Type: main |
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