ArticleEuropean journal of nuclear medicine and molecular imaging2026
Automatic metabolic breast cancer staging using [¹⁸F]FDG PET/CT: comparison with nuclear medicine physician-based and clinical staging.
Article 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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Abstract
purposeThis study aimed to evaluate a deep-learning (DL)-based framework to automatically perform breast cancer (BC) metabolic staging on [¹⁸F]FDG PET/CT, and to assess agreement among DL-based, nuclear medicine (NM) physician-based, and clinical staging.
methodsA total of 403 histologically confirmed BC patients who underwent whole-body staging [¹⁸F]FDG PET/CT were retrospectively included. All [
resultsDL-based staging showed concordance with NM-based/clinical staging of 75/62%, 87/74%, and 83/82% for T, N, and M, respectively. For N3 (presence of extra-ALN) and M1 (presence of dM), where [¹⁸F]FDG PET/CT is particularly relevant, sensitivity/specificity of DL-based (NM-based reference) were 0.86/0.96, and 0.97/0.78, respectively. Segmentation performance was good to excellent for pT, ALN, and dM (median DC ≥ 0.83 and LD ≥ 0.78), and moderate for extra-ALN (median DC = 0.63 and LD ≥ 0.71). NM-based metabolic staging agreed with clinical staging in 63%, 80%, and 99% of cases for T, N, and M, respectively.
conclusionAlthough expert supervision remains essential, the developed DL-based framework demonstrates potential as a supportive tool for metabolic staging in BC patients, facilitating a workflow-efficient [¹⁸F]FDG PET/CT-based staging assessment.
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