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

Development and validation of a nomogram to predict cancer-specific survival of patients with large hepatocellular carcinoma accepting surgical resection: a real-world analysis based on the SEER database

Background

Only a small percentage of patients with large hepatocellular carcinoma (HCC) can undergo surgical resection (SR) therapy while the prognosis of patients with large HCC is poor. However, innovations in surgical techniques have expanded the scope of surgical interventions accessible to patients with large HCC. Currently, most of the existing nomograms are focused on patients with large HCC, and research on patients who undergo surgery is limited. This study aimed to establish a nomogram to predict cancer-specific survival (CSS) in patients with large HCC who will undergo SR.

Methods

The study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database encompassing patients with HCC between 2010 and 2015. Patients with large HCC accepting SR were eligible participants. Patients were randomly divided into the training (70%) and internal validation (30%) groups. Patients from Air Force Medical Center between 2012 and 2019 who met the inclusion and exclusion criteria were used as external datasets. Demographic information such as sex, age, race, etc. and clinical characteristics such as chemotherapy, histological grade, fibrosis score, etc. were analyzed. CSS was the primary endpoint. All-subset regression and Cox regression were used to determine the relevant variables required for constructing the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to validate the nomogram. The Kaplan-Meier curve was used to assess the CSS of patients with HCC in different risk groups.

Results

In total, 1,209 eligible patients from SEER database and 21 eligible patients from Air Force Medical Center were included. Most patients were male and accepted surgery to lymph node. The independent prognostic factors included sex, histological grade, T stage, chemotherapy, α-fetoprotein (AFP) level, and vascular invasion. The CSS rate for training cohort at 12, 24, and 36 months were 0.726, 0.731, and 0.725 respectively. The CSS rate for internal validation cohort at 12, 24, and 36 months were 0.785, 0.752, and 0.734 respectively. The CSS rate for external validation cohort at 12, 24, and 36 months were 0.937, 0.929, and 0.913 respectively. The calibration curve demonstrated good consistency between the newly established nomogram and real-world observations. The Kaplan-Meier curve showed significantly unfavorable CSS in the high-risk group (P<0.001). DCA demonstrated favorable clinical applicability of the nomogram.

Conclusions

The nomogram constructed based on sex, histological grade, T stage, chemotherapy and AFP levels can predict the CSS in patients with large HCC accepting SR, which may aid in clinical decision-making and treatment.

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.

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy

Introduction

This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).

Methods

The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.

Conclusions

The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.