Radiomics in Nasopharyngeal Carcinoma.
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to d...
| Publicado en: | Clinical Medicine Insights: Oncology pp. 1 - 11 |
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| Autores principales: | , , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2/24/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155438041&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155438041 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795549 B3KT jtl: Clinical Medicine Insights: Oncology issn: 11795549 maglogo: Y pubinfo: dt: 2/24/2022 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 155438041 155438041 155438041 10.1177/11795549221079186 155438041 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics in Nasopharyngeal Carcinoma. aug: au: Duan, Wenyue Xiong, Bingdi Tian, Ting Zou, Xinyun He, Zhennan Zhang, Ling affil: College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China sug: subj: Nasopharyngeal Carcinoma Diagnosis Tomography, X-Ray Computed Magnetic Resonance Imaging Nasopharyngeal Carcinoma Prognosis Nasopharyngeal Carcinoma Therapy Cancer Patients Risk Assessment Algorithms Tumor Markers, Biological Neoplasm Grading Machine Learning ab: Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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