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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

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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.

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Data availability

The data that support the findings of this study are available from the corresponding author, M.A Habibi, upon reasonable request.

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Authors and Affiliations

  1. Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Mohammad Amin Habibi

  2. School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Farhang Rashidi

  3. Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran

    Adriana Habibzadeh

  4. Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran

    Ehsan Mehrtabar & Mohammad Reza Arshadi

  5. Student Research Committee, Faculty of Medicine, Qom University of Medical Science, Qom, Iran

    Mohammad Sina Mirjani

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Contributions

MA. H, F.R, and MS.M contributed to the study conception and design, and edited the manuscript. MA.H and A.H analyzed the data and wrote the first draft of the manuscript. E.M and MR.A collected data. MA.H and F.R made a critical revision of the manuscript. All authors commented on previous versions of the manuscript and revised it. All authors read and approved the final manuscript.

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Correspondence to Mohammad Amin Habibi.

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Habibi, M.A., Rashidi, F., Habibzadeh, A. et al. 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. Neurosurg Rev 47, 199 (2024). https://doi.org/10.1007/s10143-024-02391-3

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