The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer
Over the last four decades, lung cancer has been the leading cause of death in the United States. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and historically, treatment consists of surgical resection, chemotherapy, and/or radiotherapy. Over the past decade, targeted i...
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| Format: | Article |
| Language: | English |
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Open Exploration Publishing Inc.
2025-06-01
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| Series: | Exploration of Digital Health Technologies |
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| Online Access: | https://www.explorationpub.com/uploads/Article/A101153/101153.pdf |
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| _version_ | 1849335786580738048 |
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| author | Brandon Wilkins Emily Hartman Blake Kelley Pranali Pachika Joshua Bradley James Bradley |
| author_facet | Brandon Wilkins Emily Hartman Blake Kelley Pranali Pachika Joshua Bradley James Bradley |
| author_sort | Brandon Wilkins |
| collection | DOAJ |
| description | Over the last four decades, lung cancer has been the leading cause of death in the United States. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and historically, treatment consists of surgical resection, chemotherapy, and/or radiotherapy. Over the past decade, targeted immunotherapy has improved overall survival and treatment response. However, immunotherapy is expensive, and only select patients respond to immunotherapy. Recently, there has been much interest in using biomarkers to better identify and predict which patients will respond to therapy. There is much hope that the combined use of artificial intelligence (AI) and omics-based technology will provide enhanced capability to predict response to immunotherapy in patients with NSCLC. We performed a literature review and summarized the various approaches in which AI has been integrated with genomics, radiomics, pathomics, metabolomics, immunogenomics, and breathomics to better understand the tumor immune microenvironment and predict response to immunotherapy. |
| format | Article |
| id | doaj-art-bf796034cf6243b09b34c2f20423b5a5 |
| institution | Kabale University |
| issn | 2996-9409 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Open Exploration Publishing Inc. |
| record_format | Article |
| series | Exploration of Digital Health Technologies |
| spelling | doaj-art-bf796034cf6243b09b34c2f20423b5a52025-08-20T03:45:10ZengOpen Exploration Publishing Inc.Exploration of Digital Health Technologies2996-94092025-06-01310115310.37349/edht.2025.101153The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancerBrandon Wilkins0Emily Hartman1Blake Kelley2Pranali Pachika3Joshua Bradley4James Bradley5https://orcid.org/0000-0001-5570-4679Department of Internal Medicine, University of Louisville, Louisville, KY 40202, USADepartment of Internal Medicine, University of Louisville, Louisville, KY 40202, USADepartment of Internal Medicine, University of Louisville, Louisville, KY 40202, USADepartment of Hematology and Oncology, University of Louisville, Louisville, KY 40202, USADivision of Strategic Missions and Technologies, Microsoft Inc., Redmond, WA 98052, USALouisville Pulmonary Care, Louisville, KY 40207, USAOver the last four decades, lung cancer has been the leading cause of death in the United States. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and historically, treatment consists of surgical resection, chemotherapy, and/or radiotherapy. Over the past decade, targeted immunotherapy has improved overall survival and treatment response. However, immunotherapy is expensive, and only select patients respond to immunotherapy. Recently, there has been much interest in using biomarkers to better identify and predict which patients will respond to therapy. There is much hope that the combined use of artificial intelligence (AI) and omics-based technology will provide enhanced capability to predict response to immunotherapy in patients with NSCLC. We performed a literature review and summarized the various approaches in which AI has been integrated with genomics, radiomics, pathomics, metabolomics, immunogenomics, and breathomics to better understand the tumor immune microenvironment and predict response to immunotherapy.https://www.explorationpub.com/uploads/Article/A101153/101153.pdfainsclcmachine learningbiomarkerlung cancer |
| spellingShingle | Brandon Wilkins Emily Hartman Blake Kelley Pranali Pachika Joshua Bradley James Bradley The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer Exploration of Digital Health Technologies ai nsclc machine learning biomarker lung cancer |
| title | The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer |
| title_full | The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer |
| title_fullStr | The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer |
| title_full_unstemmed | The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer |
| title_short | The impact of artificial intelligence on a multi-omics approach toward predictive biomarkers for non-small cell lung cancer |
| title_sort | impact of artificial intelligence on a multi omics approach toward predictive biomarkers for non small cell lung cancer |
| topic | ai nsclc machine learning biomarker lung cancer |
| url | https://www.explorationpub.com/uploads/Article/A101153/101153.pdf |
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