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Journal of Hepatocellular Carcinoma Volume 11, 2024 - Issue
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ORIGINAL RESEARCH

Development and Validation of a Prognostic Nomogram for Patients with AFP and DCP Double-Negative Hepatocellular Carcinoma After Local Ablation

Wenying Qiao1 Interventional Therapy Center for Oncology, Beijing You’an Hospital, Capital Medical University, Beijing, People’s Republic of China;2 Beijing Di’tan Hospital, Capital Medical University, Beijing, People’s Republic of China;3 Changping Laboratory, Beijing, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0002-9687-4073View further author information
ORCID Icon,
Jiashuo Li2 Beijing Di’tan Hospital, Capital Medical University, Beijing, People’s Republic of ChinaView further author information
,
Qi Wang2 Beijing Di’tan Hospital, Capital Medical University, Beijing, People’s Republic of ChinaView further author information
,
Ronghua Jin2 Beijing Di’tan Hospital, Capital Medical University, Beijing, People’s Republic of China;3 Changping Laboratory, Beijing, People’s Republic of ChinaCorrespondence[email protected]
View further author information
&
Honghai Zhang1 Interventional Therapy Center for Oncology, Beijing You’an Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence[email protected]
View further author information
Pages 271-284 | Received 02 Nov 2023, Accepted 26 Jan 2024, Published online: 03 Feb 2024

Figures & data

Table 1 Demographics and Clinical Characteristics for Training and Validation Cohorts

Figure 1 Screening of variables based on Lasso regression. (A) The variation characteristics of the coefficient of variables. (B) the selection process of the optimum value of the parameter λ in the Lasso regression model by cross-validation method.

Notes: Lasso regression incorporates an L1 penalization term into the loss function, forcing more coefficients to be zero. This technique governs the rigor of feature selection by adjusting the regularization parameter λ, which could facilitate feature screening and reduce the model’s dimensionality.
Figure 1 Screening of variables based on Lasso regression. (A) The variation characteristics of the coefficient of variables. (B) the selection process of the optimum value of the parameter λ in the Lasso regression model by cross-validation method.

Table 2 Multivariate Cox Regression Analysis Based on the Results of Lasso Regression

Figure 2 Nomogram, including age, TN, TS and GGT for 1-, 3- and 5-year RFS in patients with AFP and DCP double-negative HCC.

Notes: The total point is calculated by adding up the scores of variables included in the model. Then, a vertical line is drawn at the location of the corresponding total point so that it intersects three lines representing the risk of recurrence. The values shown at the intersections indicate 1-, 3- and 5-year RFS.
Abbreviations: TN, tumor number; TS, tumor size; GGT, gamma-glutamyl transferase; RFS, recurrence-free survival; AFP, alpha-fetoprotein; DCP, des-gamma-carboxyprothrombin; HCC, Hepatocellular Carcinoma.
Figure 2 Nomogram, including age, TN, TS and GGT for 1-, 3- and 5-year RFS in patients with AFP and DCP double-negative HCC.

Figure 3 ROC curves of the nomogram in the training and validation cohort. (A) In the training cohort, the AUCs for 1-, 3- and 5-year RFS were 0.738, 0.742 and 0.836, respectively. (B) In the validation cohort, the AUCs for 1- and 3-year RFS were 0.758 and 0.821, respectively.

Notes: The ROC curve is plotted with the 1-specificity on the x-axis against the sensitivity on the y-axis. The area under the ROC curve (AUC) can intuitively evaluate the discriminative ability, and AUC values closer to 1 indicate higher prediction accuracy.
Abbreviations: ROC, receiver operating characteristic curve; AUC, area under the ROC curve; RFS, recurrence-free survival.
Figure 3 ROC curves of the nomogram in the training and validation cohort. (A) In the training cohort, the AUCs for 1-, 3- and 5-year RFS were 0.738, 0.742 and 0.836, respectively. (B) In the validation cohort, the AUCs for 1- and 3-year RFS were 0.758 and 0.821, respectively.

Figure 4 Calibration curves of the nomogram in the training and validation cohort. (A) training cohort; (B) validation cohort.

Notes: In a calibration curve, the x-axis typically represents the predicted probability from the model, while the y-axis is the actual probability. The diagonal line represents the ideal condition under which the predicted probability is equal to the actual probability. A close proximity of model’s curve to the ideal line is indicative of an effective calibration.
Figure 4 Calibration curves of the nomogram in the training and validation cohort. (A) training cohort; (B) validation cohort.

Figure 5 DCA for recurrence in the training and validation cohort. (A–C) DCA for 1-, 3- and 5-year RFS in the training cohort. (D and E) DCA for 1-, and 3-year RFS in the validation cohort.

Notes: The x-axis of DCA curve is the threshold probability, representing the possibility at which the patient would choose intervention. As for y-axis, it is the net benefit corresponding to each threshold. There are two additional lines in DCA curve, one about treating all patients and another about treating none. A model is considered useful if its use range is above the treat-all and treat-none curves.
Abbreviations: DCA, decision curve analysis; RFS, recurrence-free survival.
Figure 5 DCA for recurrence in the training and validation cohort. (A–C) DCA for 1-, 3- and 5-year RFS in the training cohort. (D and E) DCA for 1-, and 3-year RFS in the validation cohort.

Figure 6 Kaplan–Meier curves for the low-risk group, intermediate-risk group, and high-risk group in the training and validation cohort. (A) training cohort; (B) validation cohort.

Notes: The Kaplan–Meier curve is drawn with time on the x-axis and the survival probability on the y-axis. The Kaplan–Meier curve steps downward over time as the event of interest (often death or recurrence) occurs. Log rank test is commonly used to compare the differences between groups.
Figure 6 Kaplan–Meier curves for the low-risk group, intermediate-risk group, and high-risk group in the training and validation cohort. (A) training cohort; (B) validation cohort.

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