An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence

Figure 1

Construction of a nomogram capable of predicting prostate cancer patient bone metastasis. (a) Forest plot results corresponding to a univariate logistic regression model analysis of bone metastasis risk. (b) Forest plot results corresponding to a multivariate logistic regression model analysis of bone metastasis risk. The x-axis corresponds to the OR for bone metastasis. OR: odds ratio. CI: confidence interval. (c) A nomogram used to predict the odds of prostate cancer patient bone metastasis based on patient age, T_stage, N_stage, PSA, primary Gleason score, and secondary Gleason score. To use the nomogram, a straight line was drawn upwards from the appropriate point on each variable axis to the score axis, with the points for each of these predictors being summed together. The total sum score was then used to judge the odds of bone metastasis for that patient by drawing a line downwards. (d) ROC curves for the predictive nomogram in the training cohort (ROC curve AUC = 0.9; cutoff = 0.016; sensitivity = 0.864; specificity = 0.788). (e) ROC curves for the predictive nomogram in the validation cohort (ROC curve AUC = 0.904; cutoff = 0.016; sensitivity = 0.864; specificity = 0.812). (f) Calibration models for the predictive model when used to analyze the training cohort, with the actual and predicted probability being graphed against one another. (g) Calibration models for the predictive model when used to analyze the validation cohort. In the calibration curves, the reference line corresponds to perfect concordance between predicted and actual odds of bone metastasis.

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