Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China.
Department of Radiology, Fujian Provincial Cancer Hospital, Fuzhou, Fujian, 350014, China.
BMC Pulm Med. 2023 Sep 11;23(1):339. doi: 10.1186/s12890-023-02609-y.
The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images.
This retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram.
Three demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619-0.843), 0.810(95%CI, 0.696-0.907), 0.841(95%CI, 0.743-0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130.
The nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application.
本研究旨在基于非增强 CT 肺图像开发一种预测肺腺癌 T790M 突变的放射组学列线图。
这项回顾性研究回顾了 215 例 T790M 基因检测结果为阳性的肺腺癌患者的人口统计学数据和肺 CT 图像。215 例患者（包括 52 例阳性）分为训练集（n=150，36 例阳性）和独立测试集（n=65，16 例阳性）。采用多变量逻辑回归选择人口统计学数据和 CT 语义特征来构建临床模型。我们从病变的感兴趣体积（VOI）中提取定量特征，并使用不同的特征选择算法和分类器开发放射组学模型。通过在训练集上进行 5 折交叉验证策略来训练模型，并在测试集上进行评估。ROC 用于估计临床模型、放射组学模型和合并列线图的性能。
确定了三个人口统计学特征（性别、吸烟、肺气肿）和十个放射组学特征（Kruskal-Wallis 作为选择算法，LASSO 逻辑回归作为分类器）来构建模型。测试集中临床模型、放射组学模型和列线图的 AUC 分别为 0.742（95%CI，0.619-0.843）、0.810（95%CI，0.696-0.907）和 0.841（95%CI，0.743-0.938）。列线图的预测效能优于临床模型（p=0.042）。列线图预测 T790M 突变的截断值为 0.69，评分高于 130。
本研究开发的列线图是一种非侵入性、方便、经济的预测肺腺癌 T790M 突变的方法，具有良好的临床应用前景。
