About this Research Topic
The field of cancer immunology has witnessed significant progress in diagnostic capabilities due to recent breakthroughs in multispectral and hyperspectral imaging technology. The integration of artificial intelligence with imaging techniques, which possess the capability to record intricate spectral information beyond the visible spectrum, is becoming more prevalent in order to improve the early detection and diagnosis of cancer. Current imaging techniques identify immune cell numbers and densities, but lack assessment of cell-cell spatial relationships; new cross-functional Hyperspectral technologies expand our understanding of the complex interplay between tumor cells and immune infiltrate, helping identify different forms of cancer. This research topic aims to emphasize the ability of spectrum imaging techniques to detect subtle physiological changes that occur before the onset of overt disease signs. It achieves this by showcasing cutting-edge research, case studies, reviews, and creative methodologies. Researchers who are interested in exploring innovative imaging sensors, artificial intelligence algorithms for data analysis, and clinical trials that evaluate the practical efficacy and precision of these technologies in real-world medical environments are encouraged to submit their contributions.
The goal of this research topic is to tackle the crucial issue of timely and precise cancer detection, which plays a crucial role in enhancing patient outcomes. Conventional imaging techniques, however valuable, frequently exhibit limitations in their ability to identify malignancies in their first phases or accurately differentiate between benign and malignant tissues. The restrictions can be overcome by utilizing recent advancements in multispectral and hyperspectral imaging, which offer high-resolution spectral data capable of detecting biochemical and morphological alterations that are symptomatic of early-stage cancer. The purpose of this research topic is to collect and distribute research findings regarding the most recent developments in the combined utilization of imaging technologies and artificial intelligence, with the intention of improving their diagnostic capacities. We strongly encourage contributions that showcase unique applications of spectrum imaging in the field of oncology. This includes advancements in imaging technology, the creation of innovative AI algorithms to enhance analysis and interpretation, and the execution of clinical investigations. Furthermore, we highly value contributions that demonstrate the successful implementation of these technologies in clinical settings, resulting in tangible improvements in diagnostic procedures. By directing attention towards these specific domains, the study subject will make a valuable contribution towards enhancing diagnostic tools and methodologies, ultimately facilitating the timely identification and management of cancer, thus leading to life-saving interventions and enhanced patient care results.
This Research Topic titled "Recent Trends and Advancements in Multispectral and Hyperspectral Imaging for Cancer Detection" extends an invitation for submissions that demonstrate the extensive range and profound nature of contemporary advancements and implementations in this field. Our objective is to emphasize collaborative efforts that enhance the functionalities of these imaging technologies in clinical environments. Notable areas of focus encompass:
• 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.
The goal of this research topic is to tackle the crucial issue of timely and precise cancer detection, which plays a crucial role in enhancing patient outcomes. Conventional imaging techniques, however valuable, frequently exhibit limitations in their ability to identify malignancies in their first phases or accurately differentiate between benign and malignant tissues. The restrictions can be overcome by utilizing recent advancements in multispectral and hyperspectral imaging, which offer high-resolution spectral data capable of detecting biochemical and morphological alterations that are symptomatic of early-stage cancer. The purpose of this research topic is to collect and distribute research findings regarding the most recent developments in the combined utilization of imaging technologies and artificial intelligence, with the intention of improving their diagnostic capacities. We strongly encourage contributions that showcase unique applications of spectrum imaging in the field of oncology. This includes advancements in imaging technology, the creation of innovative AI algorithms to enhance analysis and interpretation, and the execution of clinical investigations. Furthermore, we highly value contributions that demonstrate the successful implementation of these technologies in clinical settings, resulting in tangible improvements in diagnostic procedures. By directing attention towards these specific domains, the study subject will make a valuable contribution towards enhancing diagnostic tools and methodologies, ultimately facilitating the timely identification and management of cancer, thus leading to life-saving interventions and enhanced patient care results.
This Research Topic titled "Recent Trends and Advancements in Multispectral and Hyperspectral Imaging for Cancer Detection" extends an invitation for submissions that demonstrate the extensive range and profound nature of contemporary advancements and implementations in this field. Our objective is to emphasize collaborative efforts that enhance the functionalities of these imaging technologies in clinical environments. Notable areas of focus encompass:
• 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.
