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Paper Details

Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.
Eur J Radiol
20
2020
ADC, Adenocarcinoma, GGO, GGOs, NSCLC, eosin, ground glass opacifications, ground glass opacity lesions, hematoxylin, lung ADC, lung ADCs, lung adenocarcinomas, lung cancers, non-small cell lung cancer, patients
Adenocarcinoma of Lung, Deep Learning, Diagnosis, Differential, Female, Humans, Lung, Lung Neoplasms, Male, Middle Aged, Predictive Value of Tests, Prognosis, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Tomography, X-Ray Computed
Author NameAffiliation
Xing WangPeking University Cancer Hospital & Institute
Li ZhangCenter for Data Science, Peking University
Xin YangPeking University Cancer Hospital & Institute
Lei TangPeking University Cancer Hospital & Institute
Jie ZhaoCenter for Data Science in Health and Medicine, Peking University
Gaoxiang ChenCenter for Data Science, Peking University
Xiang LiPeking University Cancer Hospital & Institute
Shi YanPeking University Cancer Hospital & Institute
Shaolei LiPeking University Cancer Hospital & Institute
Yue YangPeking University Cancer Hospital & Institute
Yue Kang
Quanzheng LiMGH/BWH Center for Clinical Data Science
Nan WuPeking University Cancer Hospital & Institute
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