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Updated: Sep 1, 2025
Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones
Published on: November 30, 2016
Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from
Iakovos Amygdalos1, Enno Hachgenei2, Luisa Burkl2
1Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany. iamygdalos@ukaachen.de.
Purpose:
Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN).
Methods:
Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out.
Results:
A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively.
Conclusion:
Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.
