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ORIGINAL RESEARCH

A Novel Nomogram for the Preoperative Prediction of Edmondson-Steiner Grade III-IV in Hepatocellular Carcinoma Patients

ORCID Icon, ORCID Icon, ORCID Icon, , , , , , & show all
Pages 1399-1409 | Received 12 May 2023, Accepted 17 Aug 2023, Published online: 23 Aug 2023

Figures & data

Figure 1 Typical pathological section images of Edmondson-Steiner grade. (A) Grade I. (B) Grade II. (C) Grade III. (D) Grade IV.

Figure 1 Typical pathological section images of Edmondson-Steiner grade. (A) Grade I. (B) Grade II. (C) Grade III. (D) Grade IV.

Table 1 Baseline and Clinicopathological Participant Characteristics According to the Edmondson-Steiner Grade

Table 2 Multivariate Logistic Regression Analysis of Edmondson-Steiner Grade III–IV Based on Univariate Analysis

Figure 2 Six risk factors associated with Edmondson-Steiner grade III–IV in univariate analysis (p < 0.05). (A) Alpha-fetoprotein (AFP). (B) Des-γ-carboxy prothrombin (DCP). (C) Aspartate aminotransferase to lymphocyte ratio index (ALRI). (D) Hepatitis B virus surface antigen (HBsAg). (E) Hepatitis C virus antibodies (HCVAb). (F) Macrovascular invasion. *p < 0.05, ***p < 0.001. P-values less than 0.05 or 0.001 indicate significant difference.

Figure 2 Six risk factors associated with Edmondson-Steiner grade III–IV in univariate analysis (p < 0.05). (A) Alpha-fetoprotein (AFP). (B) Des-γ-carboxy prothrombin (DCP). (C) Aspartate aminotransferase to lymphocyte ratio index (ALRI). (D) Hepatitis B virus surface antigen (HBsAg). (E) Hepatitis C virus antibodies (HCVAb). (F) Macrovascular invasion. *p < 0.05, ***p < 0.001. P-values less than 0.05 or 0.001 indicate significant difference.

Figure 3 The development and evaluation of the nomogram model. (A) Established nomogram based on the multivariate logistic regression analysis. (B) The receiver operating characteristic (ROC) curve and area under the curve (AUC) value of the nomogram. (C) The calibration curve of the model (mean absolute error = 0.043).

Figure 3 The development and evaluation of the nomogram model. (A) Established nomogram based on the multivariate logistic regression analysis. (B) The receiver operating characteristic (ROC) curve and area under the curve (AUC) value of the nomogram. (C) The calibration curve of the model (mean absolute error = 0.043).

Figure 4 The decision curve analysis (DCA) (A) and clinical impact curve (B) of the nomogram model.

Figure 4 The decision curve analysis (DCA) (A) and clinical impact curve (B) of the nomogram model.