Lyndsay Harris, Lalitha K Shankar, Claire Hildebrandt, Wendy S Rubinstein, Kristofor Langlais, Henry Rodriguez, Adam Berger, John Freymann, Erich P Huang, P Mickey Williams, Jean Claude Zenklusen, Robert Ochs, Zivana Tezak, Berkman Sahiner
{"title":"加快新一代测序和放射组学临床应用的资源需求:研讨会评论和综述。","authors":"Lyndsay Harris, Lalitha K Shankar, Claire Hildebrandt, Wendy S Rubinstein, Kristofor Langlais, Henry Rodriguez, Adam Berger, John Freymann, Erich P Huang, P Mickey Williams, Jean Claude Zenklusen, Robert Ochs, Zivana Tezak, Berkman Sahiner","doi":"10.1093/jnci/djae136","DOIUrl":null,"url":null,"abstract":"<p><p>The National Institutes of Health-US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of Health-Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the \"ground truth\" gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.</p>","PeriodicalId":14809,"journal":{"name":"JNCI Journal of the National Cancer Institute","volume":" ","pages":"1562-1570"},"PeriodicalIF":9.9000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Resource requirements to accelerate clinical applications of next-generation sequencing and radiomics: workshop commentary and review.\",\"authors\":\"Lyndsay Harris, Lalitha K Shankar, Claire Hildebrandt, Wendy S Rubinstein, Kristofor Langlais, Henry Rodriguez, Adam Berger, John Freymann, Erich P Huang, P Mickey Williams, Jean Claude Zenklusen, Robert Ochs, Zivana Tezak, Berkman Sahiner\",\"doi\":\"10.1093/jnci/djae136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The National Institutes of Health-US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of Health-Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the \\\"ground truth\\\" gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.</p>\",\"PeriodicalId\":14809,\"journal\":{\"name\":\"JNCI Journal of the National Cancer Institute\",\"volume\":\" \",\"pages\":\"1562-1570\"},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JNCI Journal of the National Cancer Institute\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/jnci/djae136\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JNCI Journal of the National Cancer Institute","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/jnci/djae136","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Resource requirements to accelerate clinical applications of next-generation sequencing and radiomics: workshop commentary and review.
The National Institutes of Health-US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of Health-Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the "ground truth" gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.
期刊介绍:
The Journal of the National Cancer Institute is a reputable publication that undergoes a peer-review process. It is available in both print (ISSN: 0027-8874) and online (ISSN: 1460-2105) formats, with 12 issues released annually. The journal's primary aim is to disseminate innovative and important discoveries in the field of cancer research, with specific emphasis on clinical, epidemiologic, behavioral, and health outcomes studies. Authors are encouraged to submit reviews, minireviews, and commentaries. The journal ensures that submitted manuscripts undergo a rigorous and expedited review to publish scientifically and medically significant findings in a timely manner.