2019/02/23 by Barbara Sarri, Sarri, Barbara, Canonge, Rafaël +18 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #AI in cancer detection #FOS: Physical sciences #Medical Physics (physics.med-ph) #Molecular Biology Techniques and Applications #Spectroscopy Techniques in Biomedical and Chemical Research #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.1902.08859
openalex publication_date 2019/02/23 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Conventional haematoxylin, eosin and saffron (HES) histopathology, currently\nthe gold-standard for pathological diagnosis of cancer, requires extensive\nsample preparations that are achieved within time scales that are not\ncompatible with intra-operative situations where quick decisions must be taken.\nProviding to pathologists a close to real-time technology revealing tissue\nstructures at the cellular level with HES histologic quality would provide an\ninvaluable tool for surgery guidance with evident clinical benefit. Here, we\nspecifically develop a stimulated Raman imaging based framework that\ndemonstrates gastro-intestinal (GI) cancer detection of unprocessed human\nsurgical specimens. The generated stimulated Raman histology (SRH) images\ncombine chemical and collagen information to mimic conventional HES\nhistopathology staining. We report excellent agreements between SRH and HES\nimages acquire on the same patients for healthy, pre-cancerous and cancerous\ncolon and pancreas tissue sections. We also develop a novel fast SRH imaging\nmodality that captures at the pixel level all the information necessary to\nprovide instantaneous SRH images. These developments pave the way for\ninstantaneous label free GI histology in an intra-operative context.\n