Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [<sup>18</sup>F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [<sup>18</sup>F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [<su...

Full description

Bibliographic Details
Main Authors: Ober Van Gómez, Joaquin L. Herraiz, José Manuel Udías, Alexander Haug, Laszlo Papp, Dania Cioni, Emanuele Neri
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Cancers
Subjects:
FDG-PET/CT
metastatic breast cancer
feature selection
machine learning
radiomics
metabolic response
Online Access:https://www.mdpi.com/2072-6694/14/12/2922

Internet

https://www.mdpi.com/2072-6694/14/12/2922

Similar Items