Dataset Information

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

ABSTRACT:

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 [¹⁸F]F-FDG PET/CT images.

Methods

A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [¹⁸F]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized

SUBMITTER: Gomez OV

PROVIDER: S-EPMC9221062 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Dataset Information

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

Background

Methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

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

Background

Methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

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