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Fluorescence confocal microscopy for evaluation of fresh surgical specimens and consecutive tumor cell isolation in rare pediatric tumors.

Steffen GretserM N KinzlerT M TheilenP J WildM VoglerE Gradhand
Published in: Virchows Archiv : an international journal of pathology (2024)
Fluorescence confocal microscopy (FCM) is an optical technique that uses laser light sources of different wavelengths to generate real-time images of fresh, unfixed tissue specimens. FCM allows histological evaluation of fresh tissue samples without the associated cryo artifacts after frozen sectioning. The aim of this study was to prospectively evaluate pediatric tumor specimens and assess their suitability for fresh tumor sampling. In addition, we aimed to determine whether tumor cell isolation for stable cell culture is still feasible after FCM imaging. Pediatric tumor specimens were imaged using FCM. Tumor viability and suitability for tissue sampling were evaluated and compared with H&E staining after paraffin embedding. In addition, FCM-processed and non-FCM-processed tissue samples were sent for tumor cell isolation to evaluate possible effects after FCM processing. When comparing estimated tumor cell viability using FCM and H&E, we found good to excellent correlating estimates (intraclass correlation coefficient = 0.891, p < 0.001), as well as substantial agreement in whether the tissue appeared adequate for fresh tissue collection (κ = 0.762, p < 0.001). After FCM, seven out of eight samples yielded passable cell cultures, compared to eight out of eight for non-FCM processed samples. Our study suggests that the use of FCM in tumor sampling can increase the yield of suitable fresh tumor samples by identifying viable tumor areas and ensuring that sufficient tissue remains for diagnosis. Our study also provides first evidence that the isolation and growth of tumor cells in culture are not compromised by the FCM technique.
Keyphrases
  • high resolution
  • single cell
  • stem cells
  • magnetic resonance imaging
  • young adults
  • deep learning
  • bone marrow
  • mass spectrometry
  • convolutional neural network