A Novel Pathological Scoring System for Hepatic Cirrhosis with Hepatocellular Carcinoma

Nomogram and calculator for predicting the prognosis of patients with giant hepatocellular carcinoma

OBJECTIVES

This study aimed to explore the factors affecting the overall survival (OS) of giant hepatocellular carcinoma (G-HCC) patients and establish a nomogram and an Internet-based OS calculator for evaluating the OS of G-HCC patients.

RESEARCH DESIGN AND METHODS

A total of 2445 G-HCC patients were searched in the SEER database. The independent variables affecting OS of G-HCC patients were determined by univariate and multivariate analyses, and a nomogram and Internet-based OS calculator were established. The accuracy of the nomogram was evaluated by the C-index, the AUC curve, and calibration curve.

RESULTS

Grade, surgery, radiotherapy, chemotherapy, T-staging, M-staging, AFP, and fibrosis were identified as independent variables affecting OS. These variables were included in the nomogram model and Internet-based OS calculator to evaluate OS in G-HCC patients. The C-indices and AUC of the nomogram are better than AJCC-staging system. Similarly, the calibration curves revealed that the actual survival was consistent with nomogram-based survival.

CONCLUSION

The nomogram and Internet-based OS calculator are superior to the traditional AJCC-staging system in the reliability and convenience of prognosis assessment for G-HCC patients, which is more conducive for clinicians to predict the survival of G-HCC patients and make the best treatment strategy.

ACTB may serve as a predictive marker for the efficacy of lenvatinib in patients with HBV-related early-stage hepatocellular carcinoma following partial hepatectomy: a retrospective cohort study

Background

The lack of effective biomarkers for the treatment of postoperative recurrence in hepatocellular carcinoma (HCC) persists despite lenvatinib therapy. This study aims to identify beta-actin (ACTB) as a predictive biomarker for lenvatinib that can facilitate individualized treatment for HCC.

Methods

This retrospective study included a subset of patients with HCC who underwent partial hepatectomy, with some receiving postoperative lenvatinib treatment and others not receiving lenvatinib treatment. A propensity score matching (PSM) analysis of patients who underwent treatment with or without lenvatinib following HCC partial hepatectomy was performed. Immunohistochemistry was employed to determine the levels of ACTB expression in HCC samples obtained from matched patients (n=225) enrolled in this study. The X-Tile was employed to determine the optimal cut-off point of ACTB levels for predicting time to recurrence (TTR). To assess the correlation between ACTB levels and lenvatinib efficacy, a subgroup analysis of TTR was conducted. A Cox regression model with an interaction term was utilized to assess the predictive significance of the model. Subsequently, a nomogram was developed and its discriminative ability and predictive accuracy were assessed using the concordance index (C-index) and calibration curve. For the investigation of the ACTB expression, HCC and para-tumoral normal tissues were employed. The patient-derived xenograft (PDX) model was utilized to validate the correlation between ACTB levels and lenvatinib responsiveness.

Results

After PSM, a total of 76 patients who underwent postoperative lenvatinib treatment were included in the analysis, with a median TTR of 24.35 months. Early-stage HCC patients with lower levels of ACTB exhibited a more favorable response to lenvatinib therapy compared to those with higher levels. The reduced expression of ACTB was indicative of the benefits of lenvatinib, as opposed to higher levels {hazard ratio (HR) =0.243 [95% confidence interval (CI): 0.096-0.619], P<0.001, P value for interaction =0.014}. In approximately 81.8% of cases involving HCC patients, there was an observed increase in the expression of ACTB. Multivariate analysis of the lenvatinib cohort revealed Child-Pugh [HR =5.416 (95% CI: 1.390-21.104), P=0.015], Barcelona Clinic Liver Cancer (BCLC) stage [HR =2.508 (95% CI: 1.116-5.639), P=0.026], and ACTB [HR =5.879 (95% CI: 2.424-14.259), P<0.001] score as independent factors for TTR, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.76 (95% CI: 0.71-0.84). Moreover, the PDXs derived from tumors exhibiting low levels of ACTB expression demonstrated a heightened sensitivity to lenvatinib treatment.

Conclusions

In patients with tumors treated with lenvatinib, low ACTB expression can predict a lower risk of recurrence. The validation of this potential biomarker in independent cohorts is necessary prior to its implementation for precision treatment stratification in patients undergoing partial hepatectomy for early-stage HCC.

A Prognostic Scoring System for Predicting Overall Survival of Patients with the TNM 8th Edition Stage I and II Hepatocellular Carcinoma After Surgery: A Population-Based Study

PURPOSE

Postoperative prognosis prediction models for patients with stage Ⅰ and Ⅱ hepatocellular carcinoma (HCC) according to the 8th edition of the Tumor-Node-Metastasis staging system after surgery are rare. This study aimed to build a prognostic score to predict survival outcomes and stratify these patients into different prognostic strata.

PATIENTS AND METHODS

We developed a web-based nomogram that incorporated four selected risk factors based on the multivariate Cox regression, using a training set (n=3567) from the Surveillance, Epidemiology, and End Results (SEER) database. It was validated with an independent internal set from the SEER database (n=1783) and an external validation set of 516 Chinese patients. The predictive performance and discrimination ability of our model were further evaluated and compared with those of the conventional HCC staging systems.

RESULTS

Our nomogram consistently outperformed the conventional staging systems in the training, internal validation set, and external validation set. We quantified the nomogram model into a numerical SNIG (an abbreviation of the incorporated variables - size, number, MVI, and grade) score by summing the points assigned to each incorporated variable, leading to the optimal cut-off values of 6 and 10, which could stratify patients into 3 categories (SNIG score <6, 6-10, ≥10). This yielded significantly different median overall survivals (interquartile ranges) of 42.0 (20.0-72.0) and 37.0 (17.0-67.0); 28.0 (12.0-60.0) and 42.0 (21.75-82.0); 40.0 (18.0-70.0) and 29.0 (11.5-61.0) months for the 3 categories in the entire SEER and external validation sets, respectively.

CONCLUSION

We developed a web-based SNIG model to graphically and numerically predict the overall survival of stage Ⅰ and Ⅱ HCC. This scoring system may shed light on risk stratification for these patients in clinical practice and clinical trials.

Prognostic Nomogram for Sorafenib Benefit in Hepatitis B Virus-Related Hepatocellular Carcinoma After Partial Hepatectomy

Background

Predicting the long-term prognosis of individuals who experienced sorafenib treatment following partial hepatectomy due to hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is difficult. This work aims to create an effective prognostic nomogram for HBV related HCC patients who are receiving sorafenib treatment as adjuvant therapy after surgery.

Methods

A total of 233 HBV-related HCC patients treated with or without sorafenib following partial hepatectomy at the Eastern Hepatobiliary Surgery Hospital from 2008 to 2013 were matched with propensity score matching analysis. The optimal cut-off point of the overall survival (OS) factor level was determined by x-tile. The selection of indicators was based on clinical findings. The Cox regression model with an interaction term was employed for evaluating the predictive value. Using a multivariate Cox proportional hazards model, a nomogram was subsequently formulated to analyze 111 patients treated with sorafenib. The nomogram's discriminative ability and predictive accuracy were determined using the concordance index (C-index), calibration, and ROC curve.

Results

The matched sorafenib cohort of 111 patients and control cohort of 118 patients were analyzed. Subgroup analysis revealed that low GPC3, pERK, pAKT, serum AFP levels, without MVI, under 50 years old, male, TNM stage I/II and BCLC stage 0/A were significantly associated with a better OS in patients subjected to sorafenib treatment compared to those without sorafenib treatment after surgery. Multivariate analysis of the sorafenib cohort revealed GPC3, pERK, pAKT, serum AST, and BCLC stage as independent factors for OS, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.73(95% CI, 0.67-0.78). The area under the ROC curve (AUC) for the nomogram to predict the survival for 1, 3, and 5-year was 0.726, 0.816, and 0.823, respectively.

Conclusion

This proposed nomogram shows the potential to make a precise prediction regarding the prognosis of HBV-related HCC patients and may help to stratify patients for personalized therapy following partial hepatectomy.