{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:35:12Z","timestamp":1773264912374,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute, USA","doi-asserted-by":"crossref","award":["R44 CA273961"],"award-info":[{"award-number":["R44 CA273961"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The heterogeneous nature of cancer with varying degrees of fat, necrosis, fibrosis, and varying degrees of tissue repair severely impacts the success of acquiring adequate tissue samples during percutaneous image-guided biopsy. Although ultrasound or CT fluoroscopy are used to identify tumor location and thus to guide biopsy needle insertion, these technologies do not provide the necessary resolution to determine tissue composition and enable the selection of the most appropriate location for biopsy specimen extraction. As a result, biopsy must be repeated, leading to significant cost to the health care system. Methods: In this study, we introduce a combined optical imaging\/artificial intelligence (OI\/AI) methodology for the real-time assessment of tissue morphology at the tip of the biopsy needle, prior to the collection of a biopsy specimen. Addressing a significant clinical challenge, this approach aims to reduce the proportion of biopsy cores\u2014currently as high as 40%\u2014that yield low diagnostic value due to elevated adipose or low tumor content. Our methodology employs micron-scale optical coherence tomography (OCT) imaging to obtain detailed structural tissue information using a minimally invasive needle probe. The OCT images are automatically analyzed using a convolutional neural network (CNN)-driven AI software developed by our team. A U-net style architecture was used to segment regions of tumor from the OCT scans. U-Net is a specialized convolutional neural network (CNN) architecture designed for fast, precise image segmentation, which involves classifying each pixel in an image to outline objects. This streamlined approach shows promise to provide clinicians with real-time results, supporting more accurate and informed decisions regarding biopsy site selection. To evaluate this technology, we conducted a clinical study using a custom-made OCT imager and recorded OCT images from patients diagnosed with liver cancers. Expert OCT interpreters supplied annotated reference images that were used to train a custom AI algorithm. Results: OCT imaging with ~10 mm axial and 20 mm lateral resolution enabled the collection of high-quality images of the tissue. The AI analysis was performed offline. UNet achieved an AUC of ~0.877 on the validation dataset, indicating promising performance for the relatively small data set used to train the model. The AI model matched human interpretations approximately 90% of the time, highlighting its promise for making biopsy procedures both more accurate and more efficient. Conclusions: A novel OCT instrument and AI software were evaluated for assessing tissue composition at the tip of biopsy needle. The OCT instrument produced micron-scale resolution images of the tissue, enabling AI analysis and accurate real-time discrimination of tissue type. This preliminary study demonstrated the clinical potential of this technology for improving biopsy success.<\/jats:p>","DOI":"10.3390\/diagnostics16050811","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:27:18Z","timestamp":1773062838000},"page":"811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Assisted OCT Imaging for Core Needle Biopsy Guidance: The 1st in Humans Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7713-6972","authenticated-orcid":false,"given":"Nicusor","family":"Iftimia","sequence":"first","affiliation":[{"name":"Physical Sciences Inc., Andover, MA 01810, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Poonam","family":"Yadav","sequence":"additional","affiliation":[{"name":"MD Anderson Cancer Center, Houston, TX 77030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0501-9563","authenticated-orcid":false,"given":"Michael","family":"Primrose","sequence":"additional","affiliation":[{"name":"Physical Sciences Inc., Andover, MA 01810, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gopi","family":"Maguluri","sequence":"additional","affiliation":[{"name":"Physical Sciences Inc., Andover, MA 01810, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jack","family":"Jones","sequence":"additional","affiliation":[{"name":"Physical Sciences Inc., Andover, MA 01810, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Grimble","sequence":"additional","affiliation":[{"name":"Physical Sciences Inc., Andover, MA 01810, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9615-8985","authenticated-orcid":false,"given":"Rahul Anil","family":"Sheth","sequence":"additional","affiliation":[{"name":"MD Anderson Cancer Center, Houston, TX 77030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1038\/nrc3261","article-title":"Intra-tumour heterogeneity: A looking glass for cancer?","volume":"12","author":"Marusyk","year":"2012","journal-title":"Nat. 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