Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [<sup>18</sup>F]FDG PET/CT: A Comparison between Two PET/CT Scanners

Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [<sup>18</sup>F]FDG PET/CT: A Comparison between Two PET/CT Scanners

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 [<sup>18</sup>F]...

Full description

Bibliographic Details
Main Authors: Francesco Dondi, Roberto Gatta, Domenico Albano, Pietro Bellini, Luca Camoni, Giorgio Treglia, Francesco Bertagna
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Journal of Clinical Medicine
Subjects:
PET/CT
radiomics
texture analysis
machine learning
lung cancer
FDG
Online Access:https://www.mdpi.com/2077-0383/12/1/255

Internet

https://www.mdpi.com/2077-0383/12/1/255

Similar Items