Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features

被引：84

作者：

Hawkins, Samuel H. ^{[1
]}

Korecki, John N. ^{[1
]}

Balagurunathan, Yoganand ^{[2
]}

Gu, Yuhua ^{[2
]}

Kumar, Virendra ^{[2
]}

Basu, Satrajit ^{[1
]}

Hall, Lawrence O. ^{[1
]}

Goldgof, Dmitry B. ^{[1
,2
]}

Gatenby, Robert A. ^{[2
]}

Gillies, Robert J. ^{[2
]}

机构：

[1] Univ S Florida, Dept Comp Sci & Engn, Tampa, FL 33620 USA

[2] Univ S Florida, Coll Med, H Lee Moffitt Canc Ctr & Res Inst, Dept Imaging, Tampa, FL 33612 USA

来源：

IEEE ACCESS | 2014年 / 2卷

基金：

美国国家卫生研究院;

关键词：

Computed tomography; CT 3D texture features; support vector machine; Naive Bayes; decision tree; COMPUTED-TOMOGRAPHY; PULMONARY NODULES; TEXTURE ANALYSIS; SEGMENTATION;

D O I：

10.1109/ACCESS.2014.2373335

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1418 / 1426

页数：9

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