About this Research Topic
The field of cancer immunology has experienced remarkable advancements in diagnostic capabilities, largely due to the integration of multispectral and hyperspectral imaging technologies. These imaging techniques, which capture detailed spectral information beyond the visible spectrum, are increasingly being combined with artificial intelligence to enhance early cancer detection and diagnosis. Traditional imaging methods have been limited to identifying immune cell numbers and densities, often failing to assess the spatial relationships between cells. However, the advent of cross-functional hyperspectral technologies has expanded our understanding of the complex interactions between tumor cells and immune infiltrates, aiding in the identification of various cancer forms. Despite these advancements, there remains a need for further exploration into the practical application and efficacy of these technologies in real-world medical settings, as well as the development of innovative AI algorithms to enhance data analysis and interpretation.
This research topic aims to address the critical challenge of timely and accurate cancer detection, which is essential for improving patient outcomes. While conventional imaging techniques have been valuable, they often fall short in detecting malignancies at their earliest stages or in distinguishing between benign and malignant tissues. Recent advancements in multispectral and hyperspectral imaging offer a solution by providing high-resolution spectral data capable of identifying biochemical and morphological changes indicative of early-stage cancer. The goal is to compile and disseminate research findings on the latest developments in the combined use of imaging technologies and artificial intelligence, with the aim of enhancing their diagnostic capabilities. Contributions that highlight unique applications of spectrum imaging in oncology, including technological advancements, AI algorithm development, and clinical investigations, are highly encouraged.
To gather further insights in the realm of multispectral and hyperspectral imaging for cancer detection, we welcome articles addressing, but not limited to, the following themes:
- Innovations in Imaging Technology: Development of new multispectral and hyperspectral imaging systems and their components.
- AI and Machine Learning Integration: Algorithms and models for processing and analyzing imaging data, improving accuracy and diagnostic precision.
- Clinical Applications and Case Studies: Reports on the use of spectral imaging in clinical trials or routine practice, particularly for early cancer detection.
- Comparative Studies: Comparisons with traditional imaging methods to highlight advantages and challenges.
- Translational Research: Studies demonstrating the pathway from lab to clinic, including regulatory challenges and implementation strategies.
We encourage submissions of many types of publications, including original research articles, review articles, case studies, methodological advancements, secondary analyses, opinions, and perspectives/commentaries.
This research topic aims to address the critical challenge of timely and accurate cancer detection, which is essential for improving patient outcomes. While conventional imaging techniques have been valuable, they often fall short in detecting malignancies at their earliest stages or in distinguishing between benign and malignant tissues. Recent advancements in multispectral and hyperspectral imaging offer a solution by providing high-resolution spectral data capable of identifying biochemical and morphological changes indicative of early-stage cancer. The goal is to compile and disseminate research findings on the latest developments in the combined use of imaging technologies and artificial intelligence, with the aim of enhancing their diagnostic capabilities. Contributions that highlight unique applications of spectrum imaging in oncology, including technological advancements, AI algorithm development, and clinical investigations, are highly encouraged.
To gather further insights in the realm of multispectral and hyperspectral imaging for cancer detection, we welcome articles addressing, but not limited to, the following themes:
- Innovations in Imaging Technology: Development of new multispectral and hyperspectral imaging systems and their components.
- AI and Machine Learning Integration: Algorithms and models for processing and analyzing imaging data, improving accuracy and diagnostic precision.
- Clinical Applications and Case Studies: Reports on the use of spectral imaging in clinical trials or routine practice, particularly for early cancer detection.
- Comparative Studies: Comparisons with traditional imaging methods to highlight advantages and challenges.
- Translational Research: Studies demonstrating the pathway from lab to clinic, including regulatory challenges and implementation strategies.
We encourage submissions of many types of publications, including original research articles, review articles, case studies, methodological advancements, secondary analyses, opinions, and perspectives/commentaries.
Keywords: Hyperspectral Imaging, Multispectral Imaging, Artificial Intelligence in Diagnostics, Non-invasive Cancer Detection, Precision Medicine Applications, Machine Learning, Narrow-band Imaging
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
