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Neurosurgical Review
Published in: Neurosurgical Review 1/2024

01-12-2024 | Metastasis | Review

Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis

Authors: Mohammad Amin Habibi, Farhang Rashidi, Adriana Habibzadeh, Ehsan Mehrtabar, Mohammad Reza Arshadi, Mohammad Sina Mirjani

Published in: Neurosurgical Review | Issue 1/2024

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Abstract

Background

Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, monitoring for toxicity, and integrating with other modalities. Practical tools to predict patient outcomes are also needed. Machine learning (ML) is currently used to predict treatment outcomes. We aim to investigate the accuracy of ML in predicting treatment response and local failure of brain metastasis treated with SRS.

Methods

PubMed, Scopus, Web of Science (WoS), and Embase were searched until April 16th, which was repeated on October 17th, 2023 to find possible relevant papers. The study preparation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The statistical analysis was performed by the MIDAS package of STATA v.17.

Results

A total of 17 articles were reviewed, of which seven and eleven were related to the clinical use of ML in predicting local failure and treatment response. The ML algorithms showed sensitivity and specificity of 0.89 (95% CI: 0.84–0.93) and 0.87 (95% CI: 0.81–0.92) for predicting treatment response. The positive likelihood ratio was 7.1 (95% CI: 4.5–11.1), the negative likelihood ratio was 0.13 (95% CI: 0.08–0.19), and the diagnostic odds ratio was 56 (95% CI: 25–125). Moreover, the pooled estimates for sensitivity and specificity of ML algorithms for predicting local failure were 0.93 (95% CI: 0.76–0.98) and 0.80 (95% CI: 0.53–0.94). The positive likelihood ratio was 4.7 (95% CI: 1.6–14.0), the negative likelihood ratio was 0.09 (95% CI: 0.02–0.39), and the diagnostic odds ratio was 53 (95% CI: 5-606).

Conclusion

ML holds promise in predicting treatment response and local failure in brain metastasis patients receiving SRS. However, further studies and improvements in the treatment process can refine the models and effectively integrate them into clinical practice.
Appendix
Available only for authorised users
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Metadata
Title
Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis
Authors
Mohammad Amin Habibi
Farhang Rashidi
Adriana Habibzadeh
Ehsan Mehrtabar
Mohammad Reza Arshadi
Mohammad Sina Mirjani
Publication date
01-12-2024
Publisher
Springer Berlin Heidelberg
Published in
Neurosurgical Review / Issue 1/2024
Print ISSN: 0344-5607
Electronic ISSN: 1437-2320
DOI
https://doi.org/10.1007/s10143-024-02391-3

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