A Novel Method for Dynamically Assessing the Prognosis of Patients with pT1 Gastric Cancer: A Large Population-Based Dynamic Prognostic Analysis

Abstract

Background. While early gastric cancer (EGC) patients are likely to experience relatively long postoperative survival, certain disease-related findings are associated with a poorer prognosis. This study sought to develop and validate a novel predictive model capable of estimating conditional disease-specific survival (CDSS) in EGC patients. Methods. A total of 3016 patients diagnosed with pT1NxM0 GC after gastrectomy between 1998 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database and were separated into training and validation cohorts. Kaplan‒Meier curves and log-rank tests were employed to evaluate DSS, after which univariate and multivariate Cox regression analyses were used to construct a predictive nomogram and to estimate CDSS at 1, 2, and 3 years postoperatively in these patients. Results. In the training cohort, the 3-year CDSS rose from 89.1% to 94.6% from 0 to 5 years postoperatively, while the 5-year CDSS rose from 84.5% to 92.0%. Cox regression analyses led to the construction of a nomogram that was able to reliably predict 3- and 5-year CDSS at 1, 2, and 3 years postoperatively (all P < 0.05 ) based upon patient age, tumor size, pT stage, pN stage, and the number of retrieved lymph nodes. This model exhibited good discriminative power in the training and validation cohorts (concordance index: 0.791 and 0.813, respectively), and nomogram calibration curves confirmed that actual and predicted survival outcomes were close to one another. Conclusions. We herein developed a nomogram capable of accurately predicting the CDSS of EGC patients that had survived for multiple years after undergoing surgery.

Development and validation of a nomogram for predicting lymph node metastasis in early gastric cancer

“…To date, numerous studies have investigated the characteristics and patterns of LNM in EGC patients globally; however, there is still no accurate prediction model. Many scholars now view nomograms as efficient instruments for predicting tumor progression and guiding clinical decision-making[ 5 - 10 ]. Prediction models are commonly employed for diagnosis and prognosis evaluation[ 11 , 12 ] and to determine tumor stage, predict recurrence and metastasis risk, estimate patient survival rates[ 11 ], and evaluate therapeutic efficacy[ 12 - 17 ].…”