Prognostic nomogram predicting survival of patients with unresectable hepatocellular carcinoma after hepatic arterial infusion chemotherapy

Predictive factors and prognostic models for Hepatic arterial infusion chemotherapy in Hepatocellular carcinoma: a comprehensive review

Hepatocellular carcinoma (HCC) is a prevalent and lethal cancer, often diagnosed at advanced stages where traditional treatments such as surgical resection, liver transplantation, and locoregional therapies provide limited benefits. Hepatic arterial infusion chemotherapy (HAIC) has emerged as a promising treatment modality for advanced HCC, enhancing anti-tumor efficacy through targeted drug delivery while minimizing systemic side effects. However, the heterogeneous nature of HCC leads to variable responses to HAIC, highlighting the necessity for reliable predictive indicators to tailor personalized treatment strategies. This review explores the factors influencing HAIC success, including patient demographics, tumor characteristics, biomarkers, genomic profiles, and advanced imaging techniques such as radiomics and deep learning models. Additionally, the synergistic potential of HAIC combined with immunotherapy and molecular targeted therapies is examined, demonstrating improved survival outcomes. Prognostic scoring systems and nomograms that integrate clinical, molecular, and imaging data are discussed as superior tools for individualized prognostication compared to traditional staging systems. Understanding these predictors is essential for optimizing HAIC efficacy and enhancing survival and quality of life for patients with advanced HCC. Future research directions include large-scale prospective studies, integration of multi-omics data, and advancements in artificial intelligence to refine predictive models and further personalize treatment approaches.

Predicting the early therapeutic response to hepatic artery infusion chemotherapy in patients with unresectable HCC using a contrast-enhanced computed tomography-based habitat radiomics model: a multi-center retrospective study

OBJECTIVE

Predicting the therapeutic response before initiation of hepatic artery infusion chemotherapy (HAIC) with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) remains challenging for patients with unresectable hepatocellular carcinoma (HCC). Herein, we investigated the potential of a contrast-enhanced CT-based habitat radiomics model as a novel approach for predicting the early therapeutic response to HAIC-FOLFOX in patients with unresectable HCC.

METHODS

A total of 148 patients with unresectable HCC who received HAIC-FOLFOX combined with targeted therapy or immunotherapy at three tertiary care medical centers were enrolled retrospectively. Tumor habitat features were extracted from subregion radiomics based on CECT at different phases using k-means clustering. Logistic regression was used to construct the model. This CECT-based habitat radiomics model was verified by bootstrapping and compared with a model based on clinical variables. Model performance was evaluated using the area under the curve (AUC) and a calibration curve.

RESULTS

Three intratumoral habitats with high, moderate, and low enhancement were identified to construct a habitat radiomics model for therapeutic response prediction. Patients with a greater proportion of high-enhancement intratumoral habitat showed better therapeutic responses. The AUC of the habitat radiomics model was 0.857 (95% CI: 0.798-0.916), and the bootstrap-corrected concordance index was 0.842 (95% CI: 0.785-0.907), resulting in a better predictive value than the clinical variable-based model, which had an AUC of 0.757 (95% CI: 0.679-0.834).

CONCLUSION

The CECT-based habitat radiomics model is an effective, visualized, and noninvasive tool for predicting the early therapeutic response of patients with unresectable HCC to HAIC-FOLFOX treatment and could guide clinical management and decision-making.

Development and validation of a prognostic nomogram including inflammatory indicators for overall survival in hepatocellular carcinoma patients treated primarily with surgery or loco-regional therapy: A single-center retrospective study

Hepatocellular carcinoma (HCC) is among the most prevalent malignant tumors, but the current staging system has limited efficacy in predicting HCC prognosis. The authors sought to develop and validate a nomogram model for predicting overall survival (OS) in HCC patients primarily undergoing surgery or loco-regional therapy. Patients diagnosed with HCC from January 2017 to June 2023 were enrolled in the study. The data were randomly split into a training cohort and a validation cohort. Utilizing univariate and multivariate Cox regression analyses, independent risk factors for OS were identified, and a nomogram model was constructed to predict patient survival. Therapy, body mass index, portal vein tumor thrombus, leukocyte, γ-glutamyl transpeptidase to platelet ratio, monocyte to lymphocyte ratio, and prognostic nutritional index were used to build the nomogram for OS. The nomogram demonstrated strong predictive ability, with high C-index values (0.745 for the training cohort and 0.650 for the validation cohort). ROC curves, calibration plots, and DCA curves all indicated satisfactory performance of the nomogram. Kaplan-Meier curve analysis showed a significant difference in prognosis between patients in the low- and high- risk groups. This nomogram provides precise survival predictions for HCC patients and helps identify individuals with varying prognostic risks, emphasizing the need for individualized follow-up and treatment plans.

Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning

BACKGROUND

Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective.

PURPOSE

To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI).

METHODS

This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index.

RESULTS

A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p < .00001 in both cohorts).

CONCLUSIONS

Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice.

CLINICAL RELEVANCE STATEMENT

Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards.

KEY POINTS

• Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort.

Patients with hepatocellular carcinoma extrahepatic metastases can benefit from hepatic arterial infusion chemotherapy combined with lenvatinib plus programmed death-1 inhibitors

BACKGROUND

Lenvatinib plus programmed death-1 (PD-1) inhibitors (LEN-P) have been recommended in China for patients with advanced hepatocellular carcinoma (HCC). However, they provide limited survival benefits to patients with extrahepatic metastases. We aimed to investigate whether combining hepatic arterial infusion chemotherapy (HAIC) with LEN-P could improve its efficacy.

MATERIALS AND METHODS

This multicenter cohort study included patients with HCC extrahepatic metastases who received HAIC combined with LEN-P (HAIC-LEN-P group, n =127) or LEN-P alone ( n =103) as the primary systemic treatment between January 2019 and December 2022. Baseline data were balanced using a one-to-one propensity score matching (PSM) and inverse probability of treatment weighting (IPTW).

RESULTS

After PSM, the HAIC-LEN-P group significantly extended the median overall survival (mOS) and median progression-free survival (mPFS), compared with the LEN-P group (mOS: 27.0 months vs. 9.0 months, P <0.001; mPFS: 8.0 months vs. 3.0 months, P =0.001). After IPTW, the mOS [hazard ratio (HR)=0.384, P <0.001] and mPFS (HR=0.507, P <0.001) were significantly higher in the HAIC-LEN-P group than in the LEN-P group. The HAIC-LEN-P group's objective response rate was twice as high as that of the LEN-P group (PSM cohort: 67.3% vs. 29.1%, P <0.001; IPTW cohort: 66.1% vs. 27.8%, P <0.001). Moreover, the HAIC-LEN-P group exhibited no noticeable increase in the percentages of grade 3 and 4 adverse events compared with the LEN-P group ( P >0.05).

CONCLUSION

HAIC can improve the efficacy of LEN-P in patients with HCC extrahepatic metastases and may be an alternative treatment for advanced HCC management.

Predicting the prognosis of hepatic arterial infusion chemotherapy in hepatocellular carcinoma

Hepatic artery infusion chemotherapy (HAIC) has good clinical efficacy in the treatment of advanced hepatocellular carcinoma (HCC); however, its efficacy varies. This review summarized the ability of various markers to predict the efficacy of HAIC and provided a reference for clinical applications. As of October 25, 2023, 51 articles have been retrieved based on keyword predictions and HAIC. Sixteen eligible articles were selected for inclusion in this study. Comprehensive literature analysis found that methods used to predict the efficacy of HAIC include serological testing, gene testing, and imaging testing. The above indicators and their combined forms showed excellent predictive effects in retrospective studies. This review summarized the strategies currently used to predict the efficacy of HAIC in middle and advanced HCC, analyzed each marker's ability to predict HAIC efficacy, and provided a reference for the clinical application of the prediction system.

Predicting the prognosis of hepatic arterial infusion chemotherapy in hepatocellular carcinoma

Hepatic artery infusion chemotherapy (HAIC) has good clinical efficacy in the treatment of advanced hepatocellular carcinoma (HCC); however, its efficacy varies. This review summarized the ability of various markers to predict the efficacy of HAIC and provided a reference for clinical applications. As of October 25, 2023, 51 articles have been retrieved based on keyword predictions and HAIC. Sixteen eligible articles were selected for inclusion in this study. Comprehensive literature analysis found that methods used to predict the efficacy of HAIC include serological testing, gene testing, and imaging testing. The above indicators and their combined forms showed excellent predictive effects in retrospective studies. This review summarized the strategies currently used to predict the efficacy of HAIC in middle and advanced HCC, analyzed each marker's ability to predict HAIC efficacy, and provided a reference for the clinical application of the prediction system.

Nomogram Based on Body Composition and Prognostic Nutritional Index Predicts Survival After Curative Resection of Gastric Cancer

RATIONALE AND OBJECTIVES

This study aimed to identify independent prognostic factors for gastric cancer (GC) patients after curative resection using quantitative computed tomography (QCT) combined with prognostic nutritional index (PNI), and to develop a nomogram prediction model for individualized prognosis.

MATERIALS AND METHODS

This study retrospectively analyzed 119 patients with GC who underwent curative resection from January 2016 to March 2018. The patients' preoperative clinical pathological data were recorded, and all patients underwent QCT scans before and after curative resection to obtain QCT parameters: bone mineral density (BMD), skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA) and CT fat fraction (CTFF), then relative rate of change in each parameter (ΔBMD, ΔSMA, ΔVFA, ΔSFA, ΔCTFF) was calculated after time normalization. Multivariate Cox proportional hazards was used to establish a nomogram model that based on independent prognostic factors. The concordance index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve and clinical decision curve were used to evaluate the predictive performance and clinical benefit of the nomogram model． RESULTS: This study found that ΔCTFF, ΔVFA, ΔBMD and PNI are independent prognostic factors for overall survival (OS) (hazard ratio: 1.034, 0.895, 0.976, 2.951, respectively, all p < 0.05). The established nomogram model could predict the area under the ROC curve of OS at 1, 3 and 5 years as 0.816, 0.815 and 0.881, respectively. The C-index was 0.743 (95% CI, 0.684-0.801), and the decision curve analysis showed that this model has good clinical net benefit.

CONCLUSION

The nomogram model based on body composition and PNI is reliable in predicting the individualized survival of underwent curative resection for GC patients.

A Novel Nomogram for Predicting the Overall Survival in Patients with Unresectable HCC after TACE plus Hepatic Arterial Infusion Chemotherapy

BACKGROUND AND AIM

Transarterial chemoembolization combined with hepatic arterial infusion chemotherapy (TACE-HAIC) has shown encouraging efficacy in the treatment of unresectable hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict overall survival (OS) of patients with unresectable HCC treated with TACE-HAIC.

METHODS

A total of 591 patients with unresectable HCC treated with TACE-HAIC between May 2009 and September 2020 were enrolled. These patients were randomly divided into training and validation cohorts. The independent prognostic factors were identified with Cox proportional hazards model. The model's discriminative ability and accuracy were validated using concordance index (C-index), calibration plots, the area under the time-dependent receiver operating characteristic curve (AUC) and decision curve analyses (DCAs).

RESULTS

The median OS was 15.6 months. A nomogram was established based on these factors, including tumor size, vein invasion, extrahepatic metastasis, tumor number, alpha fetoprotein (AFP), and albumin-bilirubin (ALBI), to predict OS for patients with unresectable HCC treated with TACE-HAIC. The C-index of the nomogram were 0.717 in the training cohort and 0.724 in validation cohort. The calibration plots demonstrated good agreement between the predicted outcomes and the actual observations. The AUC values were better than those of three conventional staging systems. The results of DCA indicated that the nomogram may have clinical usefulness. The patients in the low-risk group had a longer OS than those in intermediate-risk and high-risk groups (P<0.001).

CONCLUSION

A prognostic nomogram was developed and validated to assist clinicians in accurately predicting the OS of patients with unresectable HCC after TACE-HAIC.

Development and validation of prognostic nomograms for large hepatocellular carcinoma after HAIC

BACKGROUND AND AIMS

Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for large hepatocellular carcinoma (HCC). However, post-HAIC prognosis can vary in different patients due to tumor heterogeneity. Herein, we established two nomogram models to assess the survival prognosis of patients after HAIC combination therapy.

METHODS

A total of 1082 HCC patients who underwent initial HAIC were enrolled between February 2014 and December 2021. We built two nomogram models for survival prediction: the preoperative nomogram (pre-HAICN) using preoperative clinical data and the postoperative nomogram (post-HAICN) based on pre-HAICN and combination therapy. The two nomogram models were internally validated in one hospital and externally validated in four hospitals. A multivariate Cox proportional hazards model was used to identify risk factors for overall survival (OS). The performance outcomes of all models were compared by area under the receiver operating characteristic curve (AUC) analysis with the DeLong test.

RESULTS

Multivariable analysis identified larger tumor size, vascular invasion, metastasis, high albumin-bilirubin grade, and high alpha-fetoprotein as indicators for poor prognosis. With these variables, the pre-HAICN provided three risk strata for OS in the training cohort: low risk (5-year OS, 44.9%), middle risk (5-year OS, 20.6%), and high risk (5-year OS, 4.9%). The discrimination of the three strata was improved significantly in the post-HAICN, which included the above-mentioned factors and number of sessions, combination with immune checkpoint inhibitors, tyrosine kinase inhibitors, and local therapy (AUC, 0.802 versus 0.811, p < 0.001).

CONCLUSIONS

The nomogram models are essential to identify patients with large HCC suitable for treatment with HAIC combination therapy and may potentially benefit personalized decision-making.

LAY SUMMARY

Hepatic arterial infusion chemotherapy (HAIC) provides sustained higher concentrations of chemotherapy agents in large hepatocellular carcinoma (HCC) by hepatic intra-arterial, result in better objective response outperformed the intravenous administration. HAIC is significantly correlated with favorable survival outcome and obtains extensive support in the effective and safe treatment of intermediate advanced-stage HCC. In view of the high heterogeneity of HCC, there is no consensus regarding the optimal tool for risk stratification before HAIC alone or HAIC combined with tyrosine kinase inhibitors or immune checkpoint inhibitors treatment in HCC. In this large collaboration, we established two nomogram models to estimate the prognosis and evaluate the survival benefits with different HAIC combination therapy. It could help physicians in decision-making before HAIC and comprehensive treatment for large HCC patients in clinical practice and future trials.

Development and Validation of a nomogram for forecasting survival of alcohol related hepatocellular carcinoma patients

BACKGROUND

With the increasing incidence and prevalence of alcoholic liver disease, alcohol-related hepatocellular carcinoma has become a serious public health problem worthy of attention in China. However, there is currently no prognostic prediction model for alcohol-related hepatocellular carcinoma.

METHODS

The retrospective analysis research of alcohol related hepatocellular carcinoma patients was conducted from January 2010 to December 2014. Independent prognostic factors of alcohol related hepatocellular carcinoma were identified by Lasso regression and multivariate COX proportional model analysis, and the nomogram model was constructed. The reliability and accuracy of the model were assessed using the concordance index(C-Index), receiver operating characteristic (ROC) curve and calibration curve. Evaluate the clinical benefit and application value of the model through clinical decision curve analysis (DCA). The prognosis was assessed by the Kaplan-Meier (KM) survival curve.

RESULTS

In sum, 383 patients were included in our study. Patients were stochastically assigned to training cohort (n=271) and validation cohort (n=112) according to 7:3 ratio. The predictors included in the nomogram were splenectomy, platelet count (PLT), creatinine (CRE), Prealbumin (PA), mean erythrocyte hemoglobin concentration (MCHC), red blood cell distribution width (RDW) and TNM. Our nomogram demonstrated excellent discriminatory power (C-index) and good calibration at 1-year, 3-year and 5- year overall survival (OS). Compared to TNM and Child-Pugh model, the nomogram had better discriminative ability and higher accuracy. DCA showed high clinical benefit and application value of the model.

CONCLUSION

The nomogram model we established can precisely forcasting the prognosis of alcohol related hepatocellular carcinoma patients, which would be helpful for the early warning of alcohol related hepatocellular carcinoma and predict prognosis in patients with alcoholic hepatocellular carcinoma.