规范化(社会学)
医学
接收机工作特性
神经组阅片室
放射科
无线电技术
肺癌
腺癌
人工智能
癌症
肿瘤科
内科学
计算机科学
社会学
精神科
人类学
神经学
作者
Doohyun Park,Daejoong Oh,MyungHoon Lee,Shin Yup Lee,Kyung Min Shin,Johnson SG Jun,Dosik Hwang
标识
DOI:10.1007/s00330-022-08869-2
摘要
To analyze whether CT image normalization can improve 3-year recurrence-free survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) relative to the use of unnormalized CT images. A total of 106 patients with NSCLC were included in the training set. For each patient, 851 radiomic features were extracted from the normalized and the unnormalized CT images, respectively. After the feature selection, random forest models were constructed with selected radiomic features and clinical features. The models were then externally validated in the test set consisting of 79 patients with NSCLC. The model using normalized CT images yielded better performance than the model using unnormalized CT images (with an area under the receiver operating characteristic curve of 0.802 vs 0.702, p = 0.01), with the model performing especially well among patients with adenocarcinoma (with an area under the receiver operating characteristic curve of 0.880 vs 0.720, p < 0.01). CT image normalization may improve prediction performance among patients with NSCLC, especially for patients with adenocarcinoma. • After CT image normalization, more radiomic features were able to be identified. • Prognostic performance in patients was improved significantly after CT image normalization compared with before the CT image normalization. • The improvement in prognostic performance following CT image normalization was superior in patients with adenocarcinoma.
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