Risk factors, prognostic predictors, and nomograms for pancreatic cancer patients with initially diagnosed synchronous liver metastasis

Figure 1 Nomogram for predicting liver metastasis from pancreatic cancer patients. ^aP < 0.001. LN: Lymph node; LM: Liver metastasis.

Figure 2 Validation of the diagnostic nomogram in the training and validation sets. A: The receiver operating characteristic curve of the training set; B: The calibration curve of the training set; C: The decision curve analysis of the training set; D: The receiver operating characteristic curve of the validation set; E: The calibration curve of the validation set; F: The decision curve analysis of the validation set. AUC: Area under curve.

Figure 3 The least absolute shrinkage and selection operator regression used to select prognostic factors for overall survival. A: Least absolute shrinkage and selection operator (LASSO) coefficient profiles of 16 variables for overall survival (OS); B: LASSO Cox analysis identified 7 variables for OS. The LASSO regression analysis run in R runs 10 times K cross-validation for centralization and normalization of included variables and then selects the most appropriate lambda value depending on the type measure of -2 Log-likelihood and binomial family. “Lambda.lse” gives a model with good performance but the least number of independent variables.

Figure 4 A prognostic nomogram for pancreatic cancer patients with liver metastasis. ^aP < 0.01; ^bP < 0.001. Surg prim: Surgical treatments of the primary site; Surg dis: Surgical treatments of the distant site.

Figure 5 The calibration curves and decision curve analysis of the prognostic nomogram in the training set. A: The calibration curve of the nomogram for 6 mo in the training set; B: The calibration curve of the nomogram for 12 mo in the training set; C: The calibration curve of the nomogram for 18 mo in the training set; D: The decision curve analysis of the nomogram for 6 mo in the training set; E: The decision curve analysis of the nomogram for 12 mo in the training set; F: The decision curve analysis of the nomogram for 18 mo in the training set.

Figure 6 The calibration curves and decision curve analysis of the prognostic nomogram in the validation set. A: The calibration curve of the nomogram for 6 mo in the validation set; B: The calibration curve of the nomogram for 12 mo in the validation set; C: The calibration curve of the nomogram for 18 mo in the validation set; D: The decision curve analysis of the nomogram for 6 mo in the validation set; E: The decision curve analysis of the nomogram for 12 mo in the validation set; F: The decision curve analysis of the nomogram for 18 mo in the validation set.

Figure 7 Time-dependent receiver operating characteristic curve analysis and Kaplan-Meier survival curves of prognostic nomogram. A: Time-dependent receiver operating characteristic curve of the prognostic nomogram for 6, 12, and 18 mo in the training set; B: Time-dependent receiver operating characteristic curve of the prognostic nomogram for 6, 12, and 18 mo in the validation set; C: The Kaplan-Meier survival curves of the patients in the training set; D: The Kaplan-Meier survival curves of the patients in the validation set. AUC: Area under curve.