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SOURCE beyond first‐line: A survival prediction model for patients with metastatic esophagogastric adenocarcinoma after failure of first‐line palliative systemic therapy
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10107625/ https://www.ncbi.nlm.nih.gov/pubmed/36451334 http://dx.doi.org/10.1002/ijc.34385 |
Sumario: | 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. |
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