Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increas...

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Published inCancers Vol. 16; no. 16; p. 2880
Main Authors Spreafico, Marta, Hazewinkel, Audinga-Dea, van de Sande, Michiel A J, Gelderblom, Hans, Fiocco, Marta
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 19.08.2024
MDPI
Subjects
Bone cancer
Clinical trials
Comparative analysis
Decision making
Health care
Learning algorithms
Machine learning
Medical prognosis
Metastasis
Neural networks
Osteosarcoma
Patients
Prediction models
Prognosis
Software
Statistical analysis
Statistical models
Surgery
Survival
Variables
Netherlands
Cox model
survival predictions
survival artificial neural networks
machine learning
clinical trial data
osteosarcoma
random survival forests
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Summary:Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.
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These authors contributed equally to this work.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers16162880