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

  • Datos identificativos

    Identificador: imarina:9385561
    Autores:
    Tahmooresi, MaryamAbdel-Nasser, MohamedPuig, Domenec
    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.
  • Otros:

    Autor según el artículo: Tahmooresi, Maryam; Abdel-Nasser, Mohamed; Puig, Domenec
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Tahmooresi, Maryam
    Palabras clave: Cad Lung cance Lung cancer Radiomics Reliability
    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.
    Áreas temáticas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    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
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://ebooks.iospress.nl/doi/10.3233/FAIA220350
    Referencia al articulo segun fuente origial: Frontiers In Artificial Intelligence And Applications. 356 289-297
    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
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3233/FAIA220350
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    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
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