Nomograms Incorporating the CNLC Staging System Predict the Outcome of Hepatocellular Carcinoma After Curative Resection

Predictive Factors and Nomogram (MAP-BNP) for Post Stereotactic Body Radiotherapy Survival in Advanced Hepatocellular Carcinoma Patients

Background

Stereotactic body radiation therapy (SBRT) is a widely recognized approach for managing hepatocellular carcinoma (HCC), particularly in its advanced stages, with prognosis highly dependent on tumour burden and baseline liver function. This study aimed to develop a predictive model and nomogram that incorporates these factors to improve survival outcomes in advanced HCC patients treated with SBRT and systemic therapy.

Methods

We retrospectively reviewed records of 110 patients with advanced HCC treated with SBRT between May 2020 and April 2023. Inclusion criteria included age ≥18 years, cirrhosis, and suitability for SBRT.

Results

The median age was 63 years (range 28-84), with viral cirrhosis (40.9%) and NASH (38.2%) as the main aetiologies. At presentation, 83.6% of patients had portal vein thrombosis, 32.7% had nodal metastasis, and 50% had distant metastasis. The median tumour diameter was 9 cm, and 73.6% of patients had the multifocal disease.A median SBRT dose of 35 Gy (range 25-45 Gy) in 5 fractions was administered. Significant reductions in tumour markers were noted at three months: AFP levels dropped from a median of 309.75 ng/ml to 62 ng/ml (P = 0.015), and PIVKA II from 2230 mAU/ml to 345 mAU/ml (P = 0.001). Complete and partial responses were seen in 33% and 45% of patients, respectively. The median overall survival (OS) was 14 months (95% CI 11.7-16.2), with OS rates of 90%, 58%, and 34% at 6, 12, and 24 months. Progression-free survival (PFS) was 9 months (95% CI 6.4-11.5). Significant predictors of OS included multifocal tumour, portal vein thrombosis, lymph node involvement, serum bilirubin, serum albumin, and log PIVKA-II. The developed MAP-BNP nomogram achieved a C-index of 0.853, outperforming the Child-Turcotte-Pugh (0.62) and ALBI (0.64) scores. Patients were classified into low-risk (<200 points) and high-risk (>200 points) groups, with the low-risk group showing a significantly longer OS (P < 0.001).

Conclusion

The MAP-BNP nomogram, integrating tumour burden and liver function, provides a more individualized approach for predicting survival in advanced HCC patients treated with SBRT and systemic therapy, outperforming traditional staging systems.

Interpretable Staging Prediction of Liver Cancer Based on Joint-Knowledge Network

Clinical staging is crucial for treatment strategies and improving 5-year survival rates in hepatocellular carcinoma (HCC) patients. However, existing methods struggle to distinguish stages with highly similar textual features. Additionally, their lack of interpretability hampers their practical application in medical scenarios. Here, we introduce KnowST, a joint-knowledge network designed to leverage task relevance to explore implicit knowledge for interpretable staging prediction of liver cancer. First, the relevance of auxiliary tasks and the main task is established from two perspectives to guide the model's focus on staging-related implicit knowledge in radiology reports. Stages-to-stages: KnowST learns the inter-stage distinctions between different stages and the similarities within the same stages, using these as important references for staging differentiation. Factors-to-stages: Clinically, staging is determined by multiple tumor factors. These factors can serve as effective clues to assist KnowST in predicting the correct stage, especially in the case of confusing stages. Second, domain-specific word embeddings are introduced to bridge the gap between pre-trained language models and Chinese radiology reports. Lastly, tumor factor prediction enhances the credibility of the deep model in staging prediction, and its visualized results effectively demonstrate the model's interpretability. Overall, KnowST leverages the joint-knowledge from these two perspectives, effectively utilizing implicit information in radiology reports to achieve interpretable clinical staging. Compared to the optimal baselines, KnowST improves AUC by 7.69% and achieves 90.52% accuracy on 573 real-world radiology reports, while also demonstrating superior stage identification and stable performance across various metrics.

Consensus of Chinese Experts on Neoadjuvant and Conversion Therapies for Hepatocellular Carcinoma: 2023 Update

Hepatocellular carcinoma (HCC) is a common malignancy in China, with high recurrence rate and low resection rate among patients first diagnosed. Preoperative treatments including neoadjuvant and conversion therapy have the potential to overcome these challenges. In December 2021, Chinese expert consensus on neoadjuvant and conversion therapies for hepatocellular carcinoma was published. With the emersion of new evidence regarding the neoadjuvant and conversion therapies for HCC, the cooperative group brought together multidisciplinary researchers and scholars with experience in related fields to update the new edition (2023 Edition) for reference in China, including principle of the treatment strategies, the potential populations selection, treatment methods, multidisciplinary team, and future research for preoperative treatments. The new consensus aims to provide guidance for clinical application. Through the use of neoadjuvant therapy and conversion therapy, we can enhance the resection rate and reduce the recurrence of intermediate-to-advanced HCC patients, thereby improving survival outcomes.

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) exhibits a propensity for early recurrence following liver resection, resulting in a bleak prognosis. At present, majority of the predictive models for the early postoperative recurrence of HCC rely on the linear assumption of the Cox Proportional Hazard (CPH) model. However, the predictive efficacy of this model is constrained by the intricate nature of clinical data. The present study aims to investigate the efficacy of the random survival forest (RSF) model, which is a machine learning algorithm, in predicting the early postoperative recurrence of HCC, and compare its performance with that of the traditional CPH model. This analysis seeks to elucidate the potential advantages of the RSF model over the CPH model in addressing this clinical challenge.

METHODS

The present retrospective cohort study was conducted at a single center. After excluding 41 patients, a total of 541 patients were included in the final model construction and subsequent analysis. The patients were randomly divided into two groups at a 7:3 ratio: training group (n = 378) and validation group (n = 163). The least absolute shrinkage and selection operator (LASSO) regression was used to identify the risk factors in the training group. Then, the identified factors were used to develop the RSF and CPH regression models. The predictive ability of the model was assessed using the concordance index (C-index). The accuracy of the model predictions was evaluated using the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic curve (AUC). The clinical practicality of the model was measured by decision curve analysis (DCA), and the overall performance of the model was evaluated using the Brier score. The RSF model was visually represented using the Shapley additive explanations (SHAP) framework. Then, the RSF, CPH regression, and albumin-bilirubin (ALBI) grade models were compared.

RESULTS

The following variables were examined by LASSO regression: alpha fetoprotein (AFP), gamma-glutamyl transpeptidase to platelet ratio (GPR), blood transfusion (BT), microvascular invasion (MVI), large vessel invasion (LVI), Edmondson-Steiner (ES) grade, liver capsule invasion (LCI), satellite nodule (SN), and Barcelona clinic liver cancer (BCLC) grade. Then, a RSF model was developed using 500 trees, and the variable importance (VIMP) ranking was MVI, LCI, SN, BT, BCLC, ESG, AFP, GPR and LVI. After these aforementioned factors were applied, the RSF and CPH regression models were developed and compared using the ALBI grade model. The C-index for the RSF model (0.896 and 0.798, respectively) outperformed that of the CPH regression model (0.803 and 0.772, respectively) and ALBI grade model (0.517 and 0.515, respectively), in both the training and validation groups. Three time points were selected to assess the predictive capabilities of these models: 6, 12 and 18 months. For the training group, the AUC value for the RSF model at 6, 12 and 18 months was 0.971 (95% CI: 0.955-0.988), 0.919 (95% CI: 0.887-0.951) and 0.899 (95% CI: 0.867-0.932), respectively. For the validation cohort, the AUC value for the RSF model at 6, 12 and 18 months was 0.830 (95% CI: 0.728-0.932), 0.856 (95% CI: 0.787-0.924) and 0.832 (95% CI: 0.764-0.901), respectively. The AUC values were higher in the RSF model, when compared to the CPH regression model and ALBI grade model, in both groups. The DCA results revealed that the net clinical benefits associated to the RSF model were superior to those associated to the CPH regression model and ALBI grade model in both groups, suggesting a higher level of clinical utility in the RSF model. The Brier score for the RSF model at 6, 12 and 18 months was 0.062, 0.125 and 0.178, respectively, in the training group, and 0.111, 0.128 and 0.149, respectively, in the validation group. In summary, the RSF model demonstrated superior performance, when compared to the CPH regression model and ALBI grade model. Furthermore, the RSF model demonstrated superior predictive ability, accuracy, clinical practicality, and overall performance, when compared to the CPH regression model and ALBI grade model. In addition, the RSF model was able to successfully stratify patients into three distinct risk groups (low-risk, medium-risk and high-risk) in both groups (p < 0.001).

CONCLUSIONS

The RSF model demonstrates efficacy in predicting early recurrence following HCC surgery, exhibiting superior performance, when compared to the CPH regression model and ALBI grade model. For patients undergoing HCC surgery, the RSF model can serve as a valuable tool for clinicians to postoperatively stratify patients into distinct risk categories, offering guidance for subsequent follow-up care.

The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Prognostic significance of psoas muscle index in male hepatocellular carcinoma patients treated with immune checkpoint inhibitors and tyrosine kinase inhibitors

Currently, the relationship between nutritional indices and the prognosis of hepatocellular carcinoma (HCC) patients treated with immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) remains unclear. This study aims to investigate the prognostic value of psoas muscle index (PMI), prognostic nutritional index (PNI), body mass index (BMI), and geriatric nutritional risk index (GNRI) in HCC patients treated with ICIs combined with TKIs. A total of 124 male patients with HCC were included in this study. PNI, PMI, BMI, and GNRI were calculated at the beginning of treatment. The Cox proportional hazards model was used to analyze the effect of various variables. In the univariate analysis, PMI, PNI, GNRI, and ALB were found to impact the outcomes of the patients at different follow-up times. However, the predictive value of these nutritional indices was eliminated when established risk factors were considered. In the multivariate analysis that only included nutrition-related indicators, PMI emerged as an independent prognostic factor for 1-year treatment outcomes. The group with low PMI (≤5.5409 cm2/m2) was found to have a higher risk of death at one year and at the end of the follow-up period.

Real-world 10-year retrospective study of the guidelines for diagnosis and treatment of primary liver cancer in China

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. Many regions across the world have issued various HCC diagnosis and treatment protocols to improve the diagnosis and targeted treatment of patients with HCC. However, real-world studies analysing the practice, application value, and existing problems of the China Liver Cancer (CNLC) staging system are scarce.

AIM

To analyze the current situation and problems associated with the Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China.

METHODS

We collected the medical records of all patients with HCC admitted to the First Affiliated Hospital of Zhengzhou University from January 1, 2011 to December 31, 2019, and recorded the hospitalization information of those patients until December 31, 2020. All information on the diagnosis and treatment of the target patients was recorded, and their demographic and sociological characteristics, CNLC stages, screening situations, and treatment methods and effects were analyzed. The survival status of the patients was obtained from follow-up data.

RESULTS

This study included the medical records of 3022 patients with HCC. Among these cases, 304 patients were screened before HCC diagnosis; their early-stage diagnosis rate was 69.08%, which was significantly higher than that of patients with HCC who were diagnosed without screening and early detection (33.74%). Herein, patients with no clinical outcome at discharge were followed up, and the survival information of 1128 patients was obtained. A Cox model was used to analyse independent risk factors affecting overall survival, which were revealed as age > 50 years, no screening, alpha-fetoprotein > 400 ng/mL, Child–Pugh grade B, and middle and late CNLC stages. Based on the Cox model survival analysis, in our study, patients with HCC identified via screening had significant advantages in overall and tumor-free survival after hepatectomy.

CONCLUSION

Early diagnosis and treatment can be achieved by screening groups at high risk for HCC based on the guidelines; however, real-world compliance is poor.

Real-world 10-year retrospective study of the guidelines for diagnosis and treatment of primary liver cancer in China

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. Many regions across the world have issued various HCC diagnosis and treatment protocols to improve the diagnosis and targeted treatment of patients with HCC. However, real-world studies analysing the practice, application value, and existing problems of the China Liver Cancer (CNLC) staging system are scarce.

AIM

To analyze the current situation and problems associated with the Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China.

METHODS

We collected the medical records of all patients with HCC admitted to the First Affiliated Hospital of Zhengzhou University from January 1, 2011 to December 31, 2019, and recorded the hospitalization information of those patients until December 31, 2020. All information on the diagnosis and treatment of the target patients was recorded, and their demographic and sociological characteristics, CNLC stages, screening situations, and treatment methods and effects were analyzed. The survival status of the patients was obtained from follow-up data.

RESULTS

This study included the medical records of 3022 patients with HCC. Among these cases, 304 patients were screened before HCC diagnosis; their early-stage diagnosis rate was 69.08%, which was significantly higher than that of patients with HCC who were diagnosed without screening and early detection (33.74%). Herein, patients with no clinical outcome at discharge were followed up, and the survival information of 1128 patients was obtained. A Cox model was used to analyse independent risk factors affecting overall survival, which were revealed as age > 50 years, no screening, alpha-fetoprotein > 400 ng/mL, Child-Pugh grade B, and middle and late CNLC stages. Based on the Cox model survival analysis, in our study, patients with HCC identified via screening had significant advantages in overall and tumor-free survival after hepatectomy.

CONCLUSION

Early diagnosis and treatment can be achieved by screening groups at high risk for HCC based on the guidelines; however, real-world compliance is poor.

Delimiting Low Level of Difficulty Scoring System Based on the Extent of Resection Difficulty Scoring System for Laparoscopic Liver Resection

Background: The difficulty scoring system based on the extent of resection (DSS-ER) is a common tool for assessing the difficulty and risk of laparoscopic liver resection (LLR), but DSS-ER fails to comprehensively and accurately assess low level for beginners. Methods: The 93 cases of LLRs for primary liver cancer in the general surgery department of the Second Affiliated Hospital of Guangxi Medical University from 2017 to 2021 were retrospectively analyzed. The low level of DSS-ER difficulty scoring system was reclassified into three grades. The intraoperative and postoperative complications were compared among different groups. Results: There were significant differences in the operative time, blood loss, intraoperative allogeneic blood transfusion, conversion to laparotomy, and allogeneic blood transfusion among the different groups. Meanwhile, the postoperative complications were mainly pleural effusion and pneumonia, and the incidence of grade III was higher compared with other two grades. No significant difference existed in the postoperative biliary leakage and liver failure among three grades. Conclusions: This reclassified low level of DSS-ER difficulty scoring system has certain clinical value for LLR beginners to complete the corresponding learning curve.

Pre- to postoperative alpha-fetoprotein ratio-based nomogram to predict tumor recurrence in patients with hepatocellular carcinoma

Background

This study aimed to investigate the role of the alpha fetoprotein (AFP) ratio before and after curative resection in the prognosis of patients with hepatocellular carcinoma (HCC) and to develop a novel pre- to postoperative AFP ratio nomogram to predict recurrence free survival (RFS) for HCC patients after curative resection.

Methods

A total of 485 pathologically confirmed HCC patients who underwent radical hepatectomy from January 2010 to December 2018 were retrospectively analyzed. The independent prognostic factors of hepatocellular carcinoma were identified by multivariate COX proportional model analysis, and the nomogram model was constructed. The receiver operating characteristic and the C-index were used to evaluate the accuracy and efficacy of the model prediction, the correction curve was used to assess the calibration of the prediction model, and decision curve analysis was used to evaluate the clinical application value of the nomogram model.

Results

A total of 485 HCC patients were divided into the training cohort (n = 340) and the validation cohort (n = 145) by random sampling at a ratio of 7:3. Using X-tile software, it was found that the optimal cut-off value of the AFP ratio in the training cohort was 0.8. In both cohorts, the relapse-free survival of patients with an AFP ratio <0.8 (high-risk group) was significantly shorter than in those with an AFP ratio ≥0.8 (low-risk group) (P < 0.05). An AFP ratio <0.8 was an independent risk factor for recurrence of HCC after curative resection. Based on the AFP ratio, BCLC stage and cirrhosis diagnosis, a satisfactory nomogram was developed. The AUC of our nomogram for predicting 1-, 3-, and 5-year RFS was 0.719, 0.690, and 0.708 in the training cohort and 0.721, 0.682, and 0.681 in the validation cohort, respectively. Furthermore, our model demonstrated excellent stratification as well as clinical applicability.

Conclusion

The AFP ratio was a reliable biomarker for tumor recurrence. This easy-to-use AFP ratio-based nomogram precisely predicted tumor recurrence in HCC patients after curative resection.

Nomogram Based on Inflammatory Biomarkers to Predict the Recurrence of Hepatocellular Carcinoma-A Multicentre Experience

PURPOSE

Our study aimed to identify inflammatory biomarkers and develop a prediction model to stratify high-risk patients for hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) recurrence after curative resection.

PATIENTS AND METHODS

A total of 583 eligible HBV-HCC patients with curative hepatectomy from Guangdong Provincial People's Hospital (GDPH) and Sun Ya-sen University Cancer Centre (SYSUCC) were enrolled in our study. Cox proportional hazards regression was utilized to evaluate potential risk factors for disease-free survival (RFS). The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to assess the discrimination performance. Calibration plots and decision curve analyses (DCA) were used to evaluate the calibration of the nomogram and the net benefit, respectively.

RESULTS

Based on the systemic inflammation response index (SIRI), aspartate aminotransferase to neutrophil ratio index (ANRI), China Liver Cancer (CNLC) stage and microvascular invasion, a satisfactory nomogram was developed. The AUC of our nomogram for predicting 1-, 2-, and 3-year RFS was 0.767, 0.726, and 0.708 in the training cohort and 0.761, 0.716, and 0.715 in the validation cohort, respectively. Furthermore, our model demonstrated excellent stratification as well as clinical applicability.

CONCLUSION

The novel nomogram showed a higher prognostic power for the RFS of HCC patients with curative hepatectomy than the CNLC, AJCC 8th edition and BCLC staging systems and may help oncologists identify high-risk HCC patients.