The field of Quantitative Imaging (QI) is poised to revolutionize the accuracy of cancer diagnosis, prognosis, and therapy monitoring. Despite the potential impact, the widespread clinical implementation of these tools has been primarily impeded by cost inefficiencies. The National Cancer Institute (NCI) and the Radiological Society of North America (RSNA) have initiated the Quantitative Imaging Network (QIN) and Quantitative Imaging Biomarker Alliance (QIBA) to foster research for developing reliable QI tools and methods tailored for cancer applications. However, there are still ongoing debates and gaps in the field, particularly regarding the integration of artificial intelligence (AI) as a potential solution to streamline the clinical translation of QI tools and methods.
The primary aim of this research topic is to conduct a comprehensive review of successful clinical translations of QI tools/methods, current endeavors in QI tool/method development, and the future directions and roles of QI in healthcare in conjunction with AI. The research will also seek to address the existing concerns and questions surrounding the integration of AI in QI, and how these can be mitigated to enhance the efficiency and effectiveness of cancer diagnosis, prognosis, and therapy monitoring.
The scope of this research topic is confined to the development, application, and clinical translation of QI tools and methods in cancer diagnosis, prognosis, and therapy monitoring. We welcome articles addressing, but not limited to, the following themes:
- Editorial commentary discussing the potential and challenges of quantitative imaging in cancer for enhancing global healthcare.
- Review papers introducing QI tools/methods that have successfully transitioned into routine clinical trials and/or practice for cancer diagnosis, prognosis, and therapy monitoring.
- Review papers exploring the integration of AI into QI tools/methods for cancer diagnosis, prognosis, and therapy monitoring.
- Original research papers reporting current projects focusing on developing new or upgraded/updated QI tools/methods for cancer diagnosis, prognosis, and therapy monitoring, including AI tools. Please NOTE: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of the scope for this section and will not be accepted as part of this Research Topic.
The field of Quantitative Imaging (QI) is poised to revolutionize the accuracy of cancer diagnosis, prognosis, and therapy monitoring. Despite the potential impact, the widespread clinical implementation of these tools has been primarily impeded by cost inefficiencies. The National Cancer Institute (NCI) and the Radiological Society of North America (RSNA) have initiated the Quantitative Imaging Network (QIN) and Quantitative Imaging Biomarker Alliance (QIBA) to foster research for developing reliable QI tools and methods tailored for cancer applications. However, there are still ongoing debates and gaps in the field, particularly regarding the integration of artificial intelligence (AI) as a potential solution to streamline the clinical translation of QI tools and methods.
The primary aim of this research topic is to conduct a comprehensive review of successful clinical translations of QI tools/methods, current endeavors in QI tool/method development, and the future directions and roles of QI in healthcare in conjunction with AI. The research will also seek to address the existing concerns and questions surrounding the integration of AI in QI, and how these can be mitigated to enhance the efficiency and effectiveness of cancer diagnosis, prognosis, and therapy monitoring.
The scope of this research topic is confined to the development, application, and clinical translation of QI tools and methods in cancer diagnosis, prognosis, and therapy monitoring. We welcome articles addressing, but not limited to, the following themes:
- Editorial commentary discussing the potential and challenges of quantitative imaging in cancer for enhancing global healthcare.
- Review papers introducing QI tools/methods that have successfully transitioned into routine clinical trials and/or practice for cancer diagnosis, prognosis, and therapy monitoring.
- Review papers exploring the integration of AI into QI tools/methods for cancer diagnosis, prognosis, and therapy monitoring.
- Original research papers reporting current projects focusing on developing new or upgraded/updated QI tools/methods for cancer diagnosis, prognosis, and therapy monitoring, including AI tools. Please NOTE: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of the scope for this section and will not be accepted as part of this Research Topic.