PARProceedings of Anticancer Research2208-35452208-3553Bio-Byword Scientific Publishing Pty. Ltd.10.26689/par.v8i6.8929ArticleEstablishment and Validation of Diagnostic and Prognostic Prediction Models for Liver Metastasis in Patients with Rectal Cancer: A SEER-based Studyhttps://artdesignp.com/journal/PAR/8/6/10.26689/par.v8i6.8929WangHuimin,ZhengYa,ChenZhaofeng2024862024-11-26Background: Rectal cancer is one of the most common gastrointestinal tumors, among which the liver is the most common site of distant metastasis and liver metastasis that leads to poor prognosis. This study aimed to develop and validate a diagnostic nomogram to predict the occurrence of rectal cancer with liver metastasis (RCLM) and a prognostic nomogram to predict cancer-specific survival (CSS) in RCML patients. Methods: Data on patients with rectal cancer diagnosed between 2010 and 2013 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate logistic regression and multivariate logistic regression were used to determine the independent risk factors of RCLM. Univariate Cox proportional hazards regression and multivariate Cox proportional hazards regression were used to identify independent prognostic factors for RCLM. This study then developed two novel nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: A total of 29367 patients with rectal cancer were included. Among them, 3403 patients (11.59%) had liver metastases at the time of diagnosis. The independent risk factors of RCLM included sex, race, tumor grade, AJCC N, CEA, marital status, tumor size, total number of primary tumors, and histological type. Age, chemotherapy, total number of primary tumors, surgery sites, and histological type were independent prognostic factors of patients with RCLM. The results of ROC curves, calibration curves, and DCA in the development, validation, and testing sets confirmed that the diagnostic nomogram can precisely predict the occurrence of RCLM. The results of ROC curves, calibration curves, DCA, C-indexes, and Kaplan–Meier (K-M) survival curves in the development, validation, and testing sets confirmed that the prognostic nomogram could precisely predict the prognosis of RCLM. Conclusion: The two nomograms are expected to be effective tools for predicting the risk of liver metastasis for patients with rectal cancer and personalized prognosis prediction for patients with RCLM, which may benefit clinical decision-making.

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