Platelet-lymphocyte ratio predicts survival in patients with hepatocellular carcinoma who receive lenvatinib: an inverse probability weighting analysis

Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma

BACKGROUND

Lenvatinib is a first-line agent for advanced hepatocellular carcinoma (HCC), but individual responses to treatment are highly heterogeneous. The aim of this study was to investigate the clinical parameters that influence the efficacy of Lenvatinib and to develop a prognostic model.

METHODS

We retrospectively enrolled 333 Lenvatinib-treated patients with HCC with a median age of 57 years. Two hundred nd sixty-three of these patients had BCLC (2022) stage C. The median overall survival (mOS) time within the cohort was 12.1 months, and the median progression-free survival (mPFS) time was 4.7 months. Univariate Cox regression, best subset regression, and Lasso regression were used to screen primary variables for possible contribution to OS, multivariate Cox analysis was used to fit selected models, and the final model was selected using the maximum area under the curve (AUC) and minimum AIC. Receiver operating curves (ROC), calibration curves, and decision curve analysis were plotted to assess model performance, and 5-fold cross-validation was performed for internal validation. X-tile software was used to select the best cutoff points and to divide the study cohort into 3 different risk groups.

RESULTS

Seven variables were included in the final model: BCLC stage, prior transarterial chemoembolization and immunotherapy history, tumor number, prognostic nutritional index, log (alpha-fetoprotein), and log (platelet-to-lymphocyte ratio). We named this final model the "multivariate prognostic model for Lenvatinib" (MPML), and a nomogram was constructed to predict the probability of survival at 6, 9, and 12 months. The MPML had good discrimination, calibration, and applicability. Cross-validation showed mean AUC values of 0.7779, 0.7738, and 0.7871 at 6, 9, and 12 months, respectively. According to nomogram points, mOS time was 21.57, 8.70, and 5.37 months in the low, medium, and high-risk groups, respectively (P < .001), and these differences were also observed in the PFS survival curve (P < .001).

CONCLUSIONS

The MPML stratified patients according to baseline clinical characteristics had a strong performance in predicting Lenvatinib efficacy and has the potential for use as an auxiliary clinical tool for individualized decision-making.

Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma

BACKGROUND

Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data.

METHODS

Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4-12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model.

RESULTS

A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61-0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level.

CONCLUSIONS

A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

Prognostic Role of Neutrophil-to-Lymphocyte Ratio (NLR), Lymphocyte-to-Monocyte Ratio (LMR), Platelet-to-Lymphocyte Ratio (PLR) and Lymphocyte-to-C Reactive Protein Ratio (LCR) in Patients with Hepatocellular Carcinoma (HCC) undergoing Chemoembolizations (TACE) of the Liver: The Unexplored Corner Linking Tumor Microenvironment, Biomarkers and Interventional Radiology

TACE plays a pivotal role in hepatocellular carcinoma, from disease control to downstaging and bridging to liver transplant. Response to TACE is a surrogate marker of tumor aggressive biology, with manifold practical implications such as survival, the need for more aggressive treatments in the intermediate stage, the selection of patients on the transplant waiting list, the dropout rate from the transplant list and the post-transplant recurrence rate. Inflammation-based scores are biomarkers of the relationship between the tumor stromal microenvironment and the immune response. Investigating the connection among the tumor stromal microenvironment, biomarkers, and the response to TACE is crucial to recognize TACE refractoriness/failure, thus providing patients with tailored therapeutics. This review aims to provide a comprehensive overview of the prognostic roles of the neutrophil-to-lymphocyte ratio (NLR), the lymphocyte-to-monocyte ratio (LMR), the platelet-to-lymphocyte ratio (PLR), and the lymphocyte-to-C reactive protein ratio (LCR) in patients with HCC undergoing chemoembolization of the liver. Inflammation-based scores may be convenient, easily obtained, low-cost, and reliable biomarkers with prognostic significance for HCC undergoing TACE. Baseline cut-off values differ between various studies, thus increasing confusion about using of inflammation-based scores in clinical practice. Further investigations should be conducted to establish the optimal cut-off values for inflammation-based scores, consolidating their use in clinical practice.

Prognostic role of multiparameter MRI and radiomics in progression of advanced unresectable hepatocellular carcinoma following combined transcatheter arterial chemoembolization and lenvatinib therapy

BACKGROUND

Current study aims to determine the prognostic value of Multiparameter MRI after combined Lenvatinib and TACE therapy in patients with advanced unresectable hepatocellular carcinoma (HCC).

METHODS

A total of 61 HCC patients with pre-treatment Multiparameter MRI in Sun Yat-sen University Cancer Center from January 2019 to March 2021 were recruited in the current study. All patients received combined Lenvatinib and TACE treatment. Potential clinical and imaging risk factors for disease progression were analyzed using Cox regression model. Each patient extracts signs from the following 7 sequences: T1WI, T1WI arterial phase, T1WI portal phase, T1WI delay phase, T2WI, DWI (b = 800), ADC.1782 quantitative 3D radiomic features were extracted for each sequence, A random forest algorithm is used to select the first 20 features by feature importance. 7 logit regression-based prediction model was built for seven sequences based on the selected features and fivefold cross validation was used to evaluate the performance of each model.

RESULTS

CR, PR, SD were reported in 14 (23.0%), 35 (57.4%) and 7 (11.5%) patients, respectively. In multivariate analysis, tumor number (hazard ratio, HR = 4.64, 95% CI 1.03-20.88), and arterial phase intensity enhancement (HR = 0.24, 95% CI 0.09-0.64; P = 0.004) emerged as independent risk factors for disease progression. In addition to clinical factors, the radiomics signature enhanced the accuracy of the clinical model in predicting disease progression, with an AUC of 0.71, a sensitivity of 0.99%, and a specificity of 0.95.

CONCLUSION

Radiomic signatures derived from pretreatment MRIs could predict response to combined Lenvatinib and TACE therapy. Furthermore, it can increase the accuracy of a combined model for predicting disease progression. In order to improve clinical outcomes, clinicians may use this to select an optimal treatment strategy and develop a personalized monitoring protocol.

The Prognostic Value of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Patients with Hepatocellular Carcinoma Receiving Atezolizumab Plus Bevacizumab

Atezolizumab plus bevacizumab has been approved as the first-line systemic treatment for patients with unresectable hepatocellular carcinoma (uHCC). This study was designed to assess the clinical impact of atezolizumab plus bevacizumab in uHCC patients. A total of 48 uHCC patients receiving atezolizumab plus bevacizumab were identified, including first-line, second-line, third-line, and later-line settings. In these patients, the median progression-free survival (PFS) was 5.0 months, including 5.0 months for the first-line treatment, not reached for the second-line treatment, and 2.5 months for the third line and later line treatment. The objective response rate and disease control rate to atezolizumab plus bevacizumab were 27.1% and 68.8%, respectively. The severity of most adverse events was predominantly grade 1-2, and most patients tolerated the toxicities. The ratios of the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte (PLR) were used to predict PFS in these patients. The optimal cutoff values of NLR and PLR were 3 and 230, and NLR and PLR were independent prognostic factors for superior PFS in the univariate and multivariate analyses. Our study confirms the efficacy and safety of atezolizumab plus bevacizumab in uHCC patients in clinical practice and demonstrates the prognostic role of NLR and PLR for PFS in these patients.

Lenvatinib for Hepatocellular Carcinoma: A Literature Review

Lenvatinib, which is an oral multikinase inhibitor, showed non-inferiority to the sorafenib in terms of overall survival (OS) and a higher objective response rate (ORR) and better progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC). A good liver function and Barcelona Clinic Liver Cancer (BCLC) intermediate stage were the key factors in achieving therapeutic efficacy. The management of adverse events plays an important role in continuing lenvatinib treatment. While sequential therapies contributed to prolonging overall survival, effective molecular targeted agents for the administration after lenvatinib have not been established. Repeated transcatheter arterial chemoembolization (TACE) was associated with a decline in the liver function and poor therapeutic response in BCLC intermediate patients. Recently, the Asia-Pacific Primary Liver Cancer Expert (APPLE) Consensus Statement proposed the criteria for TACE unsuitability. Upfront systemic therapy may be better for the BCLC intermediate stage HCC patients with a high tumor burden, while selective TACE will be recommended for obtaining a curative response in patients with a low tumor burden. This article reviews the therapeutic response, management of adverse events, post-progression treatment after Lenvatinib, and treatment strategy for BCLC intermediate stage HCC.

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.

Effect of Handgrip Strength on Clinical Outcomes of Patients with Hepatocellular Carcinoma Treated with Lenvatinib

Previous studies have reported prognostic factors for hepatocellular carcinoma (HCC) patients receiving lenvatinib; however, no studies have evaluated the effects of both handgrip strength and skeletal muscle mass on the clinical outcomes. Therefore, this retrospective study investigated the individual effect of handgrip strength, skeletal muscle mass, and sarcopenia on clinical outcomes of 53 HCC patients treated with lenvatinib. Before receiving lenvatinib, handgrip strength and skeletal muscle index (SMI) were measured. Low handgrip strength and muscle depletion were defined as <26 and <18 kg and SMI <42 and SMI <38 cm2/m2 in men and women, respectively. Sarcopenia was defined as having low handgrip strength and muscle depletion. Multivariate analysis identified modified albumin–bilirubin grade 1–2a (p = 0.010), Barcelona Clinic Liver Cancer stage A–B (p = 0.011), and absence of low handgrip strength (p = 0.015) as favorable prognostic factors for survival. Furthermore, sarcopenia was an independent significant prognostic factor for survival. Time to treatment failure was associated with handgrip strength and sarcopenia. Our findings suggest that handgrip strength may be a useful marker of clinical outcomes in HCC patients treated with lenvatinib.