Development and validation of a nomogram for assessing survival in patients with hepatocellular carcinoma after hepatectomy

A pathologic scoring system for predicting postoperative prognosis in patients with ruptured hepatocellular carcinoma

BACKGROUND

The accuracy of pathological factors to predict the prognosis of patients with ruptured hepatocellular carcinoma (rHCC) is unclear. We aimed to develop and validate a novel scoring system based on pathological factors to predict the postoperative survival of patients with rHCC.

METHOD

Patients with rHCC who underwent hepatectomy were recruited from three hospitals and allocated to the training (n = 221) and validation (n = 194) cohorts. A new scoring system, namely the MSE (microvascular invasion-satellite foci-Edmondson Steiner) score, was established based on three pathological factors using univariate and multivariate Cox proportional hazards regression analyses, including microvascular invasion, satellite foci, and differentiation grade. Finally, patients were stratified into three groups based on their risk of prognosis (low, intermediate, or high) according to their MSE score. We also constructed MSE score-based nomograms. The performance of the nomograms was assessed by receiver operating characteristic and calibration curve analyses and validated using the validation cohort.

RESULTS

Three pathological factors were significantly correlated with overall survival (OS) and recurrence-free survival (RFS), three of which were included in the MSE score. The score can clearly stratify rHCC patients after hepatectomy (P < 0.05). And we established nomograms based on the MSE score (MSE score, Barcelona Clinic Liver Cancer stage, and alpha-fetoprotein concentration) to predict postoperative OS and RFS in patients with rHCC. The nomograms showed good discrimination, with C-indices over 0.760 for OS and RFS at 1, 3, and 5 years, respectively. The calibration curve showed excellent nomogram calibration, which was also verified in the validation cohort.

CONCLUSION

The clinical MSE score were accurate in predicting OS and RFS in patients with rHCC with resectable lesions after hepatectomy.

Preoperative alpha fetoprotein, total bilirubin, fibrinogen, albumin, and lymphocytes predict postoperative survival in hepatocellular carcinoma

AIMS

Our study focused on exploring the feasible prognostic laboratory parameters of HCC and establishing a score model to estimate individualized overall survival (OS) in HCC after resection.

METHODS

Four hundred and sixty-one patients with HCC who underwent hepatectomy between January 2010 and December 2017 was enrolled in this investigation. Cox proportional hazards model was conducted to analyze the prognostic value of laboratory parameters. The score model construction was based on the forest plot results. Overall survival was evaluated by Kaplan-Meier method and the log-rank test. The novel score model was validated in an external validation cohort from a different medical institution.

RESULTS

We identified that alpha fetoprotein (AFP), total bilirubin (TB), fibrinogen (FIB), albumin (ALB), and lymphocyte (LY) were independent prognostic factors. High AFP, TB, FIB (HR > 1, p < 0.05), and low ALB, LY (HR < 1, p < 0.05) were associated with the survival of HCC. The novel score model of OS based on these five independent prognostic factors achieved high C-index of 0.773 (95% confidence interval [CI]: 0.738-0.808), which was significantly higher than those of the single five independent factors (0.572-0.738). The score model was validated in the external cohort whose C-index was 0.7268 (95% CI: 0.6744-0.7792).

CONCLUSION

The novel score model we established was an easy-to-use tool which could enable individualized estimation of OS in patients with HCC who underwent curative hepatectomy.

Advances in post-operative prognostic models for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies and a leading cause of cancer-related death worldwide. Surgery remains the primary and most successful therapy option for the treatment of early- and mid-stage HCCs, but the high heterogeneity of HCC renders prognostic prediction challenging. The construction of relevant prognostic models helps to stratify the prognosis of surgically treated patients and guide personalized clinical decision-making, thereby improving patient survival rates. Currently, the prognostic assessment of HCC is based on several commonly used staging systems, such as Tumor-Node-Metastasis (TNM), Cancer of the Liver Italian Program (CLIP), and Barcelona Clinic Liver Cancer (BCLC). Given the insufficiency of these staging systems and the aim to improve the accuracy of prognostic prediction, researchers have incorporated further prognostic factors, such as microvascular infiltration, and proposed some new prognostic models for HCC. To provide insights into the prospects of clinical oncology research, this review describes the commonly used HCC staging systems and new models proposed in recent years.

A New Clinical Nomogram From the TCGA Database to Predict the Prognosis of Hepatocellular Carcinoma

Background and Aim

Hepatocellular carcinoma is a common malignant tumor of the digestive system with a poor prognosis. The high recurrence rate and metastasis after surgery reduce the survival time of patients. Therefore, assessing the overall survival of patients with hepatocellular carcinoma after hepatectomy is critical to clinicians' clinical decision-making. Conventional hepatocellular carcinoma assessment systems (such as tumor lymph node metastasis and Barcelona clinical hepatocellular carcinoma) are obviously insufficient in assessing the overall survival rate of patients. This research is devoted to the development of nomogram assessment tools to assess the overall survival probability of patients undergoing liver resection.

Methods

We collected the clinical and pathological information of 438 hepatocellular carcinoma patients undergoing surgery from The Cancer Genome Atlas (TCGA) database, then excluded 87 patients who did not meet inclusion criteria. Univariate and multivariate analyses were performed on patient characteristics and related pathological factors. Finally, we developed a nomogram model to predict patient's prognosis.

Results

A retrospective analysis of 438 consecutive samples from the TCGA database of patients with hepatocellular carcinoma who underwent potentially curative liver resections. Six risk factors were included in the final model. In the training set, the discriminative ability of the nomogram was very good (concordance index = 0.944), and the external verification method (concordance index = 0.962) was used for verification. At the same time, the internal and external calibration of the model was verified, showing that the model was well calibrated. The calibration between the evaluation of the nomogram and the actual observations was good. According to the patient's risk factors, we determined the patient's Kaplan-Meyer survival analysis curve. Finally, the clinical decision curve was used to compare the benefits of two different models in evaluating patients' clinical outcomes.

Conclusions

The nomogram can be used to evaluate the post-hepatectomy 1-, 3-, and 5-year survival rates of patients with hepatocellular carcinoma. The Kaplan-Meyer curve can intuitively display the survival differences among patients with various risk factors. The clinical decision curve is a good reference guide for clinical application.

Development and Validation of a Clinicopathological-Based Nomogram to Predict the Survival Outcome of Patients with Recurrent Hepatocellular Carcinoma After Hepatectomy Who Underwent Microwave Ablation

Aim

To develop and validate a nomogram for predicting the overall survival (OS) in patients with recurrent hepatocellular carcinoma (HCC) after hepatectomy who underwent microwave ablation (MWA).

Methods

The training cohort included 299 patients with recurrent HCCs after hepatectomy who met the Milan criteria and received MWA from April 2007 to December 2017. Baseline characteristics were collected to identify risk factors for the determination of death after MWA. A multivariate Cox proportional hazards model based on significant risk factors was used to develop the nomogram, which was then assessed for its predictive accuracy using Harrell’s C-index and the area under the curve (AUC). The nomogram was validated by internal (n = 240) and external cohorts (n = 205) from another hospital.

Results

After a median follow-up of 32.3 months, 38.8% (116/299) of patients had died. Multivariate Cox proportional hazards analyses showed that comorbid disease, early recurrence, and albumin-bilirubin (ALBI) grades 2–3 were independent prognostic factors for poor OS. This nomogram accurately stratified patients into subgroups with low or high risk. The 1-, 3- and 5-year OS rates in the low-risk subgroup were 99.4%, 97.2%, and 86.1%, respectively, and they were 92.8%, 70.3%, and 45.8% in the high-risk subgroup (P < 0.001). The nomogram predicted OS in the training cohort with a C-index score of 0.801 (95% CI 0.761–0.841). The nomogram was validated by internal and external cohorts, with C-index scores of 0.792 (95% CI 0.738–0.846) and 0.744 (95% CI 0.703–0.785), respectively.

Conclusion

The nomogram provides individualized risk estimates for long-term OS for patients with recurrent HCC after hepatectomy who underwent MWA.