The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy
Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Minliang [verfasserIn] Dong, Hai [verfasserIn] Mazlout, Adam [verfasserIn] Wu, Yuxuan [verfasserIn] Kalyanasundaram, Asanish [verfasserIn] Oshinski, John N. [verfasserIn] Sun, Wei [verfasserIn] Elefteriades, John A. [verfasserIn] Leshnower, Bradley G. [verfasserIn] Gleason, Rudolph L. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computers in biology and medicine - Amsterdam [u.a.] : Elsevier Science, 1970, 170 |
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| Übergeordnetes Werk: |
volume:170 |
| DOI / URN: |
10.1016/j.compbiomed.2024.108041 |
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| Katalog-ID: |
ELV067136192 |
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| 245 | 1 | 0 | |a The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy |
| 264 | 1 | |c 2024 | |
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| 520 | |a Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. | ||
| 650 | 4 | |a Type B aortic dissection | |
| 650 | 4 | |a Optimal medical therapy | |
| 650 | 4 | |a Statistical shape model | |
| 650 | 4 | |a Aortic growth | |
| 650 | 4 | |a Dissection flap | |
| 700 | 1 | |a Dong, Hai |e verfasserin |4 aut | |
| 700 | 1 | |a Mazlout, Adam |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yuxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Kalyanasundaram, Asanish |e verfasserin |0 (orcid)0000-0003-4175-5998 |4 aut | |
| 700 | 1 | |a Oshinski, John N. |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Elefteriades, John A. |e verfasserin |4 aut | |
| 700 | 1 | |a Leshnower, Bradley G. |e verfasserin |4 aut | |
| 700 | 1 | |a Gleason, Rudolph L. |e verfasserin |4 aut | |
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10.1016/j.compbiomed.2024.108041 doi (DE-627)ELV067136192 (ELSEVIER)S0010-4825(24)00125-2 DE-627 ger DE-627 rda eng 610 570 VZ 42.00 bkl 44.09 bkl Liu, Minliang verfasserin (orcid)0000-0001-6240-5116 aut The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. Type B aortic dissection Optimal medical therapy Statistical shape model Aortic growth Dissection flap Dong, Hai verfasserin aut Mazlout, Adam verfasserin aut Wu, Yuxuan verfasserin aut Kalyanasundaram, Asanish verfasserin (orcid)0000-0003-4175-5998 aut Oshinski, John N. verfasserin aut Sun, Wei verfasserin aut Elefteriades, John A. verfasserin aut Leshnower, Bradley G. verfasserin aut Gleason, Rudolph L. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 170 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:170 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 VZ 44.09 Medizintechnik VZ AR 170 |
| spelling |
10.1016/j.compbiomed.2024.108041 doi (DE-627)ELV067136192 (ELSEVIER)S0010-4825(24)00125-2 DE-627 ger DE-627 rda eng 610 570 VZ 42.00 bkl 44.09 bkl Liu, Minliang verfasserin (orcid)0000-0001-6240-5116 aut The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. Type B aortic dissection Optimal medical therapy Statistical shape model Aortic growth Dissection flap Dong, Hai verfasserin aut Mazlout, Adam verfasserin aut Wu, Yuxuan verfasserin aut Kalyanasundaram, Asanish verfasserin (orcid)0000-0003-4175-5998 aut Oshinski, John N. verfasserin aut Sun, Wei verfasserin aut Elefteriades, John A. verfasserin aut Leshnower, Bradley G. verfasserin aut Gleason, Rudolph L. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 170 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:170 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 VZ 44.09 Medizintechnik VZ AR 170 |
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10.1016/j.compbiomed.2024.108041 doi (DE-627)ELV067136192 (ELSEVIER)S0010-4825(24)00125-2 DE-627 ger DE-627 rda eng 610 570 VZ 42.00 bkl 44.09 bkl Liu, Minliang verfasserin (orcid)0000-0001-6240-5116 aut The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. Type B aortic dissection Optimal medical therapy Statistical shape model Aortic growth Dissection flap Dong, Hai verfasserin aut Mazlout, Adam verfasserin aut Wu, Yuxuan verfasserin aut Kalyanasundaram, Asanish verfasserin (orcid)0000-0003-4175-5998 aut Oshinski, John N. verfasserin aut Sun, Wei verfasserin aut Elefteriades, John A. verfasserin aut Leshnower, Bradley G. verfasserin aut Gleason, Rudolph L. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 170 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:170 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 VZ 44.09 Medizintechnik VZ AR 170 |
| allfieldsGer |
10.1016/j.compbiomed.2024.108041 doi (DE-627)ELV067136192 (ELSEVIER)S0010-4825(24)00125-2 DE-627 ger DE-627 rda eng 610 570 VZ 42.00 bkl 44.09 bkl Liu, Minliang verfasserin (orcid)0000-0001-6240-5116 aut The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. Type B aortic dissection Optimal medical therapy Statistical shape model Aortic growth Dissection flap Dong, Hai verfasserin aut Mazlout, Adam verfasserin aut Wu, Yuxuan verfasserin aut Kalyanasundaram, Asanish verfasserin (orcid)0000-0003-4175-5998 aut Oshinski, John N. verfasserin aut Sun, Wei verfasserin aut Elefteriades, John A. verfasserin aut Leshnower, Bradley G. verfasserin aut Gleason, Rudolph L. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 170 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:170 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 VZ 44.09 Medizintechnik VZ AR 170 |
| allfieldsSound |
10.1016/j.compbiomed.2024.108041 doi (DE-627)ELV067136192 (ELSEVIER)S0010-4825(24)00125-2 DE-627 ger DE-627 rda eng 610 570 VZ 42.00 bkl 44.09 bkl Liu, Minliang verfasserin (orcid)0000-0001-6240-5116 aut The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. Type B aortic dissection Optimal medical therapy Statistical shape model Aortic growth Dissection flap Dong, Hai verfasserin aut Mazlout, Adam verfasserin aut Wu, Yuxuan verfasserin aut Kalyanasundaram, Asanish verfasserin (orcid)0000-0003-4175-5998 aut Oshinski, John N. verfasserin aut Sun, Wei verfasserin aut Elefteriades, John A. verfasserin aut Leshnower, Bradley G. verfasserin aut Gleason, Rudolph L. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 170 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:170 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 VZ 44.09 Medizintechnik VZ AR 170 |
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Liu, Minliang @@aut@@ Dong, Hai @@aut@@ Mazlout, Adam @@aut@@ Wu, Yuxuan @@aut@@ Kalyanasundaram, Asanish @@aut@@ Oshinski, John N. @@aut@@ Sun, Wei @@aut@@ Elefteriades, John A. @@aut@@ Leshnower, Bradley G. @@aut@@ Gleason, Rudolph L. @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV067136192</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240224093206.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240222s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compbiomed.2024.108041</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV067136192</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0010-4825(24)00125-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.09</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Minliang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6240-5116</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. 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Liu, Minliang |
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Liu, Minliang ddc 610 bkl 42.00 bkl 44.09 misc Type B aortic dissection misc Optimal medical therapy misc Statistical shape model misc Aortic growth misc Dissection flap The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy |
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610 570 VZ 42.00 bkl 44.09 bkl The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy Type B aortic dissection Optimal medical therapy Statistical shape model Aortic growth Dissection flap |
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ddc 610 bkl 42.00 bkl 44.09 misc Type B aortic dissection misc Optimal medical therapy misc Statistical shape model misc Aortic growth misc Dissection flap |
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ddc 610 bkl 42.00 bkl 44.09 misc Type B aortic dissection misc Optimal medical therapy misc Statistical shape model misc Aortic growth misc Dissection flap |
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The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy |
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The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy |
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Liu, Minliang |
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Liu, Minliang Dong, Hai Mazlout, Adam Wu, Yuxuan Kalyanasundaram, Asanish Oshinski, John N. Sun, Wei Elefteriades, John A. Leshnower, Bradley G. Gleason, Rudolph L. |
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10.1016/j.compbiomed.2024.108041 |
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the role of anatomic shape features in the prognosis of uncomplicated type b aortic dissection initially treated with optimal medical therapy |
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The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy |
| abstract |
Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. |
| abstractGer |
Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. |
| abstract_unstemmed |
Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate. |
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The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy |
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Dong, Hai Mazlout, Adam Wu, Yuxuan Kalyanasundaram, Asanish Oshinski, John N. Sun, Wei Elefteriades, John A. Leshnower, Bradley G. Gleason, Rudolph L. |
| author2Str |
Dong, Hai Mazlout, Adam Wu, Yuxuan Kalyanasundaram, Asanish Oshinski, John N. Sun, Wei Elefteriades, John A. Leshnower, Bradley G. Gleason, Rudolph L. |
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| doi_str |
10.1016/j.compbiomed.2024.108041 |
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2024-07-06T20:13:25.457Z |
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Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods: 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.Results: Size-related SSM and conventional features (mean aortic diameter: p = 0.0484 , centerline length: p = 0.0112 , PCA score c 1 : p = 0.0192 , and PLS scores t 1 : p = 0.0004 , t 2 : p = 0.0274 ) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.Conclusion: Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. 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7.401531 |