基于语义信息的混合机器学习模型可优化初治单发3-5厘米肝癌患者的治疗决策。
A Hybrid Machine Learning Model Based on Semantic Information Can Optimize Treatment Decision for Naïve Single 3-5-cm HCC Patients.
作者信息
Ding Wenzhen, Wang Zhen, Liu Fang-Yi, Cheng Zhi-Gang, Yu Xiaoling, Han Zhiyu, Zhong Hui, Yu Jie, Liang Ping
机构信息
Department of Interventional Ultrasound, The First Center of Chinese PLA General Hospital, Beijing, China.
出版信息
Liver Cancer. 2022 Jan 28;11(3):256-267. doi: 10.1159/000522123. eCollection 2022 Jun.
BACKGROUND
Tumor recurrence is an abomination for hepatocellular carcinoma (HCC) patients receiving local treatment.
PURPOSE
The aim of the study was to build a hybrid machine learning model to recommend optimized first treatment (laparoscopic hepatectomy [LH] or microwave ablation [MWA]) for naïve single 3-5-cm 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, and eXtreme Gradient Boosting [XGB]) were used for model building. A model with highest area under the receiver operating characteristic curve (AUC) in a 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 comprising LH and MWA test sets.
RESULTS
Four variables in each group were selected to build LH and MWA models, respectively. LH-XGB model (AUC = 0.744) and MWA-stochastic gradient method (AUC = 0.750) model were selected for model building. In the 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 ( > 0.05). ER rate of patients whose actual treatment consistent with recommendation was lower than that of inconsistent patients (LH: 21.2% vs. 46.2%, = 0.042; MWA: 26.3% vs. 54.1%, = 0.048). By recommending optimal treatment, the hybrid model can significantly reduce ER probability from 38.2% to 25.6% for overall patients ( < 0.001).
CONCLUSIONS
The hybrid model can accurately predict ER probability of different treatments and thereby provide reliable evidence to make optimal treatment decision for patients with single 3-5-cm HCC.
背景
肿瘤复发对于接受局部治疗的肝细胞癌（HCC）患者而言是极为不利的情况。
目的
本研究旨在构建一种混合机器学习模型，以便根据早期复发（ER，≤2年）概率，为初治的单发3 - 5厘米HCC患者推荐优化的首次治疗方案（腹腔镜肝切除术[LH]或微波消融术[MWA]）。
方法
这项回顾性研究收集了来自13家医院的582例患者（LH组：300例，MWA组：282例）的20个语义变量，且对患者进行了至少24个月的随访。两组患者分别被分为训练集、验证集和测试集。使用五种算法（逻辑回归、随机森林、神经网络、随机梯度提升和极端梯度提升[XGB]）进行模型构建。在LH和MWA的验证集中，选择具有最高受试者工作特征曲线下面积（AUC）的模型连接为基于ER概率做出决策的混合模型。在包含LH和MWA测试集的综合集中进行模型测试。
结果
分别从每组中选取四个变量构建LH和MWA模型。选择LH - XGB模型（AUC = 0.744）和MWA - 随机梯度法模型（AUC = 0.750）进行模型构建。在综合集中，基于推荐治疗和实际治疗建立了治疗混淆矩阵。矩阵中各类患者的预测ER概率与实际ER率具有可比性（>0.05）。实际治疗与推荐治疗一致的患者的ER率低于不一致的患者（LH组：21.2%对46.2%，P = 0.042；MWA组：26.3%对54.1%，P = 0.048）。通过推荐优化治疗方案，混合模型可使总体患者的ER概率从38.2%显著降低至25.6%（P < 0.001）。
结论
混合模型能够准确预测不同治疗方案的ER概率，从而为单发3 - 5厘米HCC患者做出优化治疗决策提供可靠依据。
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