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• W4214710143 abstract "Background Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths. The development of therapies targeting molecular alterations has significantly improved the treatment of NSCLC patients. To identify these targets, tumor phenotyping is required, with tissue biopsies and molecular pathology being the gold standard. Some patients do not respond to targeted therapies and many patients suffer from tumor recurrence, which can in part be explained by tumor heterogeneity. This points out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment to assess patient outcome. Method The contents of this review are based on a literature search conducted using the PubMed database in March 2021 and the authors’ experience. Results and Conclusion The use of radiomics and artificial intelligence-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping. Several studies show promising results for models predicting molecular alterations, with the best results being achieved by combining structural and functional imaging. Radiomics could help solve the pressing clinical need for assessing and predicting therapy response. To reach this goal, advanced tumor phenotyping, considering tumor heterogeneity, is required. This could be achieved by integrating structural and functional imaging biomarkers with clinical data sources, such as liquid biopsy results. However, to allow for radiomics-based approaches to be introduced into clinical practice, further standardization using large, multi-center datasets is required. Key points: Citation Format" @default.
• W4214710143 created "2022-03-02" @default.
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• W4214710143 date "2022-02-24" @default.
• W4214710143 modified "2023-10-14" @default.
• W4214710143 title "Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring" @default.
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• W4214710143 doi "https://doi.org/10.1055/a-1729-1516" @default.
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• W4214710143 hasPublicationYear "2022" @default.
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