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

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.

journal_name

Aging (Albany NY)

journal_title

Aging

authors

Cao F,Shen L,Qi H,Xie L,Song Z,Chen S,Fan W

doi

10.18632/aging.102403

subject

Has Abstract

pub_date

2019-11-03 00:00:00

pages

9544-9555

issue

21

issn

1945-4589

pii

102403

journal_volume

11

pub_type

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