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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine
Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images en...
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Published in: | Cancers 2022-06, Vol.14 (12), p.2860 |
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creator | Saxena, Sanjay Jena, Biswajit Gupta, Neha Das, Suchismita Sarmah, Deepaneeta Bhattacharya, Pallab Nath, Tanmay Paul, Sudip Fouda, Mostafa M. Kalra, Manudeep Saba, Luca Pareek, Gyan Suri, Jasjit S. |
description | Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles. |
doi_str_mv | 10.3390/cancers14122860 |
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Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers14122860</identifier><identifier>PMID: 35740526</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Artificial intelligence ; Biomarkers ; Brain research ; Cancer ; Clinical aspects ; Clinical decision making ; Computer applications ; Deep learning ; Discriminant analysis ; Genomes ; Genomics ; Machine learning ; Mammography ; Medical imaging ; Medical prognosis ; Medical research ; Neural networks ; Oncology ; Patients ; Phenotypes ; Precision medicine ; Prediction models ; Probability distribution ; Prognosis ; Radiomics ; Review ; Survival ; Tumors</subject><ispartof>Cancers, 2022-06, Vol.14 (12), p.2860</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c398t-117a68aef382e2af200a57a899ea7d76c1cc04c4e0cc0eb229ad084d5e4bdda73</citedby><cites>FETCH-LOGICAL-c398t-117a68aef382e2af200a57a899ea7d76c1cc04c4e0cc0eb229ad084d5e4bdda73</cites><orcidid>0000-0002-2092-7159 ; 0000-0003-2221-6526 ; 0000-0003-1790-8640 ; 0000-0002-8288-1010</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2679682042/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2679682042?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,44566,53766,53768,74869</link.rule.ids></links><search><creatorcontrib>Saxena, Sanjay</creatorcontrib><creatorcontrib>Jena, Biswajit</creatorcontrib><creatorcontrib>Gupta, Neha</creatorcontrib><creatorcontrib>Das, Suchismita</creatorcontrib><creatorcontrib>Sarmah, Deepaneeta</creatorcontrib><creatorcontrib>Bhattacharya, Pallab</creatorcontrib><creatorcontrib>Nath, Tanmay</creatorcontrib><creatorcontrib>Paul, Sudip</creatorcontrib><creatorcontrib>Fouda, Mostafa M.</creatorcontrib><creatorcontrib>Kalra, Manudeep</creatorcontrib><creatorcontrib>Saba, Luca</creatorcontrib><creatorcontrib>Pareek, Gyan</creatorcontrib><creatorcontrib>Suri, Jasjit S.</creatorcontrib><title>Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine</title><title>Cancers</title><description>Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. 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subjects | Artificial intelligence Biomarkers Brain research Cancer Clinical aspects Clinical decision making Computer applications Deep learning Discriminant analysis Genomes Genomics Machine learning Mammography Medical imaging Medical prognosis Medical research Neural networks Oncology Patients Phenotypes Precision medicine Prediction models Probability distribution Prognosis Radiomics Review Survival Tumors |
title | Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine |
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