Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.
Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.
复制标题
基于机器学习的放射组学预测肺腺癌表皮生长因子受体突变
DOI：
10.1155/2022/2056837
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Lu, Degan
中科院分区：
文献类型：
--
作者：
Lu, Jiameng;Ji, Xiaoqing;Wang, Lixia;Jiang, Yunxiu;Liu, Xinyi;Ma, Zhenshen;Ning, Yafei;Dong, Jie;Peng, Haiying;Sun, Fei;Guo, Zihan;Ji, Yanbo;Xing, Jianping;Lu, Yue;Lu, Degan
识别表皮生长因子受体（EGFR）突变非常重要，因为EGFR酪氨酸激酶抑制剂是EGFR突变阳性肺腺癌（LUAC）患者的一线治疗选择。本研究旨在开发和验证一种基于放射组学的机器学习（ML）方法，以识别LUAC患者的EGFR突变。我们回顾性收集了201例EGFR突变LUAC阳性患者的数据（140例在训练队列中，61例在验证队列中）。我们从预处理后的CT图像中提取了1316个放射组学特征，并通过过滤方法筛选出与突变最相关的14个放射组学特征和1个临床特征。随后，我们使用7种ML方法建立模型，并建立受试者工作特征（ROC）曲线，以评估这些模型的区分性能。在预测EGFR突变方面，来自放射组学特征的模型和组合模型（放射组学特征和相关临床因素）的AUC分别为0.79（95%置信区间（CI）：0.77-0.82）、0.86（0.87-0.88）。我们的研究提供了一个基于放射组学的ML模型，使用过滤方法检测LUAC患者的EGFR突变。这种简便、低成本的方法有助于在获取肿瘤样本进行分子检测之前对患者进行无创性识别。
Identifying an epidermal growth factor receptor (EGFR) mutation is important because EGFR tyrosine kinase inhibitors are the first-line treatment of choice for patients with EGFR mutation-positive lung adenocarcinomas (LUAC). This study is aimed at developing and validating a radiomics-based machine learning (ML) approach to identify EGFR mutations in patients with LUAC. We retrospectively collected data from 201 patients with positive EGFR mutation LUAC (140 in the training cohort and 61 in the validation cohort). We extracted 1316 radiomics features from preprocessed CT images and selected 14 radiomics features and 1 clinical feature which were most relevant to mutations through filter method. Subsequently, we built models using 7 ML approaches and established the receiver operating characteristic (ROC) curve to assess the discriminating performance of these models. In terms of predicting EGFR mutation, the model derived from radiomics features and combined models (radiomics features and relevant clinical factors) had an AUC of 0.79 (95% confidence interval (CI): 0.77-0.82), 0.86 (0.87-0.88), respectively. Our study offers a radiomics-based ML model using filter methods to detect the EGFR mutation in patients with LUAC. This convenient and low-cost method may be of help to noninvasively identify patients before obtaining tumor sample for molecule testing.
登录
查看更多内容
DOI：
10.1158/1078-0432.ccr-14-0990
发表时间：
2015-01-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
O'Connor JP;Rose CJ;Waterton JC;Carano RA;Parker GJ;Jackson A
通讯作者：
Jackson A
影响因子：
3.5
作者：
Abraham A;Pedregosa F;Eickenberg M;Gervais P;Mueller A;Kossaifi J;Gramfort A;Thirion B;Varoquaux G
通讯作者：
Varoquaux G
影响因子：
2.5
作者：
Gu, Jincui;Xu, Siqi;Zhou, Yanbin
通讯作者：
Zhou, Yanbin
影响因子：
4.7
作者：
Hong, Duo;Xu, Ke;Guo, Yan
通讯作者：
Guo, Yan
影响因子：
3
作者：
Koo, Dong-Hoe;Kim, Kyu-pyo;Kim, Sang-We
通讯作者：
Kim, Sang-We