A machine learning model predicting candidates for surgical treatment modality in patients with distant metastatic esophageal adenocarcinoma: A propensity score-matched analysis.

OBJECTIVE

To explore the role of surgical treatment modality on prognosis of metastatic esophageal adenocarcinoma (mEAC), as well as to construct a machine learning model to predict suitable candidates.

METHOD

All mEAC patients pathologically diagnosed between January 2010 and December 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A 1:4 propensity score-matched analysis and a multivariate Cox analysis were performed to verify the prognostic value of surgical treatment modality. To identify suitable candidates, a machine learning model, classification and regression tree (CART), was constructed, and its predictive performance was evaluated by the area under receiver operating characteristic curve (AUC).

RESULTS

Of 4520 mEAC patients, 2901 (64.2%) were aged over 60 years and 4012 (88.8%) were males. There were 411 (9.1%) patients receiving surgical treatment modality. In the propensity score-matched analysis, surgical treatment modality was significantly associated with a decreased risk of death (HR: 0.47, 95% CI: 0.40-0.55); surgical patients had almost twice as much median survival time (MST) as those without resection (MST with 95% CI: 23 [17-27] months 11 [11-12] months, 0.0001). The similar association was also observed in the multivariate Cox analysis (HR: 0.47, 95% CI: 0.41-0.53). Then, a CART was constructed to identify suitable candidates for surgical treatment modality, with a relatively good discrimination ability (AUC with 95% CI: 0.710 [0.648-0.771]).

CONCLUSION

Surgical treatment modality may be a promising strategy to prolong survival of mEAC patients. The CART in our study could serve as a useful tool to predict suitable candidates for surgical treatment modality. Further creditable studies are warranted to confirm our findings.