A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients

A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients

Abstract Predictive modelling of cancer outcomes using radiomics faces dimensionality problems and data limitations, as radiomics features often number in the hundreds, and multi-institutional data sharing is ()often unfeasible. Federated learning (FL) and feature selection (FS) techniques combined...

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Bibliographic Details
Main Authors: Benedetta Gottardelli, Varsha Gouthamchand, Carlotta Masciocchi, Luca Boldrini, Antonella Martino, Ciro Mazzarella, Mariangela Massaccesi, René Monshouwer, Jeroen Findhammer, Leonard Wee, Andre Dekker, Maria Antonietta Gambacorta, Andrea Damiani
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Subjects:
Distributed learning
Feature selection
Radiomics
NSCLC
Online Access:https://doi.org/10.1038/s41598-024-58241-1

Internet

https://doi.org/10.1038/s41598-024-58241-1

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