Label-free multiphoton microscopy enables histopathological assessment of colorectal liver metastases and supports automated classification of neoplastic tissue
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
As the state of resection margins is an important prognostic factor after extirpation of colorectal liver metastases, surgeons aim to obtain negative margins, sometimes elaborated by resections of the positive resection plane after intraoperative frozen sections. However, this is time consuming and results sometimes remain unclear during surgery. Label-free multimodal multiphoton microscopy (MPM) is an optical technique that retrieves morpho-chemical information avoiding all staining and that can potentially be performed in real-time. Here, we investigated colorectal liver metastases and hepatic tissue using a combination of three endogenous nonlinear signals, namely: coherent anti-Stokes Raman scattering (CARS) to visualize lipids, two-photon excited fluorescence (TPEF) to visualize cellular patterns, and second harmonic generation (SHG) to visualize collagen fibers. We acquired and analyzed over forty thousand MPM images of metastatic and normal liver tissue of 106 patients. The morphological information with biochemical specificity produced by MPM allowed discriminating normal liver from metastatic tissue and discerning the tumor borders on cryosections as well as formalin-fixed bulk tissue. Furthermore, automated tissue type classification with a correct rate close to 95% was possible using a simple approach based on discriminant analysis of texture parameters. Therefore, MPM has the potential to increase the precision of resection margins in hepatic surgery of metastases without prolonging surgical intervention.
|Seiten (von - bis)||4274|
|Publikationsstatus||Veröffentlicht - 15 März 2023|
Forschungsprofillinien der TU Dresden
ASJC Scopus Sachgebiete
- Humans, Margins of Excision, Microscopy, Fluorescence, Multiphoton/methods, Liver Neoplasms, Colorectal Neoplasms