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Prognostic Models for Patient Selection in Palliative Bone Radiotherapy: A Systematic Review.

Filipa A Martins¹, Nádia Brito², Domingos Sousa^3,4

¹Radiotherapy, Centro Hospital Universitário São João, Porto, PRT.

|April 20, 2026

Summary

Accurate prognostic models are crucial for selecting patients who will benefit from palliative radiotherapy for bone metastases. Current models show heterogeneity and risk of bias, necessitating external validation for clinical use.

Keywords:

bone metastases palliative radiotherapy prognostic models survival prediction systematic review

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Area of Science:

Oncology
Radiotherapy
Biostatistics

Background:

Palliative radiotherapy effectively manages symptomatic bone metastases.
Accurate prognosis is vital for selecting patients likely to benefit, especially those with a limited prognosis (under 3 months).
Existing prognostic tools require evaluation for their utility in this specific patient group.

Purpose of the Study:

To systematically review prognostic models used for patient selection in palliative radiotherapy for bone metastases.
To assess the performance and risk of bias of identified prognostic models.

Main Methods:

A systematic review adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Searches were conducted across PubMed, Scopus, and Web of Science databases.
Risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Main Results:

Nineteen studies were included, characterized by heterogeneity in study design (mostly retrospective) and patient populations.
A wide array of prognostic approaches were identified, including established scores, novel tools, and individual factors.
Performance status emerged as the most significant prognostic variable; however, most models exhibited high or unclear risk of bias.

Conclusions:

Prognostic models show potential for optimizing patient selection for palliative radiotherapy for bone metastases.
The heterogeneity of models and patient populations underscores the need for robust external validation.
The clinical utility of these models depends on local context, clinician expertise, and individual patient characteristics.