The Significance of Gamma-Glutamyl Transpeptidase to Lymphocyte Count Ratio in the Early Postoperative Recurrence Monitoring and Prognosis Prediction of AFP-Negative Hepatocellular Carcinoma

Early identification of hepatocellular carcinoma patients at high-risk of recurrence using the ADV score: a multicenter retrospective study

BACKGROUND

Postoperative recurrence is a vital reason for poor 5-year overall survival in hepatocellular carcinoma (HCC) patients. The ADV score is considered a parameter that can quantify HCC aggressiveness. This study aimed to identify HCC patients at high-risk of recurrence early using the ADV score.

METHODS

The medical data of consecutive HCC patients undergoing hepatectomy from The First Affiliated Hospital of Nanjing Medical University (TFAHNJMU) and Nanjing Drum Tower Hospital (NJDTH) were retrospectively reviewed. Based on the status of microvascular invasion and the Edmondson-Steiner grade, HCC patients were divided into three groups: low-risk group (group 1: no risk factor exists), medium-risk group (group 2: one risk factor exists), and high-risk group (group 3: coexistence of two risk factors). In the training cohort (TFAHNJMU), the R package nnet was used to establish a multi-categorical unordered logistic regression model based on the ADV score to predict three risk groups. The Welch's T-test was used to compare differences in clinical variables in three predicted risk groups. NJDTH served as an external validation center. At last, the confusion matrix was developed using the R package caret to evaluate the diagnostic performance of the model.

RESULTS

350 and 405 patients from TFAHNJMU and NJDTH were included. HCC patients in different risk groups had significantly different liver function and inflammation levels. Density maps demonstrated that the ADV score could best differentiate between the three risk groups. The probability curve was plotted according to the predicted results of the multi-categorical unordered logistic regression model, and the best cut-off values of the ADV score were as follows: low-risk ≤ 3.4 log, 3.4 log < medium-risk ≤ 5.7 log, and high-risk > 5.7 log. The sensitivities of the ADV score predicting the high-risk group (group 3) were 70.2% (99/141) and 78.8% (63/80) in the training and external validation cohort, respectively.

CONCLUSION

The ADV score might become a valuable marker for screening patients at high-risk of HCC recurrence with a cut-off value of 5.7 log, which might help surgeons, pathologists, and HCC patients make appropriate clinical decisions.

Recurrence of Hepatocellular Carcinoma in Patients with Low Albumin-Bilirubin Grade in TACE Combined with Ablation: A Random Forest Cox Predictive Model

Purpose

The aim of our study was to investigate the relationship between albumin-bilirubin (ALBI) grade and recurrence in patients who underwent TACE sequential ablation. We developed and validated a nomogram to predict low levels of ALBI patients' recurrence.

Patients and Methods

A total of 880 patients undergoing TACE combined ablation at Beijing Youan Hospital from January 2014 to December 2021 were retrospectively enrolled, including 415 patients with L-ALBI (≤-2.6) and 465 patients with high levels (>-2.6) of ALBI (H-ALBI). L-ALBI patients were randomized in a 7:3 ratio into the training cohort (N=289) and validation cohort (N=126). Multivariate Cox regression followed by random survival forest was carried out to identify independent risk factors for prediction nomogram construction. An examination of nomogram accuracy was performed using the C-index, receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA) curves. According to the nomogram, the patients were divided into low-risk, intermediate-risk, and high-risk groups. Kaplan-Meier (KM) curves were applied to compare the difference in recurrence-free survival (RFS) among the three groups.

Results

The median RFS in L-ALBI patients was significantly longer than the H-ALBI patients (40.8m vs 20.1m, HR:1.71, 95% CI:1.44-2.04, P<0.0001). The nomogram was composed of five variables, such as age, Barcelona Clinic Liver Cancer (BCLC) stage, globulin, gamma-glutamyl transferase to lymphocyte ratio (GLR), and international normalized ratio (INR). The C-index (0.722 and 0.731) and 1-, 3-, and 5-year AUCs (0.725, 0.803, 0.870, and 0.764, 0.816, 0.798) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves demonstrated good consistency and good clinical utility. There were significant differences in RFS between the low-risk, intermediate-risk, and high-risk groups (P<0.0001).

Conclusion

L-ALBI Patients who underwent TACE combined ablation had better recurrence-free survival than patients with H-ALBI. The nomogram developed and validated in our study had good predictive ability in recurrence for L-ALBI patients.

Incorporating Inflammatory Markers and Clinical Indicators into a Predictive Model of Single Small Hepatocellular Carcinoma Recurrence After Primary Locoregional Treatments

Purpose

We explored the role of tumor size and number in the prognosis of HCC patients who underwent ablation and created a nomogram based on machine learning to predict the recurrence.

Patients and Methods

A total of 990 HCC patients who underwent transcatheter arterial chemoembolization (TACE) combined ablation at Beijing Youan Hospital from January 2014 to December 2021 were prospectively enrolled, including 478 patients with single small HCC (S-S), 209 patients with single large (≥30mm) HCC (S-L), 182 patients with multiple small HCC (M-S), and 121 patients with multiple large HCC (M-L). S-S patients were randomized in a 7:3 ratio into the training cohort (N=334) and the validation cohort (N=144). Lasso-Cox regression analysis was carried out to identify independent risk factors, which were used to construct a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves. Patients in the training and validation cohorts were divided into low-risk, intermediate-risk, and high-risk groups based on the risk scores of the nomogram.

Results

The median recurrence-free survival (mRFS) in S-S patients was significantly longer than the S-L, M-S, and S-L patients (P<0.0001). The content of the nomogram includes age, monocyte-to-lymphocyte (MLR), gamma-glutamyl transferase-to-lymphocyte (GLR), International normalized ratio (INR), and Erythrocyte (RBC). The C-index (0.704 and 0.71) and 1-, 3-, and 5-year AUCs (0.726, 0.800, 0.780, and 0.752, 0.761, 0.760) of the training and validation cohorts proved the excellent predictive performance of the nomogram. Calibration curves the DCA curves showed that the nomogram had good consistency and clinical utility. There were apparent variances in RFS between the low-risk, intermediate-risk, and high-risk groups (P<0.0001).

Conclusion

S-S patients who underwent ablation had the best prognosis. The nomogram developed and validated in the study had good predictive ability for S-S patients.

Radiomics model based on contrast-enhanced computed tomography imaging for early recurrence monitoring after radical resection of AFP-negative hepatocellular carcinoma

BACKGROUND

Although radical surgical resection is the most effective treatment for hepatocellular carcinoma (HCC), the high rate of postoperative recurrence remains a major challenge, especially in patients with alpha-fetoprotein (AFP)-negative HCC who lack effective biomarkers for postoperative recurrence surveillance. Emerging radiomics can reveal subtle structural changes in tumors by analyzing preoperative contrast-enhanced computer tomography (CECT) imaging data and may provide new ways to predict early recurrence (recurrence within 2 years) in AFP-negative HCC. In this study, we propose to develop a radiomics model based on preoperative CECT to predict the risk of early recurrence after surgery in AFP-negative HCC.

PATIENTS AND METHODS

Patients with AFP-negative HCC who underwent radical resection were included in this study. A computerized tool was used to extract radiomic features from the tumor region of interest (ROI), select the best radiographic features associated with patient's postoperative recurrence, and use them to construct the radiomics score (RadScore), which was then combined with clinical and follow-up information to comprehensively evaluate the reliability of the model.

RESULTS

A total of 148 patients with AFP-negative HCC were enrolled in this study, and 1,977 radiographic features were extracted from CECT, 2 of which were the features most associated with recurrence in AFP-negative HCC. They had good predictive ability in both the training and validation cohorts, with an area under the ROC curve (AUC) of 0.709 and 0.764, respectively. Tumor number, microvascular invasion (MVI), AGPR and radiomic features were independent risk factors for early postoperative recurrence in patients with AFP-negative HCC. The AUCs of the integrated model in the training and validation cohorts were 0.793 and 0.791, respectively. The integrated model possessed the clinical value of predicting early postoperative recurrence in patients with AFP-negative HCC according to decision curve analysis, which allowed the classification of patients into subgroups of high-risk and low-risk for early recurrence.

CONCLUSION

The nomogram constructed by combining clinical and imaging features has favorable performance in predicting the probability of early postoperative recurrence in AFP-negative HCC patients, which can help optimize the therapeutic decision-making and prognostic assessment of AFP-negative HCC patients.

The Prognostic Value of Alpha-Fetoprotein Ratio in Patients With Resectable Alpha-Fetoprotein-Negative Hepatocellular Carcinoma

PURPOSE

This study aimed to investigate the prognostic value of alpha-fetoprotein (AFP) ratio in patients with AFP-negative hepatocellular carcinoma (HCC).

PATIENTS AND METHODS

We retrospectively analyzed 600 AFP-negative HCC patients who underwent hepatectomy. The AFP ratio was calculated as the ratio of AFP level 1 week before surgery to the level 20-40 days after hepatectomy. Immunohistochemistry assay was used to assess protein expression in HCC tissue. The primary outcome measures were overall survival (OS) and disease-free survival (DFS).

RESULTS

The study found that a cutoff value of 1.6 ng/ml for AFP ratio, determined using X-tile software, was optimal for predicting prognosis. Patients with a high AFP ratio had a worse prognosis compare to those with a low AFP ratio (DFS, P = .026; OS, P = .034). Patients with a high AFP ratio had a worse prognosis compared to those with a low AFP ratio. Multivariate analysis revealed that AFP ratio >1.6, negative HepPar-1 expression, and vascular invasion were independent predictors of both DFS and OS. Vascular invasion had a higher area under the curve (AUC) than AFP ratio and HepPar-1 expression in predicting recurrence and death. The combination of AFP ratio, HepPar-1 expression, and vascular invasion provided better predictive accuracy for DFS and OS.

CONCLUSION

The AFP ratio is a potential prognostic marker for AFP-negative HCC patients after hepatectomy. Combining the analysis of AFP ratio with HepPar-1 expression and vascular invasion can enhance the accuracy of predicting prognosis in these patients.

DGPRI, a new liver fibrosis assessment index, predicts recurrence of AFP-negative hepatocellular carcinoma after hepatic resection: a single-center retrospective study

Although patients with alpha-fetoprotein-negative hepatocellular carcinoma (AFPNHCC) have a favorable prognosis, a high risk of postoperative recurrence remains. We developed and validated a novel liver fibrosis assessment index, the direct bilirubin-gamma-glutamyl transpeptidase-to-platelet ratio (DGPRI). DGPRI was calculated for each of the 378 patients with AFPNHCC who underwent hepatic resection. The patients were divided into high- and low-score groups using the optimal cutoff value. The Lasso-Cox method was used to identify the characteristics of postoperative recurrence, followed by multivariate Cox regression analysis to determine the independent risk factors associated with recurrence. A nomogram model incorporating the DGPRI was developed and validated. High DGPRI was identified as an independent risk factor (hazard ratio = 2.086) for postoperative recurrence in patients with AFPNHCC. DGPRI exhibited better predictive ability for recurrence 1-5 years after surgery than direct bilirubin and the gamma-glutamyl transpeptidase-to-platelet ratio. The DGPRI-nomogram model demonstrated good predictive ability, with a C-index of 0.674 (95% CI 0.621-0.727). The calibration curves and clinical decision analysis demonstrated its clinical utility. The DGPRI nomogram model performed better than the TNM and BCLC staging systems for predicting recurrence-free survival. DGPRI is a novel and effective predictor of postoperative recurrence in patients with AFPNHCC and provides a superior assessment of preoperative liver fibrosis.

Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers

BACKGROUND

Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment.

AIM

To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis.

METHODS

Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms.

RESULTS

This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively.

CONCLUSION

A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.

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

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.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND

Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.

AIM

To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.

METHODS

The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice.

RESULTS

Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.

CONCLUSION

The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction

This study aimed to develop a noninvasive predictive model for identifying early postoperative recurrence of hepatocellular carcinoma (within 2 years after surgery) based on contrast-enhanced ultrasound and serum biomarkers. Additionally, the model's validity was assessedthrough internal and external validation. Clinical data were collected from patients who underwent liver resection at the First Hospital of Quanzhou and Mengchao Hepatobiliary Hospital. The data included general information, contrast-enhanced ultrasound parameters, Liver Imaging Reporting and Data System (LI-RADS) classification, and serum biomarkers. The data from Mengchao Hospital were divided into 2 groups, with a ratio of 6:4, to form the modeling and internal validation sets, respectively. On the other hand, the data from the First Hospital of Quanzhou served as the external validation group. The developed model was named the Hepatocellular Carcinoma Early Recurrence (HCC-ER) prediction model. The predictive efficiency of the HCC-ER model was compared with other established models. The baseline characteristics were found to be well-balanced across the modeling, internal validation, and external validation groups. Among the independent risk factors identified for early recurrence, LI-RADS classification, alpha-fetoprotein, and tumor maximum diameter exhibited hazard ratios of 1.352, 1.337, and 1.135 respectively. Regarding predictive accuracy, the HCC-ER, Tumour-Node-Metastasis, Barcelona Clinic Liver Cancer, and China Liver Cancer models demonstrated prediction errors of 0.196, 0.204, 0.201, and 0.200 in the modeling group; 0.215, 0.215, 0.218, and 0.212 in the internal validation group; 0.210, 0.215, 0.216, and 0.221 in the external validation group. Using the HCC-ER model, risk scores were calculated for all patients, and a cutoff value of 50 was selected. This cutoff effectively distinguished the high-risk recurrence group from the low-risk recurrence group in the modeling, internal validation, and external validation groups. However, the calibration curve of the predictive model slightly overestimated the risk of recurrence. The HCC-ER model developed in this study demonstrated high accuracy in predicting early recurrence within 2 years after hepatectomy. It provides valuable information for developing precise treatment strategies in clinical practice and holds considerable promise for further clinical implementation.

Development and validation of a nomogram to predict the recurrence of hepatocellular carcinoma patients with dynamic changes in AFP undergoing locoregional treatments

Background

Serum alpha-fetoprotein (AFP) is an important clinical indicator for screening, diagnosis, and prognosis of primary hepatocellular carcinoma (HCC). Our team's previous study showed that patients with negative AFP at baseline and positive AFP at relapse had a worse prognosis (N-P). Therefore, the aim of our study was to develop and validate a nomogram for this group of patients.

Methods

A total of 513 patients with HCC who received locoregional treatments at Beijing You'an Hospital, Capital Medical University, from January 2012 to December 2019 were prospectively enrolled. Patients admitted from 2012 to 2015 were assigned to the training cohort (n = 335), while 2016 to 2019 were in the validation cohort (n =183). The clinical and pathological features of patients were collected, and independent risk factors were identified using univariate and multivariate Cox regression analysis as a basis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts.

Results

The content of the nomogram includes gender, tumor number, tumor size, lymphocyte, direct bilirubin (DBIL), gamma-glutamyl transferase (GGT), and prealbumin. The C-index (0.717 and 0.752) and 1-, 3-, and 5-year AUCs (0.721, 0.825, 0.845, and 0.740, 0.868, 0.837) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classify of patients with dynamic changes in AFP into three groups according to the risk of recurrence: low risk, intermediate risk, and high risk. There was a statistically significant difference in RFS between the three groups in the training and validation cohorts (P<0.001).

Conclusion

The nomogram developed and validated in this study had good predictive power for patients with dynamic changes in AFP.

A new model based on gamma-glutamyl transpeptidase to lymphocyte ratio and systemic immune-inflammation index can effectively predict the recurrence of hepatocellular carcinoma after liver transplantation

Background

Liver transplantation (LT) is one of the most effective treatment modalities for hepatocellular carcinoma (HCC), but patients with HCC recurrence after LT always have poor prognosis. This study aimed to evaluate the predictive value of the gamma-glutamyl transpeptidase-to-lymphocyte ratio (GLR) and systemic immune-inflammation index (SII) in terms of HCC recurrence after LT, based on which we developed a more effective predictive model.

Methods

The clinical data of 325 HCC patients who had undergone LT were collected and analyzed retrospectively. The patients were randomly divided into a development cohort (n = 215) and a validation cohort (n = 110). Cox regression analysis was used to screen the independent risk factors affecting postoperative recurrence in the development cohort, and a predictive model was established based on the results of the multivariate analysis. The predictive values of GLR, SII and the model were evaluated by receiver operating characteristic (ROC) curve analysis, which determined the cut-off value for indicating patients' risk levels. The Kaplan-Meier survival analysis and the competing-risk regression analysis were used to evaluate the predictive performance of the model, and the effectiveness of the model was verified further in the validation cohort.

Results

The recurrence-free survival of HCC patients after LT with high GLR and SII was significantly worse than that of patients with low GLR and SII (P<0.001). Multivariate Cox regression analysis identified GLR (HR:3.405; 95%CI:1.954-5.936; P<0.001), SII (HR: 2.285; 95%CI: 1.304-4.003; P=0.004), tumor number (HR:2.368; 95%CI:1.305-4.298; P=0.005), maximum tumor diameter (HR:1.906; 95%CI:1.121-3.242; P=0.017), alpha-fetoprotein level (HR:2.492; 95%CI:1.418-4.380; P=0.002) as independent risk factors for HCC recurrence after LT. The predictive model based on these risk factors had a good predictive performance in both the development and validation cohorts (area under the ROC curve=0.800, 0.791, respectively), and the performance of the new model was significantly better than that of single GLR and SII calculations (P<0.001). Survival analysis and competing-risk regression analysis showed that the predictive model could distinguish patients with varying levels of recurrence risk in both the development and validation cohorts.

Conclusions

The GLR and SII are effective indicators for evaluating HCC recurrence after LT. The predictive model based on these indicators can accurately predict HCC recurrence after LT and is expected to guide preoperative patient selection and postoperative follow-up.

Diagnostic and Prognostic Nomograms for Hepatocellular Carcinoma Based on PIVKA-II and Serum Biomarkers

BACKGROUND

The aim of the present study was to develop an improved diagnostic and prognostic model for HBV-associated HCC by combining AFP with PIVKA-II and other potential serum/plasma protein biomarkers.

METHODS

A total of 578 patients, including 352 patients with HBV-related HCC, 102 patients with HBV-associated liver cirrhosis (LC), 124 patients with chronic HBV, and 127 healthy subjects (HS), were enrolled in the study. The serum levels of AFP, PIVKA-II, and other laboratory parameters were collected. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors, respectively. The diagnostic efficacy of the nomogram was evaluated using receiver operator curve (ROC) analysis and the prognostic performance was measured by Harrell's concordance index (C-index).

RESULTS

AFP and PIVKA-II levels were significantly increased in HBV-related HCC, compared with those in HBV-associated LC and chronic HBV participants (p < 0.05 and p < 0.001, respectively). The diagnostic nomogram, which included age, gender, AFP, PIVKA-II, prothrombin time (PT), and total protein (TP), discriminated patients with HBV-HCC from those with HBV-LC or chronic HBV with an AUC of 0.970. In addition, based on the univariate and multivariate Cox regression analysis, PIVKA-II, γ-glutamyl transpeptidase, and albumin were found to be significantly associated with the prognosis of HBV-related HCC and were incorporated into a nomogram. The C-index of the nomogram for predicting 3-year survival in the training and validation groups was 0.75 and 0.78, respectively. The calibration curves for the probability of 3-year OS showed good agreement between the nomogram prediction and the actual observation in the training and the validation groups. Furthermore, the nomogram had a higher C-index (0.74) than that of the Child-Pugh grade (0.62), the albumin-bilirubin (ALBI) score (0.64), and Barcelona Clinic Liver Cancer (0.56) in all follow-up cases.

CONCLUSION

Our study suggests that the nomograms based on AFP, PIVKA-II, and potential serum protein biomarkers showed a better performance in the diagnosis and prognosis of HCC, which may help to guide therapeutic strategies and assess the prognosis of HCC.

Utility of gamma-glutamyl transpeptidase to lymphocyte count ratio in predicting prognosis of patients with oral cancer: A prospective cohort study in Southeastern China

BACKGROUND

To assess the prognostic role of gamma-glutamyl transpeptidase to lymphocyte count ratio (GLR) and develop a prognostic nomogram for patients with oral cancer.

METHODS

A prospective cohort (n = 1011) was conducted during July 2002 to March 2021 in Southeastern China.

RESULTS

The median follow-up time was 3.5 years. Multivariate Cox regression (OS: HR = 1.51, 95% CI: 1.04, 2.18) and Fine-Gray model (DSS: HR = 1.68, 95% CI: 1.14, 2.49) both showed that high GLR could act as an indicator of poor prognosis. A nonlinear dose-response relationship was observed between continuous GLR and the risk of all-cause mortality (p for overall = 0.028, p for nonlinear = 0.048). Compare with TNM stage, time-dependent ROC curve proved that GLR-based nomogram model performs better in predicting prognosis (the area under curve for 1-, 3-, and 5-years mortality: 0.63, 0.65, and 0.64 vs. 0.76, 0.77, and 0.78, p < 0.001).

CONCLUSION

GLR might be a useful tool in predicting prognosis for patients with oral cancer.

Novel Nomograms Based on Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio Predict Prognosis of Hepatocellular Carcinoma Patients After Hepatectomy

Background

The prediction of prognosis of hepatocellular carcinoma (HCC) is of great significance in improving disease outcome and optimizing clinical management, while reliable prognostic indicators are lacking. This study was conducted to develop readily-to-use nomograms for prognosis prediction of HCC after hepatectomy.

Materials and Methods

Data of eligible patients were collected and analyzed retrospectively. Independent prognostic factors were identified by Cox regression, and nomograms for the prediction of disease-free survival (DFS) and overall survival (OS) were developed. The performance of the nomograms was evaluated by receiver operating characteristics (ROC) curves, C-indexes and calibration curves and was verified by the validation cohort. The predictive value of the nomograms was also compared with the 8th edition of American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) and the Barcelona Clinic Liver Cancer (BCLC) staging systems.

Results

In total, 599 patients were enrolled in the analysis: 420 in the training cohort and 179 in the validation cohort. The optimal cut-off value of Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio (GLR) was 19.5. GLR contributed significantly to the nomograms with good predictive power. In ROC analyses, the areas under curve (AUCs) of the nomograms for 1-, 3- and 5-year DFS and OS prediction were 0.758, 0.756, 0.734 and 0.810, 0.799, 0.758, respectively. The C-indexes of the DFS nomogram were 0.697 (95% CI 0.665-0.729) in the training cohort and 0.710 (95% CI 0.664-0.756) in the validation cohort. For OS prediction, the C-indexes were 0.741 (95% CI 0.704-0.778) and 0.758 (95% CI 0.705-0.811) in the training and validation cohorts, respectively. The calibration curves demonstrated satisfactory agreement between nomogram predictions and actual observations. The nomograms demonstrated superior predictive performance to the TNM and the BCLC staging systems.

Conclusion

Our novel nomograms showed adequate performance in the prediction of HCC prognosis after hepatectomy, which may facilitate the risk stratification and individualized management of HCC patients.

Nomogram Based on Platelet-Albumin-Bilirubin for Predicting Tumor Recurrence After Surgery in Alpha-Fetoprotein-Negative Hepatocellular Carcinoma Patients

Purpose

In this study, we developed a nomogram based on the platelet-albumin-bilirubin (PALBI) score to predict recurrence-free survival (RFS) after curative resection in alpha-fetoprotein (AFP)-negative (≤20 ng/mL) hepatocellular carcinoma (HCC) patients.

Patients and Methods

A total of 194 pathologically confirmed AFP-negative HCC patients were retrospectively analyzed. Univariate and multivariate Cox regression analyses were performed to screen the independent risk factors associated with RFS, and a nomogram prediction model for RFS was established according to the independent risk factors. The receiver operating characteristic (ROC) curve and the C-index were used to evaluate the accuracy and the efficacy of the model prediction. The correction curve was used to assess the calibration of the prediction model, and decision curve analysis was performed to evaluate the clinical application value of the prediction model.

Results

PALBI score, MVI, and tumor size were independent risk factors for postoperative tumor recurrence (P < 0.05). A nomogram prediction model based on the independent predictive factors was developed to predict RFS, and it achieved a good C-index of 0.704 with an area under the ROC curve of 0.661 and the sensitivity was 73.2%. Patients with AFP-negative HCC could be divided into the high-risk group or the low-risk group by the risk score calculated by the nomogram, and there was a significant difference in RFS between the two groups (P < 0.05). Decision curve analysis (DCA) showed that the nomogram increased the net benefit in predicting the recurrence of AFP-negative HCC and exhibited a wider range of threshold probabilities than the independent risk factors (PALBI score, MVI, and tumor size) by risk stratification.

Conclusion

The nomogram based on the PALBI score can predict RFS after curative resection in AFP-negative HCC patients and can help clinicians to screen out high-risk patients for early intervention.

Preoperative prognostic value of alfa-fetoprotein density in patients with hepatocellular carcinoma undergoing radiofrequency ablation

OBJECTIVES

To examine the prognostic value of preoperative alfa-fetoprotein (AFP) density and other clinical factors in patients undergoing percutaneous radiofrequency ablation (RFA) of hepatocellular carcinoma (HCC).

METHODS

From January 2010 to December 2018, a total of 543 patients undergoing RFA for HCC meeting the Milan criteria were included at our institution. AFP density was calculated as absolute AFP pre-ablation divided by the total volume of all HCC lesions. The survival rates according to AFP density were estimated using the Kaplan-Meier method and compared using the log-rank test. Univariate and multivariate Cox proportional-hazards regression analyses were used to assess predictors of overall survival (OS) and progression-free survival (PFS).

RESULTS

The Kaplan-Meier 1-, 3-, and 5-year OS rates were 98.8%, 88.5%, and 70.4%, respectively, for the low AFP density group, and 98.3%, 74.9%, and 49.4%, respectively, for the high AFP density group. The corresponding PFS rates were 78.9%, 56.7%, and 40.9% (low AFP density group), and 63.6%, 40.8%, and 27.5% (high AFP density group). High AFP density was associated with significantly reduced PFS and OS (both p < 0.001). Multivariate analysis suggested that AFP density was a predictor of OS and PFS.

CONCLUSIONS

Serum AFP density may serve as a promising predictor of survival in patients with HCC undergoing RFA. High AFP density could identify patients who might be prone to recurrence or progression and need close surveillance.

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

Objective

The aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.

Methods

A total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process.

Results

Prostate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.

Conclusion

The RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I-II: a retrospective cohort study

Background

High recurrence rate was a major factor for the poor postoperative prognosis of hepatocellular carcinoma (HCC) patients. The present study was intended to evaluate the association of gamma-glutamyl transpeptidase to lymphocyte count ratio (GLR) and the recurrence of HCC with staging I–II in Chinese.

Methods

The retrospective cohort data was derived from the First Affiliated Hospital of Zhengzhou University from January 2014 to December 2018 on 496 patients who underwent radical resection of HCC with staging I–II. Multivariable Cox regression models were used to determine hazard ratios (HR) and 95% confidence interval (CI) for the recurrence of HCC with staging I–II of each GLR tertile category. The restricted cubic spline model was used to find out the threshold effect.

Results

With the low tertile of GLR as the reference, multivariable-adjusted HR and 95% CI of the middle and high tertile categories were 1.748 (1.170–2.612) and 2.078 (1.339–3.227). In addition, there was a positive correlation (HR 1.002; 95% CI 1.001–1.004) and a non-liner relationship was found, whose point was 27.5. When the GLR was less than 27.5, the risk of recurrence increased, obviously with the increase in GLR levels (HR 1.041; 95% CI 1.014–1.068).

Conclusions

The GLR was independently associated with the recurrence of HCC patients with staging I–II. Furthermore, the relationship was positive and no-linear.

The Combination of Age, International Standardized Ratio, Albumin and γ-Glutamyl Transpeptidase (AIAG), Tumor Size and Alpha Fetoprotein (AFP) Stage as the Prognostic Model for Hepatitis B-Related Hepatocellular Carcinoma

Background

Advanced liver fibrosis can lead to cirrhosis, portal hypertension and liver failure. Besides, advanced liver fibrosis and cirrhosis are the major risk factors for hepatocellular carcinoma (HCC). Almost all patients with HCC also have liver cirrhosis. This study aims to predict the survival rate of hepatitis B-related hepatocellular carcinoma (HCC) by age, international standardized ratio, albumin and γ-glutamyl transpeptidase (AIAG), an indicator measuring the degree of cirrhosis.

Methods

A total of 501 hepatitis B-related HCC patients experiencing radical surgery were analyzed, retrospectively. General data about demographics and labs were collected at the date of diagnosis to calculate AIAG [age, international standardized ratio (INR), albumin and gamma-glutamyl transferase (GGT)]. The Kaplan–Meier curves and Cox analysis were used to evaluate overall survival (OS) and recurrence-free survival (RFS). The C-index was calculated in R software (version 4.0.3) to evaluate the accuracy of the prognostic model.

Results

During a median follow-up period of 30 months, 31.1% (156/501) of the patients died, and 34.3% (172/501) experienced the recurrence of HCC. Compared with patients with lower AIAG score, patients with higher AIAG score had higher Child-Pugh grade and were at higher Barcelona Clinic Liver Cancer (BCLC) stage (both P<0.05). Multivariate analysis suggested that GGT, alpha fetoprotein (AFP), tumor size, BCLC stage and AIAG grade were independent predictors of OS and RFS. Furthermore, the combined use of tumor size, AFP and AIAG stage could predict survival significantly better (C-index=0.710, 95% CI: 0.669–0.751) than BCLC stage.

Conclusion

AIAG is significantly associated with survival of HCC patients, and provides additional prognostic information for patients with HCC. Our findings suggest that the combination of AIAG, tumor size and AFP stage has a better predictive value for the prognosis of patients with hepatitis B-related hepatocellular carcinoma. However, it is necessary for more external evidences to determine clinical utility.

Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection

Background

Since it’s a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients.

Methods

The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI).

Results

The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort.

Conclusions

GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08565-2.