A hybrid machine learning model based on semantic information can optimize treatment decision for naïve single 3-5cm HCC patients

• Published in: Liver cancer (Basel )
• Main Authors: Wenzhen Ding, Zhen Wang, Fang-Yi Liu, Zhi-Gang Cheng, Xiaoling Yu, Zhiyu Han, Jie Yu, Ping Liang
• Format: Journal Article
• Language: English
• Published: Karger Publishers 01.01.2022
• ISSN: 2235-1795; 1664-5553
• DOI: 10.1159/000522123

Background: Tumor recurrence is an abomination for hepatocellular carcinoma (HCC) patients receiving local treatment. Purpose: To build a hybrid machine learning model to recommend optimized first treatment (Laparoscopic hepatectomy (LH) or Microwave ablation (MWA)) for naïve single 3-5cm HCC patients based on early recurrence (ER, ≤2 years) probability. Methods: This retrospective study collected 20 semantic variables of 582 patients (LH:300, MWA:282) from 13 hospitals with at least 24 months follow-up. Both groups were divided into training, validation and test set, respectively. Five algorithms (Logistics Regression, Random Forest, Neural Network, Stochastic Gradient Boosting (SGB) and eXtreme Gradient Boosting (XGB)) were used for model building. Model with highest AUC in validation set of LH and MWA was selected to connect as a hybrid model which made decision based on ER probability. Model testing was performed in a comprehensive set composing of LH and MWA test set. Results: Four variables in each group were selected to build LH and MWA model, respectively. LH-XGB model (AUC=0.744) and MWA-SGM (AUC=0.750) model were selected for model building. In comprehensive set, a treatment confusion matrix was established based on recommended and actual treatment. The predicted ER probabilities were comparable with the actual ER rates for various types of patients in matrix (p>0.05). ER rate of patients whose actual treatment consistent with recommendation was lower than that of inconsistent patients (LH:21.2%vs46.2%, p=0.042; MWA:26.3%vs54.1%, p=0.048). By recommending optimal treatment, hybrid model can significantly reduce ER probability from 38.2% to 25.6% for overall patients (p<0.001). Conclusions: The hybrid model can accurately predict ER probability of different treatments, and thereby provide a reliable evidence to make optimal treatment decision for patients with single 3-5cm HCC.