Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics

Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics

<b>Introduction:</b> Accurate prediction of tumor dynamics following Gamma Knife radiosurgery (GKRS) is critical for optimizing treatment strategies for patients with brain metastases (BMs). Traditional machine learning (ML) algorithms have been widely used for this purpose; however, rec...

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Main Authors: Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Diagnostics
Subjects:
gamma knife radiosurgery (GKRS)
brain metastasis
tumor dynamics forecasting
machine learning models
autoencoders
Online Access:https://www.mdpi.com/2075-4418/14/18/2091
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