Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features
Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features
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DOI：
10.1109/access.2014.2373335
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发表时间：
2014-01-01
期刊：
影响因子：
3.9
通讯作者：
Gillies, Robert J.
中科院分区：
文献类型：
--
作者：
Hawkins, Samuel H.;Korecki, John N.;Gillies, Robert J.
非小细胞肺癌是一种常见的疾病。它是诊断和治疗的帮助下，计算机断层扫描（CT）扫描。在本文中，我们应用放射组学从肺部CT图像中选择3-D特征，以提供预后信息。聚焦于来自较大数据集的腺癌非小细胞肺癌肿瘤亚型的病例，我们表明可以建立分类器来预测生存时间。这是第一个已知的结果，从肺癌的CT扫描中做出这样的预测。我们比较了分类器和特征选择方法。在留一交叉验证中使用决策树预测生存率时的最佳准确率为77.5%，并且是在从219个折叠中选择5个特征后获得的。
Nonsmall cell lung cancer is a prevalent disease. It is diagnosed and treated with the help of computed tomography (CT) scans. In this paper, we apply radiomics to select 3-D features from CT images of the lung toward providing prognostic information. Focusing on cases of the adenocarcinoma nonsmall cell lung cancer tumor subtype from a larger data set, we show that classifiers can be built to predict survival time. This is the first known result to make such predictions from CT scans of lung cancer. We compare classifiers and feature selection approaches. The best accuracy when predicting survival was 77.5% using a decision tree in a leave-one-out cross validation and was obtained after selecting five features per fold from 219.