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For patients with suspected lung cancer, fast and accurate tissue diagnosis is important for optimal treatment allocation. Currently, multiple biopsies are taken, without any feedback on the biopsy quality. Immediate histopathological feedback has the potential to improve the biopsy quality and to reduce the total number of biopsies, thereby potentially reducing adverse events and repeated diagnostic procedures, and prevent delay in start oncological treatment.
A promising imaging technique for rapid histopathological feedback on lung biopsies is third and second harmonic generation (THG/SHG) and two-photon excited autofluorescence (2PEF) microscopy, which is non-invasive, label-free and provides 3D images with a high, sub-cellular resolution, within seconds. In a previous study, we showed that using THG/SHG/2PEF microscopy, we could successfully reveal alveolar structures and histopathology hallmarks of unprocessed lung tissue, including cell morphology and general tissue architecture (collagen and elastin organization) [1].
Here, we used for the first time, to the best of our knowledge, a compact, mobile THG/SHG/2PEF microscope (Flash Pathology B.V.) in the clinic to image fresh bronchoscopic lung biopsies. So far, we imaged 75 biopsies of 34 patients, each within a few minutes. Independent lung pathologists assessed the lung biopsies based on the THG/SHG/2PEF images, determining the biopsy representativity and the histopathological diagnosis, which were compared with the standard histopathological assessment of these biopsies. In this way, we tested the ability of the microscope to provide fast feedback to the endoscopist. In addition, we show the added value of using deep learning algorithms for rapid assessment of bronchoscopic lung biopsies.
[1] L.M.G. van Huizen, et al. Translational Biophotonics (2020)
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