Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy

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dc.contributor.authorCampanella, Gabriele
dc.contributor.authorNavarrete-Dechent, Cristian
dc.contributor.authorLiopyris, Konstantinos
dc.contributor.authorMonnier, Jilliana
dc.contributor.authorAleissa, Saud
dc.contributor.authorMinhas, Brahmteg
dc.contributor.authorScope, Alon
dc.contributor.authorLongo, Caterina
dc.contributor.authorGuitera, Pascale
dc.contributor.authorPellacani, Giovanni
dc.contributor.authorKose, Kivanc
dc.contributor.authorHalpern, Allan C.
dc.contributor.authorFuchs, Thomas J.
dc.contributor.authorJain, Manu
dc.date.accessioned2025-01-20T22:00:30Z
dc.date.available2025-01-20T22:00:30Z
dc.date.issued2022
dc.description.abstractBasal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the UnitedStates. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied for histopathological confirmation, thus lowering the specificity of noninvasive BCC diagnosis. Recently, reflectance confocal microscopy, a noninvasive diagnostic technique that can image skin lesions at cellular level resolution, has shown to improve specificity in BCC diagnosis and reduced the number needed to biopsy by 2-3 times. In this study, we developed and evaluated a deep learning-based artificial intelligence model to automatically detect BCC in reflectance confocal microscopy images. The proposed model achieved an area under the curve for the receiver operator characteristic curve of 89.7%(stack level) and 88.3%(lesion level), a performance on par with that of reflectance confocal microscopy experts. Furthermore, themodel achieved an area under the curve of 86.1% on a held-out test set from international collaborators, demonstrating the reproducibility and generalizability of the proposed automated diagnostic approach. These results provide a clear indication that the clinical deployment of decision support systems for the detection of BCC in reflectance confocal microscopy images has the potential for optimizing the evaluation and diagnosis of patients with skin cancer.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jid.2021.06.015
dc.identifier.eissn1523-1747
dc.identifier.issn0022-202X
dc.identifier.urihttps://doi.org/10.1016/j.jid.2021.06.015
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/93728
dc.identifier.wosidWOS:000748758900018
dc.issue.numero1
dc.language.isoen
dc.pagina.final103
dc.pagina.inicio97
dc.revistaJournal of investigative dermatology
dc.rightsacceso restringido
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleDeep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy
dc.typeartículo
dc.volumen142
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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