{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:27:20Z","timestamp":1781605640149,"version":"3.54.5"},"reference-count":22,"publisher":"Wiley","issue":"19","license":[{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Full\u2010field optical coherence tomography combined with dynamic cell imaging (D\u2010FFOCT) is a new, simple\u2010to\u2010use, nondestructive, quick technique that can provide sufficient spatial resolution to mimic histopathological analysis. The objective of this study was to evaluate diagnostic performance of D\u2010FFOCT for one\u2010stop rapid diagnosis breast clinic.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Dynamic full\u2010field optical coherence tomography was applied to fresh, untreated breast and nodes biopsies. Four different readers (senior and junior radiologist, surgeon, and pathologist) analyzed the samples without knowing final histological diagnosis or American College of Radiology classification. The results were compared to conventional processing and staining (hematoxylin\u2013eosin).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 217 biopsies were performed on 152 patients. There were 144 breast biopsies and 61 lymph nodes with 101 infiltrative cancers (49.27%), 99 benign lesions (48.29%), 3 ductal in situ carcinoma (1.46%), and 2 atypias (0.98%). The diagnostic performance results were as follow: sensitivity: 77% [0.7;0.82], specificity: 64% [0.58;0.71], PPV: 74% [0.68;0.78], and NPV: 75% [0.72;0.78]. A large image atlas was created as well as a diagnosis algorithm from the readers' experience.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>With 74% PPV and 75% NPV, D\u2010FFOCT is not yet ready to be used in clinical practice to identify breast cancer. This is mainly explained by the lack of experience and knowledge of this new technic by the four lectors. By training with the diagnosis algorithm and the image atlas, radiologists could have better outcomes allowing quick detection of breast cancer and lymph node involvement. Deep learning could also be used, and further investigation will follow.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.6560","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:55:25Z","timestamp":1695981325000},"page":"19500-19511","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Value of high\u2010resolution full\u2010field optical coherence tomography and dynamic cell imaging for one\u2010stop rapid diagnosis breast clinic"],"prefix":"10.1002","volume":"12","author":[{"given":"Alexis","family":"Simon","sequence":"first","affiliation":[{"name":"Department of Radiology, Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Assistance Publique\u2010H\u00f4pitaux de Paris (AP\u2010HP)  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasmina","family":"Badachi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Assistance Publique\u2010H\u00f4pitaux de Paris (AP\u2010HP)  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacques","family":"Ropers","sequence":"additional","affiliation":[{"name":"Clinical Research Unit, Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Assistance Publique\u2010H\u00f4pitaux de Paris (AP\u2010HP)  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaura","family":"Laurent","sequence":"additional","affiliation":[{"name":"Clinical Research Unit, Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Assistance Publique\u2010H\u00f4pitaux de Paris (AP\u2010HP)  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lida","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Pathology, Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Assistance Publique\u2010H\u00f4pitaux de Paris (AP\u2010HP)  Paris 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DalimierE DurkinJR BocearaC Olivo\u2010MarinJC Meas\u2010YedidV.Basal cell carcinoma detection in full field OCT images using convolutional neural networks.2018. IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 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