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Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education.
Wu, Yun-Ju; Wu, Fu-Zong; Yang, Shu-Ching; Tang, En-Kuei; Liang, Chia-Hao.
Afiliação
  • Wu YJ; Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 80201, Taiwan.
  • Wu FZ; Institute of Education, National Sun Yat-Sen University, 70, Lien-Hai Road, Kaohsiung 804241, Taiwan.
  • Yang SC; Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan.
  • Tang EK; Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
  • Liang CH; Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
Diagnostics (Basel) ; 12(5)2022 Apr 24.
Article em En | MEDLINE | ID: mdl-35626220
Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient-doctor cooperation and shared decision making.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Diagnostics (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Taiwan

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Diagnostics (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Taiwan