A population-based predictive model predicting candidate for primary tumor surgery in patients with metastatic esophageal cancer

Abstract

Background: The survival benefit of primary tumor surgery for metastatic esophageal cancer (mEC) patients has been observed, but methods for discriminating which individual patients would benefit from surgery have been poorly defined. Herein, a predictive model was developed to test the hypothesis that only certain metastatic patients would gain a survival benefit from primary tumor surgery.

Methods: Clinical data for patients with mEC were extracted from the Surveillance, Epidemiology and End Results (SEER) database [2004-2016] and then divided into surgery and no-surgery groups according to whether surgery was performed on the primary tumor. Propensity-score-matching (PSM) was performed to balance the confounding factors. We hypothesized that the patients who had undergone surgery and lived longer than the median cancer-specific-survival (CSS) of the no-surgery group could benefit from surgery. We constructed a nomogram to predict surgery benefit potential based on multivariable logistic-regression analysis using preoperative factors. The predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (AUC) and calibration curves. The clinical application value of the nomogram was estimated with decision curve analysis (DCA).

Results: A total of 5,250 eligible patients with mEC were identified, and 9.4% [492] received primary tumor surgery. After PSM, CSS for the surgery group was significantly longer [median: 19 vs. 9 months; hazard ratio (HR) 0.52, P<0.001] compared with the no-surgery group. Among the surgery group, 69.3% [327] survived >9 months (surgery-beneficial group). The prediction nomogram showed good discrimination both in training and validation sets (AUC: 0.72 and 0.70, respectively), and the calibration curves indicated a good consistency. DCA demonstrated that the nomogram was clinically useful. According to this nomogram, surgery patients were classified into two groups: no-benefit-candidate and benefit-candidate. The benefit-candidate group was associated with longer survival than the no-benefit-candidate group (median CSS: 19 vs. 6.5 months, P<0.001). Additionally, there was no difference in survival between the no-benefit-candidate and no-surgery groups (median CSS: 6.5 vs. 9 months, P=0.070).

Conclusions: A predictive model was created for the selection of candidates for surgical treatment among mEC patients. This predictive model might be used to select patients who may benefit from primary tumor surgery.

Keywords: Esophageal cancer (EC); metastatic; predictive model; surgery.

Figure 1

The flowchart of study population selection and predictive model construction.

Figure 2

Comparison of cancer-specific survival between surgery to primary tumor vs. no surgery to primary tumor. (A) Plots of Kaplan-Meier estimates of cancer-specific survival of metastatic esophageal cancer patients with and without primary tumor surgery in the matched cohort. (B) Hazard ratios of cancer-specific survival for those who underwent primary tumor surgery, compared with those who did not undergo primary tumor surgery, by subgroups. [(B) Diamonds represent effect size (hazard ratio (HR)], calculated separately by primary tumor surgery vs. no primary tumor surgery in different subgroups; horizontal lines (error bars) indicate 95% confidence intervals (CIs).

Figure 3

Prediction nomogram to predict candidate for benefit from primary tumor surgery in patients with metastatic esophageal cancer. The probability of each variable can be converted into scores according to the first scale “Points” at the top of the nomogram. After adding up the corresponding prediction probability at the bottom of the nomogram, the likelihood of surgery benefit of the individual patient can be calculated. The cut-off point of the nomogram is 0.5. The patient would be classified as benefit-candidate when the total prediction probability is beyond the cut-off point.

Figure 4

Validity of the predictive performance of the nomogram with ROC and calibration curves. ROC curves of the nomogram in the training (A) and validation (B) sets. Calibration curves of the nomogram in the training (C) and validation (D) sets. ROC, receiver operating characteristic.

Figure 5

A schematic depicting the application of the nomogram. In this analysis, a prediction nomogram was developed to identify candidate for benefit from primary tumor surgery in metastatic stage esophageal cancer, and provide more treatment options to these patients.

Figure 6

Kaplan-Meier curves of survival for metastatic esophageal cancer patients in different benefit classification according to the nomogram (benefit-candidate and no-benefit candidate groups) and no-surgery group.