Tree-based classification system incorporating the HVTT-PVTT score for personalized management of hepatocellular
Fei Cao1, Lujun Shen1, Han Qi1
1Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China.
Insights
A new classification system using a decision tree algorithm aids personalized management for hepatocellular carcinoma (HCC) patients with vascular invasion. This system refines prognostication and improves upon existing staging methods for better patient care.
Area of Science:
- Hepatobiliary surgery
- Oncology
- Medical informatics
Background:
- Hepatocellular carcinoma (HCC) with macroscopic vascular invasion presents a significant clinical challenge.
- Effective personalized management strategies are crucial for improving patient outcomes.
Purpose of the Study:
- To develop a decision tree algorithm-based classification system for personalized management of HCC patients with macroscopic vascular invasion.
- To create a refined prognostic tool that improves upon existing staging systems.
Main Methods:
- A cohort of 869 HCC patients with macroscopic vascular invasion was divided into training and validation sets.
- A Hepatic Vascular Tumor Thrombus-Portal Vein Tumor Thrombus (HVTT-PVTT) score was developed.
- A decision tree algorithm (Classification and Regression Tree - CART) was employed to establish a refined classification system incorporating prognostic factors.
Main Results:
- The HVTT-PVTT score differentiated patient groups with distinct survival outcomes and surgical proportions.
- The decision tree algorithm classified patients into three subgroups with significantly different prognoses in both training and validation sets.
- The developed classification system demonstrated superior predictive accuracy compared to other common staging systems.
Conclusions:
- The proposed classification system offers a novel approach for the personalized management of HCC patients with macroscopic vascular invasion.
- This system holds potential for guiding treatment decisions and improving survival predictions in this patient cohort.
Purpose:
To develop a decision tree algorithm-based classification system for personalized management of hepatocellular carcinoma (HCC) patients with macroscopic vascular invasion.
Results:
The HVTT-PVTT score could differentiate two groups of patients (< 3 and ≥ 3 points) with different survival outcomes (7.4 vs 4.6 months, P < 0.001) and surgical proportion (24.4% vs 3.6%, P < 0.001). Using the Cox regression model and classification and regression tree (CART) algorithm, patients in the training set were automatically separated into three subgroups with different prognosis (10.3 vs 6.1 vs 3.3 months). The predictive accuracy was verified in the validation group (12.3 vs 6.9 vs 5.6 months) and was better than other commonly used staging systems.
Conclusions:
Our study proposed a new classification system for HCC patients with macroscopic vascular invasion that could be meaningful for personalized management of these patients.
Methods:
A total of 869 HCC patients initially diagnosed with macroscopic vascular invasion were randomly divided into training and validation sets. A comprehensive and simplified HVTT-PVTT score was set up for subdivision of vascular invasion according to the patients' survival outcome. Then, a decision tree algorithm-based classification system was used to establish the refined subdivision system incorporating all independent prognostic factors.
