Nomograms in Hepatectomy Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma

Development and Validation of a Prognostic Model for Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma Based on Preoperative Serum Prealbumin

Purpose

We aimed to develop a prognostic nomogram utilizing preoperative serum prealbumin levels to predict the overall survival (OS) in patients undergoing transarterial chemoembolization (TACE) for unresectable hepatocellular carcinoma (HCC).

Patients and Methods

A total of 768 individuals with unresectable HCC who underwent TACE at three medical facilities in Suzhou between January 2007 December 2018 were included. The patient cohort was assigned to a training set (n = 461) and a validation set (n = 307). Cox regression analysis identified independent prognostic factors, which were then used to construct a prognostic nomogram. Internal validation was performed in the testing group, and its effectiveness and capability were evaluated with reference to the concordance index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

Independent risk factors identified through Cox regression analyses included the BCLC stage, cirrhosis, invasion, tumor number, preoperative serum PALB, performance status (PS), and tumor size. The nomogram demonstrated a C-index of 0.734 (95% confidence interval (CI): 0.710-0.758) in the training set and 0.717 (95% CI: 0.678-0.756) in the validation set, indicating strong discriminatory ability. The nomogram also demonstrated favorable discriminatory performance with AUC values of 0.873, 0.820, and 0.833 for 1-, 2-, and 3-year OS, respectively, in the training set, and 0.854, 0.765, and 0.724 in the validation set. The AUC value of the nomogram (0.843) was significantly higher than that of the four conventional staging systems. Moreover, calibration graphs confirmed a strong concordance between the predicted and observed results. Furthermore, DCA underscored the significant clinical utility of the nomogram. Additionally, the low-risk group exhibited considerably superior rates of survival compared to the high-risk group.

Conclusion

The developed nomogram demonstrated excellent prognostic capability, which served as a valuable tool for personalized clinical decision-making for patients with HCC.

Development of preoperative prognostic models including radiological features for survival of singular nodular HCC patients

BACKGROUND

Early singular nodular hepatocellular carcinoma (HCC) is an ideal surgical indication in clinical practice. However, almost half of the patients have tumor recurrence, and there is no reliable prognostic prediction tool. Besides, it is unclear whether preoperative neoadjuvant therapy is necessary for patients with early singular nodular HCC and which patient needs it. It is critical to identify the patients with high risk of recurrence and to treat these patients preoperatively with neoadjuvant therapy and thus, to improve the outcomes of these patients. The present study aimed to develop two prognostic models to preoperatively predict the recurrence-free survival (RFS) and overall survival (OS) in patients with singular nodular HCC by integrating the clinical data and radiological features.

METHODS

We retrospective recruited 211 patients with singular nodular HCC from December 2009 to January 2019 at Eastern Hepatobiliary Surgery Hospital (EHBH). They all met the surgical indications and underwent radical resection. We randomly divided the patients into the training cohort (n =132) and the validation cohort (n = 79). We established and validated multivariate Cox proportional hazard models by the preoperative clinicopathologic factors and radiological features for association with RFS and OS. By analyzing the receiver operating characteristic (ROC) curve, the discrimination accuracy of the models was compared with that of the traditional predictive models.

RESULTS

Our RFS model was based on HBV-DNA score, cirrhosis, tumor diameter and tumor capsule in imaging. RFS nomogram had fine calibration and discrimination capabilities, with a C-index of 0.74 (95% CI: 0.68-0.80). The OS nomogram, based on cirrhosis, tumor diameter and tumor capsule in imaging, had fine calibration and discrimination capabilities, with a C-index of 0.81 (95% CI: 0.74-0.87). The area under the receiver operating characteristic curve (AUC) of our model was larger than that of traditional liver cancer staging system, Korea model and Nomograms in Hepatectomy Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma, indicating better discrimination capability. According to the models, we fitted the linear prediction equations. These results were validated in the validation cohort.

CONCLUSIONS

Compared with previous radiography model, the new-developed predictive model was concise and applicable to predict the postoperative survival of patients with singular nodular HCC. Our models may preoperatively identify patients with high risk of recurrence. These patients may benefit from neoadjuvant therapy which may improve the patients' outcomes.

Fibrosis-4 index, a predictor for prognosis of hepatocellular carcinoma patients after curative hepatectomy even in hepatitis B virus dominant populations

Purpose

Liver fibrosis plays an important role in the development of hepatocellular carcinoma (HCC) and determining its prognosis. Although many staging systems and liver reserve models have been developed without the intention of predicting prognosis of HCC, some studies have investigated their prognostic values in HCC after curative liver resection (LR). The aim of this study is to evaluate prognostic value of non-invasive biomarkers after curative LR.

Methods

Between 2006 and 2013, HCC patients underwent LR were included and total 962 patients were enrolled. All non-invasive biomarkers (fibrosis 4 index (FIB-4), aspartate aminotransferase-to-platelet ratio index (APRI), aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), AAR-to-platelet ratio index (AARPRI), and albumin-bilirubin (ALBI) score) were measured at the time of HCC diagnosis. To binarize each biomarker, an optimal cut-off value for fibrosis stage was selected using the value of minimum distance from the left-upper corner of the receiver operating characteristic curve with a specificity >60%. We performed Cox regression analysis on 2-year recurrence-free survival (RFS) and overall survival (OS).

Results

The area under curve values for FIB-4 and APRI were the largest for fibrosis stage compared to other biomarkers, 0.669 (95% confidential interval (CI), 0.610-0.719) and 0.748 (95% CI, 0.692-0.800), respectively. Between those two indices, FIB-4 is considered a statistically significant prognostic factor of RFS in HCC patients after LR. The HR for 2-year RFS and OS were 1.81 (95% CI, 1.18-2.77; P = 0.007) and 2.36 (95% CI, 0.99-5.65; P = 0.054), respectively.

Conclusion

FIB-4 is identified as a statistically significant predictor of HCC prognosis after curative LR even in HBV dominant populations.

A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase

BACKGROUND

Recurrence is a major risk factor affecting the postoperative survival of patients with hepatocellular carcinoma (HCC), especially those with high preoperative serum γ-glutamyl transpeptidase (GGT) levels. This study had the aim of developing a personalized predictive tool to accurately determine the risk of postoperative recurrence of hepatitis B-virus (HBV)-related HCC in patients with high preoperative serum GGT levels.

METHODS

Patients who underwent curative liver resection of HBV-related HCC and had high preoperative GGT levels were consecutively enrolled between 2008 and 2011. Prognostic indicators for recurrence were determined using Cox regression analysis. A nomogram was then developed and assessed by integrating the independent risk factors into the model.

RESULTS

A total of 603 eligible patients were included. The final nomogram for predicting HCC recurrence in patients with high preoperative GGT levels consisted of five independent prognostic factors: α-fetoprotein (AFP), HBV-DNA, satellite nodules, microvascular invasion, and tumor grade. The C-index of the nomogram for predicting recurrence was 0.759, and validation showed high accuracy and discriminatory.

CONCLUSIONS

The predictive nomogram developed and validated in this study performs well in predicting postoperative recurrence of HBV-related HCC in patients with high preoperative GGT levels. It can provide personalized assessments to inform the development of surveillance strategies and allows patients with a high risk of recurrence to be selected for further adjuvant treatment.

Progression of Prothrombin Induced by Vitamin K Absence-II in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of cancer-related death worldwide. Due to the lack of efficient tools for early detection, asymptomatic HCC patients are diagnosed at an advanced stage, leading to a poor prognosis. To improve survival, serum biomarker prothrombin induced by vitamin K absence-II (PIVKA-II) was under investigation. PIVKA-II is an abnormal protein produced in HCC. The coagulation function was insufficient due to the lack of Gla residues. Elevated PIVKA-II was associated with bad tumor behavior in terms of proliferation, metastasis, and invasion. Three major signaling pathways were proposed to clarify the mechanism. With the advantages including affordability, minimal invasiveness, convenience, and efficiency, PIVKA-II could improve HCC management consisting of four aspects. First, PIVKA-II was an effective and dynamic tool for improving HCC surveillance in high-risk population. Changes in the serum levels of PIVKA-II provided valuable molecular alteration information before imaging discovery. Second, PIVKA-II offered a complementary approach for HCC early detection. Compared to traditional diagnostic approaches, the combination of PIVKA-II and other biomarkers had better performance. Third, PIVKA-II was an indicator for the assessment of response to treatment in HCC. Preoperative assessment was for selecting personalized therapy, and postoperative measurement was for assessing treatment efficacy. Fourth, PIVKA-II was considered as a prognostic predictor for HCC. Patients with elevated PIVKA-II were more likely to develop microvascular invasion, metastasis, and recurrence.

Preoperative Prediction of Cytokeratin 19 Expression for Hepatocellular Carcinoma with Deep Learning Radiomics Based on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging

Purpose

Cytokeratin 19 (CK19) expression is a proven independent prognostic predictor of hepatocellular carcinoma (HCC). This study aimed to develop and validate the performance of a deep learning radiomics (DLR) model for CK19 identification in HCC based on preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI).

Patients and Methods

A total of 141 surgically confirmed HCCs with preoperative gadoxetic acid-enhanced MRI from two institutions were included. Prediction models were established based on hepatobiliary phase (HBP) images using a training set (n=102) and validated using time-independent (n=19) and external (n=20) test sets. A receiver operating characteristic curve was used to evaluate the performance for CK19 prediction. Recurrence-free survival (RFS) was also analyzed by incorporating the CK19 expression and other factors.

Results

For predicting CK19 expression, the area under the curve (AUC) of the DLR model was 0.820 (95% confidence interval [CI]: 0.732–0.907, P<0.001) with sensitivity, specificity, accuracy of 0.800, 0.766, and 0.775, respectively, and reached 0.781 in the external test set. Combined with alpha fetoprotein, the AUC increased to 0.833 (95% CI: 0.753–0.912, P<0.001) and the sensitivity was 0.960. Intratumoral hemorrhage and peritumoral hypointensity on HBP were independent risk factors for HCC recurrence by multivariate analysis. Based on predicted CK19 expression and the independent risk factors, a nomogram was developed to predict RFS and achieved C-index of 0.707.

Conclusion

This study successfully established and verified an optimal DLR model for preoperative prediction of CK19-positive HCCs based on gadoxetic acid-enhanced MRI. The prediction of CK19 expression in HCC using a non-invasive method can help inform preoperative planning.

Development of a preoperative prognostic scoring system to predict benefits of hepatic resection in advanced hepatocellular carcinoma patients

OBJECTIVE

The present study aimed to identify risk factors for overall survival in advanced hepatocellular carcinoma (HCC) patients and establish a scoring system to select patients who would benefit from hepatic resection.

METHODS

Survival curves were analyzed using the Kaplan-Meier method and log-rank test. The prognostic scoring system was developed from training cohort using a Cox-regression model and validated in a external validation cohort Results: There were 401 patients in the training cohort, 163 patients in the external validation cohorts. The training cohort median survival in all patients was 12 ± 1.07 months, rate of overall survival was 49.6% at 1 year, 25.0% at 3 years, and 18.0% at 5 years. A prognostic scoring system was established based on age, body mass index, alkaline phosphatase, tumor number and tumor capsule. Patients were classified as low- risk group(≤3.5) or high-risk group(>3.5). High-risk patients had a median survival of 9 months, compared with 23 months in low-risk patients. The area under the receiver operating characteristic curve (AUC) of the prognostic scoring system was 0.747 (0.694-0.801), which is significantly better than AFP, Child-Pugh and ALBI. The AUC of validation cohorts was 0.716 (0.63-0.803).

CONCLUSION

A prognostic scoring system for hepatic resection in advanced HCC patients has been developed based entirely on preoperative variables. Patients classified as low risk using this system may experience better prognosis after hepatic resection.

Prognostic Nomograms for Patients with Hepatocellular Carcinoma After Curative Hepatectomy, with a Focus on Recurrence Timing and Post-Recurrence Management

Background

Prognoses of patients with hepatocellular carcinoma (HCC) after curative hepatectomy remain unsatisfactory because of the high incidence of postoperative recurrence. Published predictive systems focus on pre-resection oncological characteristics, ignoring post-recurrence factors.

Purpose

This study aimed to develop prognostic nomograms for 3- and 5-year overall survival (OS) of patients with HCC after curative hepatectomy, focusing on potentially influential post-recurrence factors.

Patients and Methods

Clinicopathological and postoperative follow-up data were extracted from 494 patients with HCC who underwent curative hepatectomy between January 2012 and June 2019. Early recurrence (ER) and late recurrence (LR) were defined as recurrence at ≤2 and >2 years, respectively, after curative hepatectomy. Nomograms for the prediction of 3- and 5-year OS were established based on multivariate analysis. The areas under time-dependent receiver operating characteristic curves (AUCs) for the nomograms were calculated independently to verify predictive accuracy. The nomograms were internally validated based on 2000 bootstrap resampling of 75% of the original data.

Results

In total, 494 patients with HCC who underwent curative hepatectomy met the eligibility criteria. Cox proportional hazard regression analysis identified factors potentially influencing 3- and 5-year OS. Multivariate analysis indicated that patient age, Hong Kong Liver Cancer stage, γ-glutamyl transferase (γ-GGT) level, METAVIR inflammation activity grade, ER and post-recurrence treatment modality were influencing factors for 3-year OS (AUC, 0.891; 95% CI, 0.8364-0.9447). γ-GGT > 60 U/L, hepatectomy extent, LR and post-recurrence treatment modality were influencing factors for 5-year OS (AUC, 0.864; 95% CI, 0.8041-0.9237). Calibration plots showed satisfactory concordance between the predicted and actual observation cohorts.

Conclusion

We propose new prognostic nomograms for OS prediction with a focus on the differentiation of recurrence timing and post-recurrence management. These nomograms overcome the shortcomings of previous predictive nomograms and significantly improve predictive accuracy.

Predictive value of preoperative and postoperative peripheral lymphocyte difference in hepatitis B virus-related hepatocellular cancer patients: Based on the analysis of dynamic nomogram

BACKGROUND

Inflammation plays an important role in the progression and prognosis of hepatocellular carcinoma (HCC). Our aim is to explore the prognostic value of preoperative and postoperative peripheral lymphocyte differences and to develop a dynamic prognosis nomogram in hepatitis B virus-related HCC patients.

METHODS

Important indicators related to overall survival (OS) are screened out by Cox proportional hazard models. The receiver operating characteristic curves, decision curve analysis curves, net reclassification improvement, and integrated discrimination improvement were used to evaluate the performance of the model.

RESULTS

Lymphocyte (L) difference was an independent risk factor. It was further verified that the performance of the nomogram was significantly improved after the L difference was incorporated into the nomogram. The nomogram generated had the area under curves of 0.779, 0.775, and 0.793 at 3, 5, and 7 years after surgery, respectively. Our nomogram models showed significantly better performance in predicting the HCC prognosis compared to other models. And online webserver and scoring system table was built based on the proposed nomogram for convenient clinical use.

CONCLUSIONS

It is newly found that L difference is an effective predictor of OS, and the nomogram based on this indicator can accurately predict the prognosis of HCC patients.

Novel Models Predict Postsurgical Recurrence and Overall Survival for Patients with Hepatitis B Virus-Related Solitary Hepatocellular Carcinoma ≤10 cm and Without Portal Venous Tumor Thrombus

BACKGROUND

The predictive model of postsurgical recurrence for solitary early hepatocellular carcinoma (SE-HCC) is not well established. The aim of this study was to develop a novel model for prediction of postsurgical recurrence and survival for patients with hepatitis B virus (HBV)-related SE-HCC ≤10 cm.

PATIENTS AND METHODS

Data from 1,081 patients with HBV-related SE-HCC ≤10 cm who underwent curative liver resection from 2003 to 2016 in our center were collected retrospectively and randomly divided into the derivation cohort (n = 811) and the internal validation cohort (n = 270). Eight hundred twenty-three patients selected from another four tertiary hospitals served as the external validation cohort. Postsurgical recurrence-free survival (RFS) and overall survival (OS) predictive nomograms were generated. The discriminatory accuracies of the nomograms were compared with six conventional hepatocellular carcinoma (HCC) staging systems.

RESULTS

Tumor size, differentiation, microscopic vascular invasion, preoperative α-fetoprotein, neutrophil-to-lymphocyte ratio, albumin-to-bilirubin ratio, and blood transfusion were identified as the risk factors associated with RFS and OS. RFS and OS predictive nomograms based on these seven variables were generated. The C-index was 0.83 (95% confidence interval [CI], 0.79-0.87) for the RFS-nomogram and 0.87 (95% CI, 0.83-0.91) for the OS-nomogram. Calibration curves showed good agreement between actual observation and nomogram prediction. Both C-indices of the two nomograms were substantially higher than those of the six conventional HCC staging systems (0.54-0.74 for RFS; 0.58-0.76 for OS) and those of HCC nomograms reported in literature.

CONCLUSION

The novel nomograms were shown to be accurate at predicting postoperative recurrence and OS for patients with HBV-related SE-HCC ≤10 cm after curative liver resection.

IMPLICATIONS FOR PRACTICE

This multicenter study proposed recurrence or mortality predictive nomograms for patients with hepatitis B virus-related solitary early hepatocellular carcinoma ≤10 cm after curative liver resection. A close postsurgical surveillance protocol and adjuvant therapy should be considered for patients at high risk of recurrence.

Novel Prognostic Nomograms for Predicting Early and Late Recurrence of Hepatocellular Carcinoma After Curative Hepatectomy

Aim

Hepatectomy is the main curative method for patients with hepatocellular carcinoma (HCC) in China. Unfortunately, high recurrence rate after hepatectomy poses negative impact on the prognosis of patients. This study aimed to develop prognostic nomograms to predict early recurrence (ER) and late recurrence (LR) of HCC after curative hepatectomy.

Patients and Methods

Total of 318 HCC patients undergoing curative hepatectomy from January 2012 to January 2018 were retrospectively recruited. Potential risk factors that were significant for predicting ER and LR in univariate analysis were selected for multivariate survival model analysis using the backward stepwise method. Risk factors identified in multivariate analysis were used to develop nomograms to predict ER and LR. The nomogram was internally validated using 2,000 bootstrap samples from 75% of the original data.

Results

Among 318 patients, 164 showed postoperative recurrence, of which 140 and 24 had ER (≤2 years) and LR (>2 years), respectively. Multivariate analysis showed that age, Hong Kong Liver Cancer Stage, albumin-bilirubin, METAVIR fibrosis grade, and microvascular invasion were risk factors of ER for HCC after curative hepatectomy. The AUC of the ROC curve for ER in the development set (D-set) was 0.888 while that in the validation set (V-set) was 0.812. Neutrophil/lymphocyte ratio and glypican-3 (+) were risk factors for LR in HCC patients after curative hepatectomy. The AUC of the ROC curve for LR predictive nomogram that integrated all independent predictors was 0.831. The AUC of the ROC curve for LR in the D-set was 0.833, while that for LR in the V-set was 0.733. The C-index and AUC of ROC for the proposed nomograms were more satisfactory than three conventional HCC staging systems used in this study.

Conclusion

We developed novel nomograms to predict ER and LR of HCC patients after curative hepatectomy for clinical use to individualize follow-up and therapeutic strategies.