SOURCE beyond first-line: A survival prediction model for patients with metastatic esophagogastric adenocarcinoma after failure of first-line palliative systemic therapy

Int J Cancer. 2023 Mar 15;152(6):1202-1209. doi: 10.1002/ijc.34385. Epub 2022 Dec 9.

Abstract

Prior models have been developed to predict survival for patients with esophagogastric cancer undergoing curative treatment or first-line chemotherapy (SOURCE models). Comprehensive clinical prediction models for patients with esophagogastric cancer who will receive second-line chemotherapy or best supportive care are currently lacking. The aim of our study was to develop and internally validate a new clinical prediction model, called SOURCE beyond first-line, for survival of patients with metastatic esophagogastric adenocarcinoma after failure of first-line palliative systemic therapy. Patients with unresectable or metastatic esophageal or gastric adenocarcinoma (2015-2017) who received first-line systemic therapy (N = 1067) were selected from the Netherlands Cancer Registry. Patient, tumor and treatment characteristics at primary diagnosis and at progression of disease were used to develop the model. A Cox proportional hazards regression model was developed through forward and backward selection using Akaike's Information Criterion. The model was internally validated through 10-fold cross-validations to assess performance. Model discrimination (C-index) and calibration (slope and intercept) were used to evaluate performance of the complete and cross-validated models. The final model consisted of 11 patient tumor and treatment characteristics. The C-index was 0.75 (0.73-0.78), calibration slope 1.01 (1.00-1.01) and calibration intercept 0.01 (0.01-0.02). Internal cross-validation of the model showed that the model performed adequately on unseen data: C-index was 0.79 (0.77-0.82), calibration slope 0.93 (0.85-1.01) and calibration intercept 0.02 (-0.01 to 0.06). The SOURCE beyond first-line model predicted survival with fair discriminatory ability and good calibration.

Keywords: best supportive care; esophageal cancer; gastric cancer; prediction model; second-line.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenocarcinoma* / pathology
  • Esophageal Neoplasms* / pathology
  • Humans
  • Models, Statistical
  • Prognosis
  • Stomach Neoplasms* / pathology