C-Reactive Protein-Albumin Ratio (CAR): A More Promising Inflammation-Based Prognostic Marker for Patients Undergoing Curative Hepatectomy for Hepatocellular Carcinoma.

Background: Systemic inflammatory response is a hallmark of cancer and plays a significant role in the development and progression of various malignant tumors. This research aimed to estimate the prognostic function of the C-reactive protein-albumin ratio (CAR) in patients undergoing hepatectomy for hepatocellular carcinoma (HCC) and compare it with other inflammation-based prognostic scores, including the neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, monocyte-lymphocyte ratio, systemic immune inflammation index, prognostic index, Glasgow prognostic score, and modified Glasgow prognostic score. Methods: Retrospective analysis was conducted on data from 1039 HCC cases who underwent curative liver resection. The prognostic performance of CAR was compared with other scores using the area under the time-dependent receiver operating characteristic (t-ROC) curve. Multivariable Cox regression analyses were performed to confirm independent predictors for disease-free survival (DFS) and overall survival (OS). Results: The area under the t-ROC curve for CAR in the evaluation of DFS and OS was significantly greater than that of other scores and alpha-fetoprotein (AFP). Patients were stratified based on the optimal cut-off value of CAR, and the data revealed that both DFS and OS were remarkably worse in the high-CAR set compared to the low-CAR set. Multivariable Cox analysis demonstrated that CAR was an independent prognostic parameters for assessing DFS and OS. Regardless of AFP levels, all patients were subsequently divided into significantly different subgroups of DFS and OS based on CAR risk stratification. Similar results were observed when applying CAR risk stratification to other scoring systems. CAR also showed good clinical applicability in patients with different clinical features. Conclusion: CAR is a more effective inflammation-based prognostic marker than other scores and AFP in predicting DFS as well as OS among patients with HCC after curative hepatectomy.

Indocyanine Green Retention Test as a Predictor of Postoperative Complications in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma.

Background: Accurate preoperative estimation of liver function reserve is the key to the safety of hepatectomy. Recently, indocyanine green retention test at 15 minutes (ICG-R15) has been widely used to estimate hepatic function reserve in different liver diseases. The purpose of this research was to investigate the clinical value of ICG-R15 in predicting postoperative major complications and severe posthepatectomy liver failure (PHLF) in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) subjected to hepatectomy. Methods: A total of 354 HBV-associated HCC patients who underwent hepatectomy were enrolled. The Child-Pugh, model for end-stage liver disease (MELD), albumin-bilirubin (ALBI) and ICG-R15 for assessing postoperative complications risk were compared using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: Postoperative major complications developed in 32 patients (9.1%) and severe PHLF developed in 57 (16.1%) patients. Multivariate analyses revealed that ICG-R15 were independent factors for predicting postoperative major complications and severe PHLF. ROC curve analyses and DCA plots showed that the predictive abilities of ICG-R15 for postoperative major complications and severe PHLF risk was significantly greater than Child-Pugh, MELD, and ALBI scores. Similar results were obtained by stratifying different background subgroups. Then, patients were divided into three different risk cohorts, emphasizing the significantly discrepancy between the incidence of postoperative major complications and severe PHLF. Conclusion: Compared with Child-Pugh, MELD and ALBI scores, ICG-R15 revealed significantly advantages in predicting postoperative major complications and severe PHLF in HBV-related HCC patients subjected to liver resection.

Developmental artificial neural network model to evaluate the preoperative safe limit of future liver remnant volume for HCC combined with clinically significant portal hypertension.

Background & aims: Finding a way to comprehensively integrate the presence and grade of clinically significant portal hypertension, amount of preserved liver function and extent of hepatectomy into the guidelines for choosing appropriate candidates to hepatectomy remained challenging. This study sheds light on these issues to facilitate precise surgical decisions for clinicians. Methods: Independent risk factors associated with grade B/C post-hepatectomy liver failure were identified by stochastic forest algorithm and logistic regression in hepatitis B virus-related hepatocellular carcinoma patients. Results: The artificial neural network model was generated by integrating preoperative pre-ALB, prothrombin time, total bilirubin, AST, indocyanine green retention rate at 15 min, standard future liver remnant volume and clinically significant portal hypertension grade. In addition, stratification of patients into three risk groups emphasized significant distinctions in the risk of grade B/C post-hepatectomy liver failure. Conclusion: The authors' artificial neural network model could provide a reasonable therapeutic option for clinicians to select optimal candidates with clinically significant portal hypertension for hepatectomy and supplement the hepatocellular carcinoma surgical treatment algorithm.

Hepatectomy involves removing the tumor from the liver and is considered the most effective treatment for hepatocellular carcinoma (HCC). Clinically significant portal hypertension is characterized by the presence of gastric and/or esophageal varices and a platelet count <100 × 10⁹/l with the presence of splenomegaly, which would aggravate the risk of post-hepatectomy liver failure, and is therefore regarded as a contraindication to hepatectomy. Over the past few decades, with improvement in surgical techniques and perioperative care, the morbidity of postoperative complications and mortality have decreased greatly. Current HCC guidelines recommend the expansion of hepatectomy to HCC patients with clinically significant portal hypertension. However, determining how to select optimal candidates for hepatectomy remains challenging. The authors' artificial neural network is a mathematical tool developed by simulating the properties of neurons with large-scale information distribution and parallel structure. Here the authors retrospectively enrolled 871 hepatitis B virus-related HCC patients and developed an artificial neural network model to predict the risk of post-hepatectomy liver failure, which could provide a reasonable therapeutic option and facilitate precise surgical decisions for clinicians.

Preoperative fibrinogen-to-albumin ratio predicts the prognosis of patients with hepatocellular carcinoma subjected to hepatectomy.

BACKGROUND: Systemic inflammatory response (SIR) plays a crucial role in every step of tumorigenesis and development. More recently, the fibrinogen-to-albumin ratio (FAR), an inflammation-based model, was suggested as a prognostic maker for various cancer patients. This research aimed to estimate the prognostic abilities of FAR, neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet- lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) in patients with hepatocellular carcinoma (HCC) subjected to curative hepatectomy. METHODS: A total of 1,502 cases who underwent hepatectomy for HCC were included. The predictive performances of FAR, NLR, MLR, PLR and SII were assessed with regards to overall survival (OS) and disease-free survival (DFS). The area under the time-dependent receiver operating characteristic curve was used to compare prognostic performances. RESULTS: Data revealed that FAR had higher predictive accuracy than other inflammation-based models and alpha-fetoprotein (AFP) in assessing OS and DFS. Indeed, the OS and DFS of patients with high FAR (> 8.9), differentiated by the optimal cut-off value of FAR, were remarkably reduced (p < 0.05 for OS and DFS). Multivariate Cox regression analyses identified that AFP, FAR, clinically significant portal hypertension, tumor size, Barcelona Clinical Liver Cancer staging system, major resection and blood loss were independent indicators for predicting OS and DFS. Furthermore, these patients could be classified according to their FAR into significantly different subgroups, regardless of AFP levels (p < 0.05 for DFS and OS). Similar results were obtained in other inflammation-based prognostic models. CONCLUSIONS: Compared with NLR, MLR, PLR, SII and AFP, FAR showed significant advantages in predicting survival of HCC patients subjected to liver resection.

Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion.

BACKGROUND: The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion. METHODS: Nine hundred and three patients who underwent curative liver resection for HCC participated in this study. They were randomly divided into derivation (n = 679) and validation (n = 224) cohorts. The ANN model was developed in the derivation cohort and subsequently verified in the validation cohort. RESULTS: PHER morbidity in the derivation and validation cohorts was 34.8 and 39.2%, respectively. A multivariable analysis revealed that hepatitis B virus deoxyribonucleic acid load, γ-glutamyl transpeptidase level, α-fetoprotein level, tumor size, tumor differentiation, microvascular invasion, satellite nodules, and blood loss were significantly associated with PHER. These factors were incorporated into an ANN model, which displayed greater discriminatory abilities than a Cox's proportional hazards model, preexisting recurrence models, and commonly used staging systems for predicting PHER. The recurrence-free survival curves were significantly different between patients that had been stratified into two risk groups. CONCLUSION: When compared to other models and staging systems, the ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion.

Combining Aspartate Aminotransferase-to-Platelet Ratio Index with Future Liver Remnant to Assess Preoperative Hepatic Functional Reserve in Patients with Hepatocellular Carcinoma.

BACKGROUND: Accurate preoperative assessment of hepatic functional reserve is essential for conducting a safe hepatectomy. In recent years, aspartate aminotransferase-to-platelet ratio index (APRI) has been used as a noninvasive model for assessing fibrosis stage, hepatic functional reserve, and prognosis after hepatectomy with a high level of accuracy. The purpose of this research was to evaluate the clinical value of combining APRI with standardized future liver remnant (sFLR) for predicting severe post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). METHODS: Six hundred thirty-seven HCC patients who had undergone hepatectomy were enrolled in this study. The performance of the Child-Pugh (CP) grade, model for end-stage liver disease (MELD), APRI, sFLR, and APRI-sFLR in predicting severe PHLF was assessed using the area under the ROC curve (AUC). RESULTS: Severe PHLF was found to have developed in 101 (15.9%) patients. Multivariate logistic analyses identified that prealbumin, cirrhosis, APRI score, sFLR, and major resection were significantly associated with severe PHLF. The AUC values of the CP, MELD, APRI, and sFLR were 0.626, 0.604, 0.725, and 0.787, respectively, indicating that the APRI and sFLR showed significantly greater discriminatory abilities than CP and MELD (P < 0.05 for all). After APRI was combined with sFLR, the AUC value of APRI-sFLR for severe PHLF was 0.816, which greatly improved the prediction accuracy, compared with APRI or sFLR alone (P < 0.05 for all). Stratified analysis using the status of cirrhosis and extent of resection yielded similar results. Moreover, the incidence and grade of PHLF were significantly different among the three risk groups. CONCLUSION: The combination of APRI and sFLR can be considered to be a predictive factor with increased accuracy for severe PHLF in HCC patients, compared with CP grade, MELD, APRI, or sFLR alone.

Nomogram for prediction of the international study Group of Liver Surgery (ISGLS) grade B/C Posthepatectomy liver failure in HBV-related hepatocellular carcinoma patients: an external validation and prospective application study.

BACKGROUND: To develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. METHODS: Patients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application. RESULTS: There were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5, 11.0 and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC = 0.868, 95%CI = 0.836-0.900), internal validation (AUC = 0.868, 95%CI = 0.811-0.926) and external validation cohorts (AUC = 0.820, 95%CI = 0.756-0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score < 169 or ≧169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise. CONCLUSIONS: The nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma.

BACKGROUND: Posthepatectomy liver failure is a worrisome complication after major hepatectomy for hepatocellular carcinoma and is the leading cause of postoperative mortality. Recommendations for hepatectomy for hepatocellular carcinoma are based on the risk of severe posthepatectomy liver failure, and accurately predicting posthepatectomy liver failure risk before undertaking major hepatectomy is of great significance. Thus, herein, we aimed to establish and validate an artificial neural network model to predict severe posthepatectomy liver failure in patients with hepatocellular carcinoma who underwent hemihepatectomy. METHODS: Three hundred and fifty-three patients who underwent hemihepatectomy for hepatocellular carcinoma were included. We randomly divided the patients into a development set (n = 265, 75%) and a validation set (n = 88, 25%). Multivariate logistic analysis facilitated identification of independent variables that we incorporated into the artificial neural network model to predict severe posthepatectomy liver failure in the development set and then verified in the validation set. RESULTS: The morbidity of patients with severe posthepatectomy liver failure in the development and validation sets was 24.9% and 23.9%, respectively. Multivariate analysis revealed that platelet count, prothrombin time, total bilirubin, aspartate aminotransferase, and standardized future liver remnant were all significant predictors of severe posthepatectomy liver failure. Incorporating these factors, the artificial neural network model showed satisfactory area under the receiver operating characteristic curve for the development set of 0.880 (95% confidence interval, 0.836-0.925) and for the validation set of 0.876 (95% confidence interval, 0.801-0.950) in predicting severe posthepatectomy liver failure and achieved well-fitted calibration ability. The predictive performance of the artificial neural network model for severe posthepatectomy liver failure outperformed the traditional logistic regression model and commonly used scoring systems. Moreover, stratification into 3 risk groups highlighted significant differences between the incidences and grades of posthepatectomy liver failure. CONCLUSION: The artificial neural network model accurately predicted the risk of severe posthepatectomy liver failure in patients with hepatocellular carcinoma who underwent hemihepatectomy. Our artificial neural network model might help surgeons identify intermediate and high-risk patients to facilitate earlier interventions.

Artificial Neural Network Model for Liver Cirrhosis Diagnosis in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma.

BACKGROUND: Testing for the presence of liver cirrhosis (LC) is one of the most critical diagnostic and prognostic assessments for patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). More non-invasive tools are needed to diagnose LC but the predictive abilities of current models are still inconclusive. This study aimed to develop and validate a novel and non-invasive artificial neural network (ANN) model for diagnosing LC in patients with HBV-related HCC using routine laboratory serological indicators. METHODS: A total of 1152 HBV-related HCC patients who underwent hepatectomy were included and randomly divided into the training set (n = 864, 75%) and validation set (n = 288, 25%). The ANN model was constructed from the training set using multivariate Logistic regression analysis and then verified in the validation set. RESULTS: The morbidity of LC in the training and validation sets was 41.2% and 46.8%, respectively. Multivariate analysis showed that age, platelet count, prothrombin time and total bilirubin were independent risk factors for LC (P < 0.05). The area under the ROC curve (AUC) analyses revealed that the ANN model had higher predictive accuracy than the Logistic model (ANN: 0.757 vs Logistic: 0.721; P < 0.001), and other scoring systems (ANN: 0.757 vs CP: 0.532, MELD: 0.594, ALBI: 0.575, APRI: 0.621, FIB-4: 0.644, AAR: 0.491, and GPR: 0.604; P < 0.05 for all) in diagnosing LC. Similar results were obtained in the validation set. CONCLUSION: The ANN model has better diagnostic capabilities than other commonly used models and scoring systems in assessing LC risk in patients with HBV-related HCC.

Combination Of ALBI And APRI To Predict Post-Hepatectomy Liver Failure After Liver Resection For HBV-Related HCC Patients.

PURPOSE: Post-hepatectomy liver failure (PHLF) is a severe complication in hepatocellular carcinoma (HCC) patients who have undergone hepatectomy. This research aimed to investigate the combination of albumin-bilirubin (ALBI) score and aspartate aminotransferase-platelet ratio index (APRI) as a novel approach in predicting PHLF risk in hepatitis B virus (HBV)-related HCC patients. PATIENTS AND METHODS: HBV-related HCC patients who underwent hepatectomy from January 2006 to October 2013 were enrolled in this study. A novel model was constructed using a combination of ALBI and APRI scores to predict PHLF risk, and the prognostic value of the model was evaluated and compared with Child-Pugh (C-P) grade, ALBI score and APRI score. RESULTS: A total of 1,055 HCC patients were retrospectively studied, which included 151 experienced PHLF. Univariable and multivariate analyses showed that the ALBI and APRI scores were independent predictors of PHLF. The area under the ROC curve (AUC) of the ALBI score, APRI score, and C-P grade was 0.717, 0.720, and 0.602, respectively, with AUC (ALBI) > AUC (C-P) (P <0.001) and AUC (APRI) > AUC (C-P) (P <0.001). After ALBI was associated with APRI, the AUC (ALBI-APRI) was 0.766, and AUC (ALBI-APRI) > AUC (ALBI) (P <0.001), AUC (ALBI-APRI) > AUC (APRI) (P =0.047). Our results indicated that ALBI and APRI scores had higher discriminatory abilities than C-P grade in predicting the risk of PHLF, and the ALBI-APRI model could enhance the capability of predicting PHLF compared to ALBI or APRI alone. CONCLUSION: ALBI-APRI score is a novel and effective predictive model of PHLF for HBV-related HCC patients, and its accuracy in predicting the risk of PHLF is better than that of C-P, ALBI and APRI scores.

Preoperative aspartate aminotransferase-to-platelet-ratio index as a predictor of posthepatectomy liver failure for resectable hepatocellular carcinoma.

PURPOSE: This study aimed to investigate the efficacy of preoperative aspartate aminotransferase-to-platelet-ratio index (APRI) score to predict the risk of posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) after liver resection, and to compare the discriminatory performance of the APRI with the Child-Pugh score, model for end-stage liver disease (MELD) score, and albumin-bilirubin (ALBI) score. PATIENTS AND METHODS: A total of 1,044 consecutive patients with HCC who underwent liver resection were enrolled and studied. Univariate and multivariate analyses were performed to investigate risk factors associated with PHLF. Predictive discrimination of Child-Pugh, MELD, ALBI, and APRI scores for predicting PHLF were assessed according to area under the ROC curve. The cutoff value of the APRI score for predicting PHLF was determined by ROC analysis. APRI scores were stratified by dichotomy to analyze correlations with incidence and grade of PHLF. RESULTS: PHLF occurred in 213 (20.4%) patients. Univariate and multivariate analyses revealed that Child-Pugh, MELD, ALBI, and APRI scores were significantly associated with PHLF. Area under the ROC analysis revealed that the APRI score for predicting PHLF was significantly more accurate than Child-Pugh, MELD, or ALBI scores. With an optimal cutoff value of 0.55, the sensitivity and specificity of the APRI score for predicting PHLF were 72.2% and 68.0%, respectively, and the incidence and grade of PHLF in patients with high risk (APRI score >0.55) was significantly higher than in the low-risk cohort (APRI score <0.55). CONCLUSION: The APRI score predicted PHLF in patients with HCC undergoing liver resection more accurately than Child-Pugh, MELD, or ALBI scores.