F. García-García, E. Metaxa, S. Christodoulidis, M. Anthimopoulos, N. Kontopodis, Martina Correa-Londono, T. Wyss, Y. Papaharilaou, C. Ioannou, H. Tengg-Kobligk, S. Mougiakakou
{"title":"腹主动脉瘤的预后:一种融合临床、形态、生物力学和纹理信息的机器学习方法","authors":"F. García-García, E. Metaxa, S. Christodoulidis, M. Anthimopoulos, N. Kontopodis, Martina Correa-Londono, T. Wyss, Y. Papaharilaou, C. Ioannou, H. Tengg-Kobligk, S. Mougiakakou","doi":"10.1109/CBMS.2017.158","DOIUrl":null,"url":null,"abstract":"An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as slow, medium or quick growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees (LPBoost) using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information\",\"authors\":\"F. García-García, E. Metaxa, S. Christodoulidis, M. Anthimopoulos, N. Kontopodis, Martina Correa-Londono, T. Wyss, Y. Papaharilaou, C. Ioannou, H. Tengg-Kobligk, S. Mougiakakou\",\"doi\":\"10.1109/CBMS.2017.158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as slow, medium or quick growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees (LPBoost) using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment.\",\"PeriodicalId\":141105,\"journal\":{\"name\":\"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2017.158\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2017.158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information
An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as slow, medium or quick growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees (LPBoost) using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment.