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This study investigates the potential of optical coherence tomography (OCT) combined with anomaly detection for differentiating hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA) and CRLM from normal liver parenchyma, ex-vivo. Our dataset comprises 173 OCT images sourced from 69 patients undergoing liver surgery. We leveraged pre-trained neural networks with frozen weights and statistical outlier modeling to train an anomaly detection model using only non-cancer parenchyma scans. Given the small-scale nature of the dataset and the presence of label uncertainty, a stratified cross-validation procedure was employed to robustly assess the model\u2019s performance in accurately matching OCT scans with their corresponding histological diagnoses. This resulted in promising classification performance using a pre-trained Vision Transformer: sensitivity 80%, specificity 78%, accuracy 79%, and area under the receiving-operating-characteristic-curve (ROC-AUC) of 81%. While limited by a relatively small and noisy dataset, this study highlights the promising potential of OCT combined with anomaly detection for intraoperative liver cancer detection. This semi-supervised learning approach offers several advantages, including reduced training time and data requirements, as well as interpretable anomaly scores.<\/jats:p>","DOI":"10.1038\/s41598-026-54850-0","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:22:35Z","timestamp":1781068955000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Differentiating malignancy from liver parenchyma in Ex-Vivo OCT images using anomaly detection"],"prefix":"10.1038","volume":"16","author":[{"given":"Ulrich","family":"Krispel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Pamler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Girmen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niels","family":"K\u00f6nig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Schmitt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Eggeling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00f6ren","family":"B\u00fcsker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Beetz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Oldhafer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Truhn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian W. 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