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Abstract
Introduction/Background Cervical cancer is a significant health concern, often requiring multiple visits and invasive procedures for diagnosis and treatment. Our study proposes the integration of a revolutionary fiber-based robotic instrument with Rapid Evaporative Ionisation Mass Spectrometry (REIMS) technology to enhance diagnostic accuracy and streamline treatment processes. Previous studies using the intelligent knife (iKnife) demonstrated promising results, but limitations prompted the development of a minimally destructive robotic fiber-laser system. The objective is to offer bedside automated diagnosis, optimize treatment feedback, and replace the need for punch biopsies.
Methodology Our case-control cross-sectional study involves a two-fold investigation—ex vivo and in vivo. The ex vivo study utilizes 200 cervical tissue samples, analyzed with OPO and CO2 lasers. LD-REIMS profiles guide segmentation, identifying areas with pathology. The in vivo study involves the application of the robotic instrument during colposcopy and surgical interventions. Histologically validated spectral databases for normal, precancerous, and cancerous tissues will be constructed.
Results Preliminary data from the ex vivo study demonstrate that LD-REIMS profiles effectively identify pathological areas. Initial segmentation based on OPO and CO2 laser analyses reveals promising outcomes. The technology shows potential in discriminating between the presence of precancer (CIN) and normal tissue, as well as distinguishing high-grade precancer (CIN2+) from low-grade precancer (CIN1-).
Conclusion Our study presents a novel approach to cervical disease diagnosis and treatment using a fiber-based robotic instrument with REIMS technology. The preliminary ex vivo results indicate the potential for accurate discrimination between normal and precancerous tissues. The ongoing in vivo study aims to optimize real-time information during surgical interventions and colposcopy. Successful implementation may revolutionize cervical pathology assessment, offering a one-stop clinic solution, reducing patient anxiety, and optimizing treatment outcomes. Further research will focus on biomarker discovery and pathway identification for comprehensive clinical application.
Disclosures Authors declare no conflicts of interest with any commercial entities, and the study adheres to ethical guidelines and regulatory standards. Financial support for the research is provided by institutional grants and collaborative partnerships. The authors are committed to transparent reporting of findings and encourage collaboration for the advancement of cervical disease diagnosis and treatment technologies.
Cervical tissue ion images and tissue segmentation based on the LD-REIMS profiles and ion intensities. LD-REIMS was able to distinguish cancerous tissue from normal (top) and stratify precancer - CIN1, CIN2 (bottom)