{"title":"用于癌症治疗的人类-人工智能共生报告。","authors":"J Harvey Turner","doi":"10.1089/cbr.2024.0216","DOIUrl":null,"url":null,"abstract":"<p><p>Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with <sup>177</sup>Lu radioligands for prostate cancer and neuroendocrine tumors is routinely given as the same arbitrary fixed administered activity to every patient. Nuclear oncology, as currently practiced with <sup>177</sup>Lu-prostate-specific membrane antigen and <sup>177</sup>Lu peptide receptor radionuclide therapy, cannot, therefore, be characterized as personalized precision medicine. The evolution of artificial intelligence (AI) could change this \"one-size-fits-all\" approach to theranostics, through development of a symbiotic relationship with physicians. Combining quantitative data collection, collation, and analytic computing power of AI algorithms with the clinical expertise, empathy, and personal care of patients by their physician envisions a new paradigm in theranostic reporting for molecular imaging and radioligand treatment of cancer. Human-AI interaction will facilitate the compilation of a comprehensive, integrated nuclear medicine report. This holistic report would incorporate radiomics to quantitatively analyze diagnostic digital imaging and prospectively calculate the radiation absorbed dose to tumor and critical normal organs. The therapy activity could then be accurately prescribed to deliver a preordained, effective, tumoricidal radiation absorbed dose to tumor, while minimizing toxicity in the particular patient. Post-therapy quantitative imaging would then validate the actual dose delivered and sequential pre- and post-treatment dosimetry each cycle would allow individual dose prescription and monitoring over the entire course of theranostic treatment. Furthermore, the nuclear medicine report would use AI analysis to predict likely clinical outcome, predicated upon AI definition of tumor molecular biology, pathology, and genomics, correlated with clinical history and laboratory data. Such synergistic comprehensive reporting will enable self-assurance of the nuclear physician who will necessarily be deemed personally responsible and accountable for the theranostic clinical outcome. Paradoxically, AI may thus be expected to enhance the practice of phronesis by the nuclear physician and foster a truly empathic trusting relationship with the cancer patient.</p>","PeriodicalId":55277,"journal":{"name":"Cancer Biotherapy and Radiopharmaceuticals","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.\",\"authors\":\"J Harvey Turner\",\"doi\":\"10.1089/cbr.2024.0216\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with <sup>177</sup>Lu radioligands for prostate cancer and neuroendocrine tumors is routinely given as the same arbitrary fixed administered activity to every patient. Nuclear oncology, as currently practiced with <sup>177</sup>Lu-prostate-specific membrane antigen and <sup>177</sup>Lu peptide receptor radionuclide therapy, cannot, therefore, be characterized as personalized precision medicine. The evolution of artificial intelligence (AI) could change this \\\"one-size-fits-all\\\" approach to theranostics, through development of a symbiotic relationship with physicians. Combining quantitative data collection, collation, and analytic computing power of AI algorithms with the clinical expertise, empathy, and personal care of patients by their physician envisions a new paradigm in theranostic reporting for molecular imaging and radioligand treatment of cancer. Human-AI interaction will facilitate the compilation of a comprehensive, integrated nuclear medicine report. This holistic report would incorporate radiomics to quantitatively analyze diagnostic digital imaging and prospectively calculate the radiation absorbed dose to tumor and critical normal organs. The therapy activity could then be accurately prescribed to deliver a preordained, effective, tumoricidal radiation absorbed dose to tumor, while minimizing toxicity in the particular patient. Post-therapy quantitative imaging would then validate the actual dose delivered and sequential pre- and post-treatment dosimetry each cycle would allow individual dose prescription and monitoring over the entire course of theranostic treatment. Furthermore, the nuclear medicine report would use AI analysis to predict likely clinical outcome, predicated upon AI definition of tumor molecular biology, pathology, and genomics, correlated with clinical history and laboratory data. Such synergistic comprehensive reporting will enable self-assurance of the nuclear physician who will necessarily be deemed personally responsible and accountable for the theranostic clinical outcome. Paradoxically, AI may thus be expected to enhance the practice of phronesis by the nuclear physician and foster a truly empathic trusting relationship with the cancer patient.</p>\",\"PeriodicalId\":55277,\"journal\":{\"name\":\"Cancer Biotherapy and Radiopharmaceuticals\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Biotherapy and Radiopharmaceuticals\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1089/cbr.2024.0216\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Biotherapy and Radiopharmaceuticals","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/cbr.2024.0216","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.
Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with 177Lu radioligands for prostate cancer and neuroendocrine tumors is routinely given as the same arbitrary fixed administered activity to every patient. Nuclear oncology, as currently practiced with 177Lu-prostate-specific membrane antigen and 177Lu peptide receptor radionuclide therapy, cannot, therefore, be characterized as personalized precision medicine. The evolution of artificial intelligence (AI) could change this "one-size-fits-all" approach to theranostics, through development of a symbiotic relationship with physicians. Combining quantitative data collection, collation, and analytic computing power of AI algorithms with the clinical expertise, empathy, and personal care of patients by their physician envisions a new paradigm in theranostic reporting for molecular imaging and radioligand treatment of cancer. Human-AI interaction will facilitate the compilation of a comprehensive, integrated nuclear medicine report. This holistic report would incorporate radiomics to quantitatively analyze diagnostic digital imaging and prospectively calculate the radiation absorbed dose to tumor and critical normal organs. The therapy activity could then be accurately prescribed to deliver a preordained, effective, tumoricidal radiation absorbed dose to tumor, while minimizing toxicity in the particular patient. Post-therapy quantitative imaging would then validate the actual dose delivered and sequential pre- and post-treatment dosimetry each cycle would allow individual dose prescription and monitoring over the entire course of theranostic treatment. Furthermore, the nuclear medicine report would use AI analysis to predict likely clinical outcome, predicated upon AI definition of tumor molecular biology, pathology, and genomics, correlated with clinical history and laboratory data. Such synergistic comprehensive reporting will enable self-assurance of the nuclear physician who will necessarily be deemed personally responsible and accountable for the theranostic clinical outcome. Paradoxically, AI may thus be expected to enhance the practice of phronesis by the nuclear physician and foster a truly empathic trusting relationship with the cancer patient.
期刊介绍:
Cancer Biotherapy and Radiopharmaceuticals is the established peer-reviewed journal, with over 25 years of cutting-edge content on innovative therapeutic investigations to ultimately improve cancer management. It is the only journal with the specific focus of cancer biotherapy and is inclusive of monoclonal antibodies, cytokine therapy, cancer gene therapy, cell-based therapies, and other forms of immunotherapies.
The Journal includes extensive reporting on advancements in radioimmunotherapy, and the use of radiopharmaceuticals and radiolabeled peptides for the development of new cancer treatments.