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Elastic Staining on Paraffin-embedded Slides of pT3N0M0 Gastric Cancer Tissue
Published on: May 1, 2019
Incomplete TNM Documentation in Gastric Cancer: Frequency, Phenotype, and Treatment Allocation
Alexandru-Marian Vieru1, Maria-Lorena Mustață1, Virginia-Maria Rădulescu2
1Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
Abstract:
Background/Objectives: Real-world gastric cancer cohorts often show incomplete TNM documentation, which can affect the interpretation of stage, phenotype, and treatment allocation. We aimed to quantify staging completeness, describe advanced-disease phenotype, and examine treatment selection at diagnosis in a real-world gastric cancer cohort. Methods: We performed a retrospective observational study of consecutive patients diagnosed with gastric cancer at a tertiary referral center. Data included age, sex, TNM components, metastatic status, surgery (any vs. none), and available serum markers (CEA, CA19-9). Incomplete staging was defined a priori as Tx and/or Nx and/or Mx. The primary endpoint was metastatic disease at diagnosis (M1) among patients with defined M status. In TNM-complete cases, a composite locally advanced or metastatic endpoint (LAM: M1 or T4 or N2-N3) supported sensitivity analyses. Logistic regression assessed associations with M1 and treatment allocation without biomarker cut-offs (markers modeled as continuous covariates). Results: The cohort included 419 patients. Incomplete staging was observed in 36.8%. M status was defined in 89.5%, with M1 in 52.0% of M-defined cases. Surgery was less frequent in M1 than M0 patients (34.4% vs. 73.3%; p < 0.001). Phenotype stratification showed a marked difference in surgical allocation, which was highest in M0-LAM (89.1%) and lowest in M1 (48.4%). Marker associations were directionally coherent but not definitive. Conclusions: Incomplete staging is common and clinically relevant in real-world gastric cancer and should be reported explicitly. Phenotype-based summaries provide a pragmatic framework for interpreting advanced disease and treatment selection, while tumor markers should be interpreted cautiously without predefined cut-offs.
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