Nomogram for predicting survival of patients with gastric cancer and multiple primary malignancies: a real-world retrospective analysis using the Surveillance, Epidemiology and End Results database

Abstract

Objectives: Gastric cancer combined with multiple primary malignancies (GCM) is increasingly common. This study investigated GCM clinical features and survival time.

Methods: Patients with GCM and GC only (GCO) were selected from the Surveillance, Epidemiology and End Results (SEER) database. Survival was compared between GCM and GCO groups using propensity score matching. Then, the GCM group was divided into a training cohort and a validation cohort. These cohorts were used to establish a nomogram for survival prediction in patients with GCM.

Results: Survival time was significantly longer in the GCM group than in the GCO group. All-subsets regression was used to identify four variables for nomogram establishment: age, gastric cancer sequence, N stage, and surgery. The concordance index and time-dependent receiver operating characteristic curve indicated that the nomogram had favorable discriminative ability. Calibration plots of predicted and actual probabilities showed good consistency in both the training and validation cohorts. Decision curve analysis and risk stratification showed that the nomogram was clinically useful; it had favorable discriminative ability to recognize patients with different levels of risk.

Conclusions: Compared with GCO, GCM is a relatively indolent malignancy. The nomogram developed in this study can help clinicians to assess GCM prognosis.

Keywords: Nomogram; SEER database; gastric cancer; multiple primary malignancies; prognosis; propensity score; receiver operating characteristic curve; stomach neoplasm; survival.

Figure 1.

Flowchart of patient inclusion and separation into cohorts. Database name: Incidence–SEER Research Plus Data, 17 Registries, Nov 2021 Sub (2000–2019). GC, gastric cancer; GCM, gastric cancer combined with multiple primary malignancies; GCO, gastric cancer only.

Figure 2.

Comparison of survival between GCM and GCO groups. (a) Comparison of survival between GCM and GCO before PSM. (b) Comparison of survival between GCM and GCO after PSM. GCM, gastric cancer combined with multiple primary malignancies; GCO, gastric cancer only; PSM, propensity score matching.

Figure 3.

Nomogram for prognostic prediction in a patient with GCM. The patient was aged <60 years, and gastric cancer was not the patient’s first tumor. The patient’s N stage was N1, and the patient did not undergo surgery. The density plots of total points and age show their distributions. The distributions of categorical variables are indicated by box size. To use the nomogram, an individual patient’s specific points (black dots) are placed on each variable axis. Red lines and dots are drawn upward to determine the points contributed by each variable; the sum (238) of these points is placed on the total points axis, and a line is drawn downward to the survival axes to determine the probabilities of 12‐month (47.7%), 36-month (17.0%) and 60-month (9.8%) survival. GC, gastric cancer; GCM, gastric cancer combined with multiple primary malignancies; Pr, probability.

Figure 4.

Evaluation of nomogram discriminative and calibration abilities. (a, b) Time-dependent AUC values in the training and validation cohorts. (c, d) Twelve-, 36-, and 60-month ROC curves and AUC values in the training and validation cohorts and (e, f) Calibration curves of 12-, 36-, and 60-month overall survival for patients with GCM in the training and validation cohorts. Dots in calibration curves were calculated by bootstrap sampling (1000 samples); they represent nomogram performance. A predicted value close to the ideal line (diagonal) indicates greater accuracy in survival prediction AUC, area under the time‐dependent receiver operating characteristic curves; GCM, gastric cancer combined with multiple primary malignancies; ROC, receiver operating characteristic.

Figure 5.

Clinical utility of the nomogram. (a, b) DCA of the nomogram for training and validation cohorts. Twelve-, 36-, and 60-month DCA lines represent the net clinical benefit over a range of threshold probabilities: horizontal red lines assume no patients will experience the event; all-12 months, all-36 months, and all 60 months lines assume all patients will experience the event and (c, d) Kaplan–Meier survival curves for training and validation cohorts at different risk levels, stratified using the nomogram. DCA, decision curve analysis.