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Background <p>Artificial intelligence (AI) tools for neuro-oncology are rapidly entering clinical workflows for image segmentation, treatment planning, and outcome prediction, yet their real-world performance and clinical correlates remain uncertain. We conducted a meta-analysis to synthesize diagnostic/predictive accuracy, segmentation quality, dosimetric surrogates, clinician override, and patient outcomes across studies.</p> Methods <p>We searched major databases, for studies reporting quantitative performance for AI models applied to brain tumors radiotherapy. Outcomes were pooled using random-effects models with inverse-variance weighting. We conducted prespecified subgroup analyses. Heterogeneity was performed.</p> Results <p>Across all AI tasks, AUC was 0.856, higher for planning than outcome prediction. DSC was 0.840. Accuracy was 0.842, with planning 0.852, outcome prediction 0.824, metastases 0.863, and glioma 0.875. Sensitivity was 0.854 (planning 0.886; outcome 0.817; metastases 0.848; glioma 0.914). Specificity was 0.845 (planning 0.953; outcome 0.793; metastases 0.856). HD was 8.51&#xa0;mm, 4.46&#xa0;mm for metastases and 10.07&#xa0;mm for glioma. Dosimetric conformity was 0.900 and 0.917 in metastases; target coverage was 0.976 (0.969 in metastases). Physician override rate was 0.258 (0.332 in metastases). OS was 19.13 months, 15.5 in metastases, and 17.53 in glioblastoma.</p> Conclusions <p>AI for brain tumors radiotherapy demonstrates strong pooled discrimination and robust segmentation with encouraging dosimetric surrogates and high target coverage. AI-assisted segmentation and planning could streamline radiotherapy workflows, reducing manual contouring burden while maintaining high spatial accuracy and dosimetric quality. Nevertheless, high heterogeneity, variable reporting/metrics, and physician override rates show the need for standardized evaluation, prospective clinical validation, and consistent reporting to translate technical gains into practice.</p>

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Performance of artificial intelligence for brain tumor segmentation, treatment planning, and outcome prediction: a comprehensive systematic review and meta-analysis

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Barbara Buccilli,
  • Brandon Lucke-Wold

摘要

Background

Artificial intelligence (AI) tools for neuro-oncology are rapidly entering clinical workflows for image segmentation, treatment planning, and outcome prediction, yet their real-world performance and clinical correlates remain uncertain. We conducted a meta-analysis to synthesize diagnostic/predictive accuracy, segmentation quality, dosimetric surrogates, clinician override, and patient outcomes across studies.

Methods

We searched major databases, for studies reporting quantitative performance for AI models applied to brain tumors radiotherapy. Outcomes were pooled using random-effects models with inverse-variance weighting. We conducted prespecified subgroup analyses. Heterogeneity was performed.

Results

Across all AI tasks, AUC was 0.856, higher for planning than outcome prediction. DSC was 0.840. Accuracy was 0.842, with planning 0.852, outcome prediction 0.824, metastases 0.863, and glioma 0.875. Sensitivity was 0.854 (planning 0.886; outcome 0.817; metastases 0.848; glioma 0.914). Specificity was 0.845 (planning 0.953; outcome 0.793; metastases 0.856). HD was 8.51 mm, 4.46 mm for metastases and 10.07 mm for glioma. Dosimetric conformity was 0.900 and 0.917 in metastases; target coverage was 0.976 (0.969 in metastases). Physician override rate was 0.258 (0.332 in metastases). OS was 19.13 months, 15.5 in metastases, and 17.53 in glioblastoma.

Conclusions

AI for brain tumors radiotherapy demonstrates strong pooled discrimination and robust segmentation with encouraging dosimetric surrogates and high target coverage. AI-assisted segmentation and planning could streamline radiotherapy workflows, reducing manual contouring burden while maintaining high spatial accuracy and dosimetric quality. Nevertheless, high heterogeneity, variable reporting/metrics, and physician override rates show the need for standardized evaluation, prospective clinical validation, and consistent reporting to translate technical gains into practice.