Construction and validation of the predictive model for gallbladder cancer liver metastasis patients: a SEER-based study

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Abstract
Background: The purpose of this present research was to construct a nomograph model to predict prognosis in gallbladder cancer liver metastasis (GCLM) patients so as to provide a basis for clinical decision-making. Methods: We surveyed patients diagnosed with GCLM in the Surveillance Epidemiology and the End Results database between 2010 and 2019. They were randomized 7 : 3 into a training set and a validation set. In the training set, meaningful prognostic factors were determined using univariate and multivariate Cox regression analyses, and an individualized nomogram prediction model was generated. The prediction model was evaluated by C-index, calibration curve, ROC curve and DCA curve from the training set and the validation set. Results: A total of 727 confirmed cases were enrolled in the research, 510 in the training set and 217 in the validation set. Factors including bone metastasis, surgery, chemotherapy and radiotherapy were independent prognostic factors for cancer-specific survival (CSS) rates and were employed in the construction of the nomogram model. The C-index for the training set and validation set were 0.688 and 0.708, respectively. The calibration curve exhibited good consistency between predicted and actual CSS rates. ROC curve and DCA of the nomogram showed superior performance at 6 months CSS, 1-year CSS and 2 years CSS in both the training set and validation set. Conclusion: We have successfully constructed a nomogram model that can predict CSS rates in patients with GCLM. This prediction model can help patients in counseling and guide clinicians in treatment decisions.

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