{"title":"From organs to algorithms: Redefining cancer classification in the age of artificial intelligence","authors":"Sean Khozin","doi":"10.1111/cts.70001","DOIUrl":null,"url":null,"abstract":"<p>Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI-driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70001","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cts-Clinical and Translational Science","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cts.70001","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI-driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.
期刊介绍:
Clinical and Translational Science (CTS), an official journal of the American Society for Clinical Pharmacology and Therapeutics, highlights original translational medicine research that helps bridge laboratory discoveries with the diagnosis and treatment of human disease. Translational medicine is a multi-faceted discipline with a focus on translational therapeutics. In a broad sense, translational medicine bridges across the discovery, development, regulation, and utilization spectrum. Research may appear as Full Articles, Brief Reports, Commentaries, Phase Forwards (clinical trials), Reviews, or Tutorials. CTS also includes invited didactic content that covers the connections between clinical pharmacology and translational medicine. Best-in-class methodologies and best practices are also welcomed as Tutorials. These additional features provide context for research articles and facilitate understanding for a wide array of individuals interested in clinical and translational science. CTS welcomes high quality, scientifically sound, original manuscripts focused on clinical pharmacology and translational science, including animal, in vitro, in silico, and clinical studies supporting the breadth of drug discovery, development, regulation and clinical use of both traditional drugs and innovative modalities.