医学
接收机工作特性
队列
逻辑回归
肺癌
卷积神经网络
人工智能
无线电技术
人工神经网络
成像生物标志物
放射科
肿瘤科
内科学
磁共振成像
计算机科学
作者
Junli Tao,Changyu Liang,Ke Yin,Jiayang Fang,Bohui Chen,Zhenyu Wang,Xiaosong Lan,Jiuquan Zhang
标识
DOI:10.1016/j.diii.2022.06.002
摘要
The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning. A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22–80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS. For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70–0.82) in the training cohort and 0.80 (95% CI: 0.65–0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.
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