[Retracted] Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature

Figure 3

Multivariate Cox regression was performed to validate independent prognostic factors in ACC, and a nomogram model was established to predict progression risk for each patient. (a) Forest plot of corresponding multivariate HR for six algorithms, including age, gender, stage, laterality, and NAGs. Tumor stages assessed at stage I, stage II, stage III, stage IV, and unknown because of the pivotal effects on prognosis. Lines that do not cross the dashed line are considered as independent prognostic factors. (b) Discriminative power of four clinicopathological features as well as the nomogram and NAGs; the accuracy was equal to corresponding AUC value, and the value more than 0.75 represents high stability. (c) Establishment of a nomogram combining tumor stage and NAGs. For a given patient, find patient’s tumor stage on stage axis, find patient’s NAGs on NAGs axis, each time draw straight line upward toward points axis, total points were the sum of each predictor point, find the total point on total point on total point axis, draw straight line to the bottom 3-year progression probability and 5-year progression probability axis, and the points in progression line represented the progression probability. (d) Calibration plot for the nomogram. The dashed line represents the ideal nomogram, the solid line represents our nomogram, and a value of 0.865 indicates that our nomogram is very close to the ideal nomogram. (e) DCA showed that our nomogram had the greatest net benefit among the four policies. (f) The clinical impact curve for the predictive value of the nomogram model, the orange solid line represents the predictive number of patients with high risk, and the black dashed line represents the actual number of patients with high risk.

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