无线电技术
列线图
医学
队列
内科学
肿瘤科
接收机工作特性
Lasso(编程语言)
一致性
放射科
腺癌
表皮生长因子受体
曲线下面积
多元分析
比例危险模型
相关性
癌症
逻辑回归
回顾性队列研究
生物标志物
危险系数
计算机科学
万维网
作者
Shuxing Wang,Yiqing Chen,Han Zhang,Zhanhua Liang,Jun Bu
标识
DOI:10.3389/fonc.2021.707686
摘要
We developed and validated a CT-based radiomics nomogram to predict HER2 status in patients with adenocarcinoma of esophagogastric junction (AEG).A total of 101 patients with HER2-positive (n=46) and HER2-negative (n=55) esophagogastric junction adenocarcinoma (AEG) were retrospectively analyzed. They were then randomly divided into a training cohort (n=70) and a verification cohort (n=31). The radiomics features were obtained from the portal phase of the CT enhanced scan. We used the least absolute shrinkage and selection operator (LASSO) logistic regression method to select the best radiomics features in the training cohort, combined them linearly, and used the radiomics signature formula to calculate the radiomics score (Rad-score) of each AEG patient. A multivariable logistic regression method was applied to develop a prediction model that incorporated the radiomics signature and independent risk predictors. The prediction performance of the nomogram was evaluated using the training and validation cohorts.In the training (P<0.001) and verification groups (P<0.001), the radiomics signature combined with seven radiomics features was significantly correlated with HER2 status. The nomogram composed of CT-reported T stage and radiomics signature showed very good predictive performance for HER2 status. The area under the curve (AUC) of the training cohort was 0.946 (95% CI: 0.919-0.973), and that of the validation group was 0.903 (95% CI: 0.847-0.959). The calibration curve of the radiomics nomogram showed a good degree of calibration. Decision-curve analysis revealed that the radiomics nomogram was useful.The nomogram CT-based radiomics signature combined with CT-reported T stage can better predict the HER2 status of AEG before surgery. It can be used as a non-invasive prediction tool for HER2 status and is expected to guide clinical treatment decisions in clinical practice, and it can assist in the formulation of individualized treatment plans.
科研通智能强力驱动
Strongly Powered by AbleSci AI