A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation.

Lin, Fan-Ya; Chang, Yeun-Chung; Huang, Hsuan-Yu; Li, Chia-Chen; Chen, Yi-Chang; Chen, Chung-Ming

Lin, Fan-Ya; Chang, Yeun-Chung; Huang, Hsuan-Yu; Li, Chia-Chen; Chen, Yi-Chang; Chen, Chung-Ming.

Affiliation

Lin FY; Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan.
Chang YC; Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
Huang HY; IOIP Taiwan CO., Ltd, Taipei, Taiwan.
Li CC; Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan.
Chen YC; Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan.
Chen CM; Department of Medical Imaging, Cardinal Tien Hospital, New Taipei City, Taiwan.

Eur Radiol ; 32(6): 3767-3777, 2022 Jun.

Article in En | MEDLINE | ID: mdl-35020016

Subject(s)

Lung Neoplasms; Solitary Pulmonary Nodule; Algorithms; Humans; Lung/pathology; Lung Neoplasms/diagnostic imaging; Lung Neoplasms/pathology; Radiographic Image Interpretation, Computer-Assisted/methods; Solitary Pulmonary Nodule/diagnostic imaging; Tomography, X-Ray Computed/methods

Key words

Deep learning; Lung; Machine learning; Multiple pulmonary nodules; X-ray computed tomography

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Full text: 1 Database: MEDLINE Main subject: Solitary Pulmonary Nodule / Lung Neoplasms Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Year: 2022 Type: Article

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