Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [

FDG PET/CT lung cancer machine learning radiomics texture analysis

Journal

Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588

Informations de publication

Date de publication:
29 Dec 2022
Historique:
received: 04 11 2022
revised: 07 12 2022
accepted: 28 12 2022
entrez: 8 1 2023
pubmed: 9 1 2023
medline: 9 1 2023
Statut: epublish

Résumé

The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role of radiomics features (RaF) and machine learning (ML) in the prediction of the histological classification of stage I and II non-small-cell lung cancer (NSCLC) at baseline [

Identifiants

pubmed: 36615053
pii: jcm12010255
doi: 10.3390/jcm12010255
pmc: PMC9820870
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Francesco Dondi (F)

Nuclear Medicine, ASST Spedali Civili Brescia, 25123 Brescia, Italy.

Roberto Gatta (R)

Dipartimento di Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, 25123 Brescia, Italy.

Domenico Albano (D)

Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy.

Pietro Bellini (P)

Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy.

Luca Camoni (L)

Nuclear Medicine, ASST Spedali Civili Brescia, 25123 Brescia, Italy.

Giorgio Treglia (G)

Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland.
Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, University of Lausanne, 1011 Lausanne, Switzerland.
Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland.

Francesco Bertagna (F)

Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili Brescia, 25123 Brescia, Italy.

Classifications MeSH