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Tumor-Stroma Percentage Associated with Clinicopathology and Tumor Markers Predict Prognosis in Gastric Cancer with the Different Primary Site: A Retrospective, Observational Cohort Study
53 Pages Posted: 1 Nov 2022
More...Abstract
Background: Tumor location plays a vital role in the prognosis of gastric cancer. Personalized prognostic prediction models are critical for patients with proximal gastric cancer (PGC) and distal gastric cancer (DGC). This study aimed to identify prognosis related factors as to develop and validate predictive customized models for PGC and DGC patients.
Methods: A prediction model was built including 813 GC patients from Shanghai General Hospital in Shanghai, China, hospitalized between Dec 1, 2012 and Dec 31, 2019. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and compared with TNM stage. Models were externally validated in cohorts from Bengbu Hospital in Anhui, China.
Findings: Multivariate Cox regression demonstrated that TSP-visual (P<0.05, HR=2.599,95% CI1.998-2.294), pTNM (P<0.05, HR=2.054, 95% CI 1.238-2.294), and vascular invasion (P<0.05, HR=0.417, 95% CI 0.213 -0.435) were independent predictors for PGC, as pTNM (P<0.05, HR=2.395, 95% CI 1.373-4.177), nerve invasion (P<0.05, HR=2.174, 95% CI 1.227-3.851) and pathologic types (P<0.05, HR=1.806, 95% CI 1.036-3.148) to DGC. The area under the curve (AUC) and concordance index (C-index) of the nomogram constructed from the above three factors were TSP, TNM, LMR and TNM, CEA, nerve invasion in PGC and DGC, respectively. The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram.
Interpretation: New nomograms based on clinicopathological factors were developed and externally validated to identify those at risk of poor prognosis in patients with PGC or DGC, respectively，which will provide clinicians with an accurate and effective tool for the early prediction and facilitate optimized and individualized treatment strategies.
Funding: This study was supported by the National Natural Science Foundation of China(No.82072662, No.81772526), Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (2016142), Medical Engineering Intersection Project of Shanghai Jiao Tong University (YG2017MS28), Shanghai Three-year Action Plan to Promote Clinical Skills and Clinical Innovation in Municipal Hospitals (SHDC2020CR4022), and the 2021 Shanghai “Rising Stars of Medical Talent” Youth Development Program: Outstanding Youth Medical Talents.
Declaration of Interest: The authors declare no conflict of interest.
Ethical Approval: This study was approved by the Ethics Committee of Shanghai General Hospital
(Approval number: 2021KSQ352).
Keywords: Keywords: Gastric cancer, tumor-stroma percentage, tumor location, algorithm
Suggested Citation: Suggested Citation