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Key OCT parameters like the attenuation coefficient, <jats:italic>R<\/jats:italic>\n<jats:sup>2<\/jats:sup>, and RMSE, extracted through exponential fitting, were incorporated into machine learning, achieving 96.9% accuracy and an AUC\u2010ROC of 0.99 in 10\u2010fold cross\u2010validation. Raman spectroscopy revealed differences in carotenoid, amide\u2010I, and CH<jats:sub>2<\/jats:sub>\u2013CH<jats:sub>3<\/jats:sub> structures between melanoma and nevi, supporting the OCT findings. Autofluorescence background intensity variations further distinguished lesion types and enhanced lesion assessment. Future work will include the investigation of larger patient groups and the combination of both data sets in a combined algorithm. Also, the integration of both modalities and the developed method with photoacoustic tomography and high\u2010frequency ultrasound appears beneficial toward achieving an optical biopsy of melanoma skin cancer and improving diagnostics.<\/jats:p>","DOI":"10.1002\/jbio.70040","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T18:59:03Z","timestamp":1745261943000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["In Vivo Differentiation of Cutaneous Melanoma From Benign Nevi With Dual\u2010Modal System of Optical Coherence\u00a0Tomography and Raman Spectroscopy"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5919-7010","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"first","affiliation":[{"name":"Hannover Centre for Optical Technologies Leibniz University Hannover  Hanover 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