ReviewJournal of personalized medicine2026
From Anatomical Staging to Precision Prognostication: Evolution of Gallbladder Cancer Staging Across AJCC Editions and the 2025 UICC TNM Update.
Review in Journal of personalized medicine, 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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8 authors.
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Abstract
Gallbladder cancer (GBC) is the most common biliary tract malignancy and is among the most lethal gastrointestinal cancers. Since its inclusion in cancer staging manuals, the GBC Tumour-Node-Metastasis (TNM) classification has undergone repeated revisions intended to improve anatomical precision and prognostic discrimination. This narrative review traces the evolution of GBC staging across AJCC editions and the 2025 UICC TNM update and critically examines whether these refinements have translated into clinically meaningful risk stratification. In the 2025 UICC TNM 9 grouping, Stage IA corresponds to T1aN0M0 and Stage IB to T1bN0M0, while T2aN0M0 and T2bN0M0 remain Stage IIA and IIB, respectively. Structured literature searches were performed to identify historical staging documents, population-based analyses, validation studies, and prognostic reports relevant to stage migration, survival discrimination, and emerging staging modifiers. Successive changes, including T-category refinement and the transition from nodal location to nodal burden, have improved anatomical resolution. However, available validation studies suggest that gains in overall prognostic performance remain modest, with reported concordance indices often remaining near 0.66. Contemporary evidence increasingly shows that tumour location, adequacy of lymphadenectomy, number of positive lymph nodes, lymph node ratio, log odds of positive lymph nodes, obstructive jaundice, residual disease status, and selected molecular alterations may capture inter-patient heterogeneity beyond anatomy alone. Future staging refinement should therefore preserve TNM as a common anatomical language while developing validated parallel modifiers that support more individualized prognostication and treatment selection.
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