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Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma
Xiao-Xiao Shao1, Xi-Chen Li2, Zi-Jian Lin2
1Department of Gastroenterology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China, shaoxiaoxiaowmu@163.com.
Introduction:
The objective of our study was to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRCC).
Methods:
A total of 3,408 GSRCC patients between 1975 and 2017 were screened from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts. Univariate and multivariate Cox analyses were conducted to identify independent prognostic factors for the construction of a nomogram. The performance of the model was then assessed by the concordance index (C-index), calibration plot, and area under the receiver operating characteristic curve (AUC). Then, the novel nomogram was further assessed by 64 GSRCC patients from our hospital as the external cohort.
Results:
We identified age, tumor lymph node metastasis (TNM) staging system, surgery, and chemotherapy as significant independent elements of prognosis. On this basis, a nomogram was constructed, with a C-index of OS in the training and validation cohorts of 0.763 (95% CI: 0.751-0.774) and 0.766 (95% CI: 0.748-0.784) and a C-index of CSS of 0.765 (95% CI: 0.753-0.777) and 0.773 (95% CI: 0.755-0.791), respectively. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.848 and 0.885, respectively, and those for predicting CSS were 0.854 and 0.899, respectively, demonstrating the excellent predictive value of the constructed nomogram compared to the traditional AJCC staging system. Similar results were also observed in both the internal and external validation sets.
Conclusion:
The nomogram provided an accurate tool to predict OS and CSS in patients with GSRCC, which can assist clinicians in making predictions about individual patient survival.
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