Innovative Dual-Mode Optical Imaging System Enhances Noninvasive Skin Cancer Diagnosis

Skin cancer remains the most prevalent form of cancer worldwide, emphasizing the importance of early and accurate detection for effective treatment. Recent advancements have introduced a novel, noninvasive imaging technology that leverages dual modalities to examine skin lesions in greater detail than ever before. This cutting-edge system combines line-field confocal optical coherence tomography (LC-OCT), which produces high-resolution images at the cellular level, with confocal Raman microspectroscopy, a technique that analyzes the chemical composition of targeted skin areas. Together, these tools enable clinicians to visualize both the structure and molecular characteristics of suspicious tissues.

Developed through a collaboration between Saint-Étienne University Hospital, Paris-Saclay University, and French medical device company Damae Medical, the system underwent rigorous testing within a clinical setting over a year. More than 330 skin cancer samples, primarily nonmelanoma types like basal cell carcinoma and squamous cell carcinoma, were analyzed. Initially, LC-OCT identified abnormal structures, and subsequently, Raman microspectroscopy was used to collect over 1,300 chemical spectra from these regions. An artificial intelligence (AI) model was trained on this data to accurately differentiate cancerous tissues based on their molecular signatures.

The AI demonstrated impressive performance, with an accuracy of 95% in identifying basal cell carcinoma and 92% across multiple cancer types. These results suggest a reliable method for distinguishing skin cancers, with significant potential to improve diagnostic precision and reduce the need for invasive biopsies. Additionally, the chemical insights gained from the analysis revealed distinct differences between cancer types, offering new understanding into the biology of skin cancers.

This innovative dual-mode imaging technology could transform dermatology by enabling faster, more accurate, and less invasive skin cancer detection. Its ability to integrate structural and chemical data supports clinicians in making well-informed treatment decisions, potentially leading to better patient outcomes.