ArticleJournal of cancer research and clinical oncology2023
Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- Automated Anatomical Landmark Localization in Anterior Segment OCT Images Using an Efficient Deep Learning Framework.Sensors (Basel, Switzerland) · 2026Article
- Differentiating malignancy from liver parenchyma in Ex-Vivo OCT images using anomaly detection.Scientific reports · 2026Article
- Article
- Optical Coherence Tomography Angiography, Elastography, and Attenuation Imaging for Evaluation of Liver Regeneration.Diagnostics (Basel, Switzerland) · 2025Article
- Outcome prediction after resection of colorectal cancer liver metastases: out with the old, in with the new?Hepatobiliary surgery and nutrition · 2024Article
- Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo.Journal of cancer research and clinical oncology · 2023Article
- Towards targeted colorectal cancer biopsy based on tissue morphology assessment by compression optical coherence elastography.Frontiers in oncology · 2023Article
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17 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeOptical 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).
methodsBetween 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.
resultsA 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.
conclusionOptical 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.
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