SynthesisAnnals of nuclear medicine2026
Diagnostic accuracy of artificial intelligence-assisted 18f-fdg pet/ct for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis.
Synthesis in Annals of nuclear medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Points for clear interpretation of AI-assisted 18 F-FDG PET/CT in breast cancer.Annals of nuclear medicine · 2026Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We conducted a systematic review and meta-analysis to assess the diagnostic accuracy of artificial intelligence (AI)-assisted 18 F-FDG PET/CT for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. A comprehensive search of PubMed, Embase, and Web of Science was conducted for studies, with a cutoff date of August 29, 2025, and updated on October 16, 2025. The QUADAS-2 technique and Grading of Recommendations Assessment, Development and Evaluation framework were employed to evaluate study quality. Diagnosis accuracy was aggregated utilizing a bivariate random-effects model. A total of 49 studies involving 3038 patients were included. The Spearman rank correlation coefficient for AI was determined to be 0.159 (P = 0.662). The pooled sensitivity, specificity, PLR, NLR, DOR of AI-assisted 18 F-FDG PET/CT for predicting pCR to NAC in breast cancer were 0.82 (95% CI 0.76-0.87), 0.83 (95% CI 0.75-0.89), 5.03 (95% CI 3.79-6.69), 0.39 (95% CI 0.31-0.49), and 17.71 (95% CI 10.37-30.25), respectively. Furthermore, the AUC was determined to be 0.83 (95% CI: 0.80-0.86). The Fagan nomogram indicated a positive likelihood ratio of 52% and a negative likelihood ratio of 6%. This meta-analysis demonstrates that AI-assisted 18 F-FDG PET/CT shows good diagnostic accuracy for predicting pCR to NAC in breast cancer, achieving better sensitivity and specificity than MRI and ultrasound, and comparable accuracy to conventional PET/CT with improved specificity. These findings highlight its potential as a reliable tool to aid clinical decision-making, though moderate heterogeneity underscores the need for standardized methods and multicenter prospective validation.
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