Predicting Malignant Nodules from Screening CT Scans.
Predicting Malignant Nodules from Screening CT Scans.
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DOI：
10.1016/j.jtho.2016.07.002
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Gillies RJ
中科院分区：
文献类型：
--
作者：
Hawkins S;Wang H;Liu Y;Garcia A;Stringfield O;Krewer H;Li Q;Cherezov D;Gatenby RA;Balagurunathan Y;Goldgof D;Schabath MB;Hall L;Gillies RJ
确定基线时低剂量CT肺癌筛查图像的定量分析（“放射组学”）是否可以预测随后的癌症出现。来自国家肺筛查试验（ACRIN 6684）的公开数据被分为两组，分别为104名和92名筛查出肺癌（SDLC）的患者，然后与208名和196名筛查出良性肺结节（bPN）的患者进行匹配。从每个结节中提取图像特征并用于预测随后的癌症出现。最佳模型使用随机森林分类器中的23个稳定特征，可以预测1年和2年后癌变的结节，准确率分别为80% （AUC 0.83）和79% （AUC 0.75）。放射组学优于肺rads和体积。McWilliams的风险评估模型是相称的。肺癌筛查ct的放射组学基线可用于评估癌症发展的风险。
Determine if quantitative analyses (“radiomics”) of low dose CT lung cancer screening images at baseline can predict subsequent emergence of cancer. Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen detected lung cancer (SDLC), then matched to cohorts of 208 and 196 screening subjects with benign pulmonary nodules (bPN). Image features were extracted from each nodule and used to predict the subsequent emergence of cancer. The best models used 23 stable features in a Random Forest classifier, and could predict nodules that will become cancerous 1 and 2 years hence with accuracies of 80% (AUC 0.83) and 79% (AUC 0.75), respectively. Radiomics outperformed Lung-RADS and volume. McWilliams’ risk assessment model was commensurate. Radiomics of lung cancer screening CTs at baseline can be used to assess risk for development of cancer.
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DOI：
10.1109/tpami.2007.250609
发表时间：
2007-01-01
影响因子：
23.6
作者：
Banfield, Robert E.;Hall, Lawrence O.;Kegelmeyer, W. P.
通讯作者：
Kegelmeyer, W. P.
影响因子：
5
作者：
Balagurunathan, Yoganand;Gu, Yuhua;Gillies, Robert J.
通讯作者：
Gillies, Robert J.
影响因子：
39
作者：
Patz, Edward F., Jr.;Pinsky, Paul;Gatsonis, Constantine;Sicks, JoRean D.;Kramer, Barnett S.;Tammemaegi, Martin C.;Chiles, Caroline;Black, William C.;Aberle, Denise R.
通讯作者：
Aberle, Denise R.
DOI：
10.1097/jto.0b013e3181e0b977
发表时间：
2010-08
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
--
作者：
Gopal M;Abdullah SE;Grady JJ;Goodwin JS
通讯作者：
Goodwin JS
影响因子：
2.9
作者：
Alpaydin, E
通讯作者：
Alpaydin, E