Analyzing the Reliability of Different Machine Radiomics Features Considering Various Segmentation Approaches in Lung Cancer CT Images

Tahmooresi, Maryam; Abdel-Nasser, Mohamed; Puig, Domenec

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Analyzing the Reliability of Different Machine Radiomics Features Considering Various Segmentation Approaches in Lung Cancer CT Images - imarina:9385561

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https://hdl.handle.net/20.500.11797/imarina9385561

Autor/es de la URV:	Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Tahmooresi, Maryam
Autor según el artículo:	Tahmooresi, Maryam; Abdel-Nasser, Mohamed; Puig, Domenec
Direcció de correo del autor:	domenec.puig@urv.cat maryam.tahmooresi@estudiants.urv.cat mohamed.abdelnasser@urv.cat
Identificador del autor:	0000-0002-0562-4205 0000-0002-1074-2441
Año de publicación de la revista:	2022
Tipo de publicación:	Proceedings Paper
Referencia de l'ítem segons les normes APA:	Tahmooresi, Maryam; Abdel-Nasser, Mohamed; Puig, Domenec (2022). Analyzing the Reliability of Different Machine Radiomics Features Considering Various Segmentation Approaches in Lung Cancer CT Images. Amsterdam: IOS Press
Referencia al articulo segun fuente origial:	Frontiers In Artificial Intelligence And Applications. 356 289-297
Resumen:	Cancer is generally defined as the uncontrollable increase of number of cells in the body. These cells might be formed anywhere in the body and spread to other parts of the body. Although the mortality rate of cancer is high, it is possible to decrease cancer cases by up to 30% to 50% through taking a healthy lifestyle and avoiding unhealthy habits. Imaging is one of the powerful technologies used for detecting and treating cancer at its early stages. Nowadays, scientists admit that medical images hold more information than their diagnosis, which is called a radiomics approach. Radiomics demonstrate that images comprise numerous quantitative features that are useful in predicting, detecting, and treating cancers in a personalized manner. While radiomics can extract numerous features, not all of them are useful. It should not be neglected that the outcome of data analysis is highly dependent on the selected features. There are different ways of finding the most reliable features. One possible way is to select all extracted features, analyze them, and find the most reproducible and reliable ones. Different statistical analysis metrics could analyze the features. To discover and introduce the most accurate metrics, in this paper, different statistical metrics used for measuring the stability and reproducibility of the features are investigated.
DOI del artículo:	10.3233/FAIA220350
Enlace a la fuente original:	https://ebooks.iospress.nl/doi/10.3233/FAIA220350
Versión del articulo depositado:	info:eu-repo/semantics/publishedVersion
Acceso a la licencia de uso:	https://creativecommons.org/licenses/by/3.0/es/
Departamento:	Enginyeria Informàtica i Matemàtiques
URL Documento de licencia:	https://repositori.urv.cat/ca/proteccio-de-dades/
Áreas temáticas:	Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
Palabras clave:	Cad Lung cance Lung cancer Radiomics Reliability
Entidad:	Universitat Rovira i Virgili
Fecha de alta del registro:	2024-10-12

Descripción:	Cancer is generally defined as the uncontrollable increase of number of cells in the body. These cells might be formed anywhere in the body and spread to other parts of the body. Although the mortality rate of cancer is high, it is possible to decrease cancer cases by up to 30% to 50% through taking a healthy lifestyle and avoiding unhealthy habits. Imaging is one of the powerful technologies used for detecting and treating cancer at its early stages. Nowadays, scientists admit that medical images hold more information than their diagnosis, which is called a radiomics approach. Radiomics demonstrate that images comprise numerous quantitative features that are useful in predicting, detecting, and treating cancers in a personalized manner. While radiomics can extract numerous features, not all of them are useful. It should not be neglected that the outcome of data analysis is highly dependent on the selected features. There are different ways of finding the most reliable features. One possible way is to select all extracted features, analyze them, and find the most reproducible and reliable ones. Different statistical analysis metrics could analyze the features. To discover and introduce the most accurate metrics, in this paper, different statistical metrics used for measuring the stability and reproducibility of the features are investigated.
Tipo:	Proceedings Paper info:eu-repo/semantics/publishedVersion
Coautor:	Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili
Títol:	Analyzing the Reliability of Different Machine Radiomics Features Considering Various Segmentation Approaches in Lung Cancer CT Images
Materia:	Artificial Intelligence Cad Lung cance Lung cancer Radiomics Reliability Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
Fecha:	2022
Autor:	Tahmooresi, Maryam Abdel-Nasser, Mohamed Puig, Domenec
Derechos:	info:eu-repo/semantics/openAccess

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