SynthesisEuropean journal of nuclear medicine and molecular imaging2026
Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.
Synthesis in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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13 authors.
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Abstract
purposeThis study aims to evaluate the performance of artificial intelligence (AI)-assisted PET imaging in predicting neoadjuvant chemotherapy (NAC) response in breast cancer patients.
methodsThe Ovid MEDLINE, Ovid Embase, Cochrane, Web of Science, and IEEE Xplore databases were systematically searched for studies utilizing AI algorithms in PET imaging for predicting responses to NAC in breast cancer, covering publications up to June 26, 2025. Binary diagnostic accuracy data were extracted for meta-analysis, with the area under the curve (AUC) serving as the primary outcome. Subgroup analyses and meta-regression analyses were conducted to explore potential sources of heterogeneity.
resultsEighteen studies were eligible for systematic review, and eleven studies that selected 907 patients were included in the meta-analysis, with a pooled AUC of 0.80 (95% confidence interval [CI]: 0.77-0.84). However, significant heterogeneity was observed among the studies, with a I² of 79.65% (95% CI: 74.69-84.60) for sensitivity and 86.62% (95% CI: 83.73-89.51) for specificity. Meta-regression analyses revealed that patient sample size and the integration of clinical data in the models were significant contributors to heterogeneity.
conclusionsThe use of AI in predicting treatment response to NAC in breast cancer based on PET imaging demonstrated promising accuracy and potential for clinical use. But its clinical implementation is challenged by methodological variability, small datasets, lack of external validation and limited interpretability. Future research should prioritize the improvement of dataset quality and the integration of explainable AI (XAI) to facilitate the broader adoption of AI in clinical practice.
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