Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer
Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer
复制标题
应用放射组学提取脾脏特征预测胃癌患者预后
DOI：
10.1016/j.ejso.2020.06.021
复制
发表时间：
2020-10-01
期刊：
影响因子：
3.8
通讯作者：
Shen, Xian
中科院分区：
文献类型：
--
作者：
Wang, Xiang;Sun, Jing;Shen, Xian
简介：放射组学允许挖掘成像数据以非侵入性地检查组织特征，其可用于预测患者的预后。本研究旨在探讨利用影像学技术评估脾脏组织特征以预测胃癌患者的预后。材料与方法：回顾性收集胃癌患者的CT图像。随机分组训练组患者的脾脏图像特征，提取pyradiomics。选择P值< 0.1的特征进行套索回归以构建生存风险模型。建立了高风险和低风险群体的模型。结果：训练组和验证组的脾脏特征性预后模型具有一致性（p < 0.001和p = 0.016），训练组和验证组的脾脏特征性预后模型具有一致性（p < 0.001和p = 0.016）。两组显示不同脾脏特征的患者，除肿瘤淋巴结转移（pTNM）分期（p = 0.007）外，其他基本数据无统计学差异。生存危险因素的单变量和多变量分析显示，脾脏特征（p = 0.042）、年龄（p < 0.001）、肿瘤位置（p = 0.002）和pTNM分期（p < 0.001）是生存的独立危险因素。结论：影像学技术提取的脾脏特征能够准确预测胃癌患者的长期生存。脾脏特征分组能有效提高生存预测的准确性和胃癌预后。(C)2020年爱思唯尔有限公司，BASO类似于癌症外科协会和欧洲外科肿瘤学会。All rights reserved.
Introduction: Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer.Materials and methods: Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value < 0.1 were selected for lasso regression to construct a survival risk model. Models for high-and low-risk groups were established. Patients were divided into the high- and low-risk groups for univariate and multivariate regression analysis of survival-related factors, and a visual prognostic prediction model was established.Results: The splenic characteristic prognostic model was consistent in the training and verification groups (p < 0.001 and p = 0.016, respectively). The two groups that displayed different splenic characteristics showed no statistical difference in other basic data except the tumour-node-metastasis (pTNM) stage (p = 0.007). Univariate and multivariate analysis of survival risk factors showed that splenic characteristics (p = 0.042), age (p < 0.001), tumor location (p = 0.002), and pTNM stage (p < 0.001) were independent risk factors for survival. The prognostic prediction model combined with splenic characteristics significantly improved the accuracy of prognosis, predicting one-and three-year survival rates.Conclusion: Splenic features extracted from imaging technology can accurately predict the long-term survival of patients with gastric cancer. Splenic characteristic grouping can effectively improve the accuracy of survival prediction and gastric cancer prognosis. (C) 2020 Elsevier Ltd, BASO similar to The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.