Author, as appears in the article.: Tahmooresi, Maryam; Abdel-Nasser, Mohamed; Puig, Domenec
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Tahmooresi, Maryam
Keywords: Cad Lung cance Lung cancer Radiomics Reliability
Abstract: 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.
Thematic Areas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: domenec.puig@urv.cat maryam.tahmooresi@estudiants.urv.cat mohamed.abdelnasser@urv.cat
Author identifier: 0000-0002-0562-4205 0000-0002-1074-2441
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://ebooks.iospress.nl/doi/10.3233/FAIA220350
Papper original source: Frontiers In Artificial Intelligence And Applications. 356 289-297
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
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.3233/FAIA220350
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Proceedings Paper