A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma

Abstract

Background: 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). Patients and Methods: A total of 3408 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 (ROC) 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.

Prognosis and Treatment of Gastric Cancer: A 2024 Update

“…The prognosis is determined by several independent factors, including age, surgery, chemotherapy, and the tumor-node-metastasis (TNM) staging system. In light of this, the nomogram is a model that can be used to predict both cancer-specific survival (CSS) and OS in patients with gastric signet cell carcinoma [22] and is facilitated by machine learning (ML) models available for diagnosis. Using a noninvasive method such as a radiomic-clinicopathological model of this kind may accurately predict perineural invasion (PNI) in GC patients before surgery.…”

A novel web-based dynamic prognostic nomogram for gastric signet ring cell carcinoma: a multicenter population-based study

“…Nomograms, which are commonly used for assessing cancer patient prognosis and personally predicting survival rates, are more suitable for clinical patient management than the TNM staging system. However, despite the establishment of several postoperative nomograms that have significantly contributed to the management of patients with GSRCC, some issues have arisen (5)(6)(7)(8). First, these models focused solely on the overall postoperative survival and did not predict cancer-specific survival.…”