Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging

Abstract

Background: Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour.

Aim: To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data.

Methods: Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7^th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.

Results: For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI.

Conclusion: The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Keywords: Epidemiology; Gastric cancer; Lung metastasis; Nomograms; Overall survival; Prognosis; Surveillance; Surveillance epidemiology and end results program database.

Figure 1

Gastric cancer with lung metastasis patient screening process flowchart. GCLM: Gastric cancer with lung metastasis; ROC: Receiver operating characteristic.

Figure 2

Cumulative Incidence Prediction plot of cancer special survival in gastric cancer with lung metastasis. A: Age; B: N stage; C: Race; D: T stage; E: Radiotherapy; F: Chemotherapy; G: Liver Metastasis; H: Bone Metastasis; I: Brain metastasis; J: Marital status; K: Node positive number; L: Surgery; M: Primary site; N: Treatment sequence; O: Histological type. CSS: Cancer special survival; GCLM: Gastric cancer with lung metastasis; CIF: Cumulative incidence function.

Figure 3

All included variables' Pearson correlation analysis. AJCC: American Joint Committee on Cancer; Surg: Surgery; T: Tumor; N: Node; LN: Lymph node; Reg: Regional.

Figure 4

The overall survival Nomgram for gastric cancer with lung metastasis. AJCC: American Joint Committee on Cancer; Surg: Surgery; T: Tumor; N: Node; LN: Lymph node; Reg: Regional; Surg: Surgery; Oth: Other; Dis: Disease.

Figure 5

Gastric cancer with lung metastasis calibration curves. A: 12-month likelihoods of overall survival (OS) in the training dataset; B: 36-month likelihoods of OS in the training dataset; C: 12-month likelihoods of OS in the validation dataset; D: 36-month likelihoods of OS in the validation dataset.

Figure 6

Time-dependent area under the curve and receiver operating characteristic curves of overall survival. A: Receiver operating characteristic (ROC) curves corresponding to 1-year in the training cohort; B: ROC curves corresponding to 3-year overall survival in the training cohort; C: ROC curves corresponding to 1-year in the validation cohort; D: ROC curves corresponding to 3-year cancer-specific survival in the validation cohort. AUC: Area under the curve.

Figure 7

Decision curve analysis of the nomogram in the estimation of overall survival. A: The Decision curve analysis (DCA) curve for the 1-year overall survival of the training dataset; B: The DCA curve for the 1-year overall survival of the validation dataset; C: The DCA curve for the 3-year overall survival of the training dataset; D: The DCA curve for the 3-year overall survival of the validation dataset.

Figure 8

Survival curves for different features of overall survival. A: Primary site; B: Surgery; C: Surgery other regional distant; D: Treatment Sequence; E: Radiation; F: Chemotherapy; G: Node stage; H: Metastasis at bone; I: Metastasis at liver. HR: Hazard ratio; Surg: Surgery; Oth: Other; Reg: Regional; Dis: Disease.

Figure 9

Comparison of Kaplan–Meier curves of gastric cancer with lung metastasis patients between new Cox model and Cox-American Joint Committee on Cancer. A: Kaplan–Meier overall survival curves of gastric cancer with lung metastasis (GCLM) patients with different risks stratified; B: Kaplan–Meier Cox-American Joint Committee on Cancer curves of GCLM patients with different risks stratified.

Figure 10

Comparison between the new and old models of net reclassification improvement and integrated discrimination improvement. A: Receiver operating characteristic (ROC) curve of the participants in the 1-year integrated discrimination improvement (IDI) of the new model; B: ROC curve of the participants in the 3-year IDI of the new model; C: Area under the receiver operating characteristic curve difference between new and old model in 1-year and 3-year; D: New model net reclassification improvement and IDI for 1 Year and 3 Years. ROC: Receiver operating characteristic; AUC: Area under the receiver operating characteristic curve; NRI: Net reclassification improvement; IDI: Integrated discrimination improvement.