Research Paper|Volume 11, Issue 21|pp 9544—9555

Tree-based classification system incorporating the HVTT-PVTT score for personalized management of hepatocellular carcinoma patients with macroscopic vascular invasion

Fei Cao¹, Lujun Shen¹, Han Qi¹, Lin Xie¹, Ze Song², Shuanggang Chen¹, Weijun Fan¹

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
²Department of Oncology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China

* Equal contribution

Received: August 8, 2019Accepted: October 26, 2019Published: November 3, 2019

Copyright © 2019 Cao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

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.