Construction and Validation of a Novel Nomogram Predicting Recurrence in Alpha-Fetoprotein-Negative Hepatocellular Carcinoma Post-Surgery Using an Innovative Liver Function-Nutrition-Inflammation-Immune (LFNII) Score: A Bicentric Investigation

Abstract

Purpose: We developed a nomogram based on the liver function, nutrition, inflammation, and immunity (LFNII) score to predict recurrence-free survival (RFS) post-resection in patients with hepatocellular carcinoma (HCC) exhibiting alpha-fetoprotein (AFP) negativity (AFP ≤20 ng/mL).

Patients and methods: Clinical data of 661 patients diagnosed with alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) who underwent surgical resection at two medical centers between 2012 and 2021 were collected. A total of 462 and 199 patients served as the training and validation sets, respectively. Pre-operative blood markers were collected and analyzed for LFNII. The LFNII score was formulated using the least absolute shrinkage and selection operator Cox regression model. A nomogram model was developed using the training set to incorporate other relevant clinicopathological indicators and predict postoperative recurrence. Model discrimination was assessed using the receiver operating characteristic curve, calibration was evaluated using a calibration curve, and clinical applicability was assessed using clinical decision curve analysis. A comparison with liver cancer staging was performed using the nomogram model. Finally, a cohort study was conducted to validate our findings.

Results: We derived the LFNII scores from nine indicators. Elevated LFNII scores correlated with unfavorable clinicopathological features. The LFNII score area under the curve revealed superior predictive efficacy at 1-, 2-, and 5-year RFS intervals, with values of 0.675, 0.658, and 0.633, respectively. Multivariate Cox analysis revealed that a high LFNII score independently increased RFS risk in patients with AFP-NHCC. The C-index of the LFNII-nomogram model was 0.686 (95% confidence interval [CI], 0.651-0.721). The nomogram model's clinical application value surpassed that of standard HCC staging systems.

Conclusion: The LFNII score-derived nomogram effectively predicted the RFS of patients with AFP-NHCC after curative resection.

Keywords: alpha-fetoprotein-negative; hepatocellular carcinoma; immunity; inflammation; nutrition; recurrence-free survival.

Figure 1

Flow chart of the retrospective study.

Figure 2

Recurrence-free survival Kaplan–Meier curves of patients with AFP-NHCC using ALRI (A), SIRI (B), ANRI (C), SII (D), PNI (E), GAPR (F), AAR (G), AAPR (H), and ALBI Grades (I). ALRI, aspartate aminotransferase to lymphocyte ratio; SIRI, systemic inflammation response index; ANRI, aspartate aminotransferase to neutrophil ratio; SII, systemic immune-inflammation index; PNI, prognostic nutritional index; GAPR, gamma-glutamyl transpeptidase to alkaline phosphatase ratio; AAR, alanine aminotransferase to aspartate aminotransferase ratio; AAPR, albumin to alkaline phosphatase ratio; ALBI, albumin-bilirubin ratio.

Figure 3

Construction of the LFNII Score using the LASSO Cox Regression Model. (A) LASSO coefficient profiles of the nine features. (B) Optimal parameter (lambda) selection in the LASSO model utilizing ten-fold cross-validation through minimum criteria. Dotted vertical lines indicate the optimal values determined by Lambda.min and Lambda.1 se. (C) Risk factor maps displaying the relationship between the LFNII score and clinicopathological features, highlighting the association of high LFNII scores with poor clinicopathological features and advanced tumor stage. (D-I) Receiver operating characteristic (ROC) curves illustrate the predictive abilities of the LFNII score and the nine indicators for the 1-, 2-, and 5-year RFS in patients with AFP-NHCC postoperatively. LFNII, liver function-nutrition-inflammation-immune; MVI, microvascular invasion; AUC, area under the curve; RFS, recurrence-free survival.

Figure 4

LFNII score-based nomogram model construction and validation. (A) The Kaplan–Meier (KM) curve demonstrates the association of high LFNII with shorter post-operative recurrence-free survival. (B) LFNII score-based nomogram model for predicting postoperative recurrence in patients with AFP-NHCC. (C) LFNII-nomogram receiver operating characteristic (ROC) curve for predicting the 12-, 24-, and 60-month RFS in the training set. (D) ROC curve of the LFNII-nomogram for the 12-, 24-, and 60-month RFS in the validation set. (E) LFNII-nomogram calibration curve: predicted and observed 12-, 24-, and 60-month RFS in the training set. (F) LFNII-nomogram calibration curve: predicted and observed 12-, 24-, and 60-month RFS in the validation set. LFNII, liver function-nutrition-inflammation-immune; MVI, microvascular invasion; AUC, Area Under the Curve; RFS, Recurrence-Free Survival. AFP-NHCC, alpha-fetoprotein-negative HCC.

Figure 5

Decision curve analysis (DCA) of recurrence-free survival (RFS) prediction using the LFNII-nomogram and rationality analysis of the nomogram model. DCA of the nomogram, LFNII score, Scheuer Scoring System, MVI, liver capsule invasion, and intraoperative blood loss for 1-year RFS (A), 2-year RFS (B), and 5-year RFS (C) in the training set. DCA of the nomogram, LFNII Score, Scheuer Scoring System, MVI, Liver capsule invasion, and intraoperative blood loss for 1-year RFS (D), 2-year RFS (E), and 5-year RFS (F) in the validation set. Kaplan–Meier curves demonstrated that a high nomogram score was associated with a poor prognosis in the training (G) and validation (H) sets.

Figure 6

Comparison of the nomogram model with commonly used HCC staging systems. Decision curve analysis (DCA) for 1-, 2-, and 5-year recurrence-free survival (RFS) in the training set (A-C) comparing the nomogram, AJCC-TNM stage, and BCLC stage. DCA curves for 1-, 2-, and 5-year RFS in the validation set (D-F) comparing the nomogram, AJCC-TNM stage, and BCLC stage. Time-dependent area under the curve (AUC) of the LFNII-nomogram, AJCC-TNM stage, and BCLC stage for predicting RFS in the training (G) and validation (H) sets. AUC, area under the curve; AJCC-TNM, American Joint Committee on Cancer tumor–node–metastasis; BCLC, Barcelona Clinic Liver Cancer system.