The Impact of Melanoma Imaging Biomarker Cues on Detection Sensitivity and Specificity in Melanoma versus Clinically Atypical Nevi

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dc.contributor.authorAguero, Rosario
dc.contributor.authorBuchanan, Kendall L.
dc.contributor.authorNavarrete-Dechent, Cristian
dc.contributor.authorMarghoob, Ashfaq A.
dc.contributor.authorStein, Jennifer A.
dc.contributor.authorLandy, Michael S.
dc.contributor.authorLeachman, Sancy A.
dc.contributor.authorLinden, Kenneth G.
dc.contributor.authorGarcet, Sandra
dc.contributor.authorKrueger, James G.
dc.contributor.authorGareau, Daniel S.
dc.date.accessioned2025-01-20T16:08:48Z
dc.date.available2025-01-20T16:08:48Z
dc.date.issued2024
dc.description.abstractSimple Summary Early detection of melanoma and differentiation from benign nevi can be challenging even for the most experienced dermatologists. To improve melanoma detection, artificial intelligence algorithms incorporating dermoscopy have been developed, but lack transparency and therefore have limited training value for healthcare providers. To address this, an automated approach utilizing imaging biomarker cues (IBCs), logical features extracted from images that mimic expert dermatologists' dermoscopic pattern recognition skills, was developed. This study excluded deep learning approaches to which IBCs are complementary or alternative. Ten participants assessed 78 dermoscopic images (39 melanomas and 39 nevi) first without IBCs and then with IBCs. Using IBCs significantly improved diagnostic accuracy: sensitivity increased significantly from 73.69% to 81.57% (p = 0.0051) and specificity increased from 60.50% to 67.25% (p = 0.059). These results indicate that incorporating IBCs can significantly enhance melanoma diagnosis, with potential implications for improved screening practices. Further research is needed to confirm these findings across a variety of healthcare providers.Abstract Incorporation of dermoscopy and artificial intelligence (AI) is improving healthcare professionals' ability to diagnose melanoma earlier, but these algorithms often suffer from a "black box" issue, where decision-making processes are not transparent, limiting their utility for training healthcare providers. To address this, an automated approach for generating melanoma imaging biomarker cues (IBCs), which mimics the screening cues used by expert dermoscopists, was developed. This study created a one-minute learning environment where dermatologists adopted a sensory cue integration algorithm to combine a single IBC with a risk score built on many IBCs, then immediately tested their performance in differentiating melanoma from benign nevi. Ten participants evaluated 78 dermoscopic images, comprised of 39 melanomas and 39 nevi, first without IBCs and then with IBCs. Participants classified each image as melanoma or nevus in both experimental conditions, enabling direct comparative analysis through paired data. With IBCs, average sensitivity improved significantly from 73.69% to 81.57% (p = 0.0051), and the average specificity improved from 60.50% to 67.25% (p = 0.059) for the diagnosis of melanoma. The index of discriminability (d ') increased significantly by 0.47 (p = 0.002). Therefore, the incorporation of IBCs can significantly improve physicians' sensitivity in melanoma diagnosis. While more research is needed to validate this approach across other healthcare providers, its use may positively impact melanoma screening practices.
dc.fuente.origenWOS
dc.identifier.doi10.3390/cancers16173077
dc.identifier.eissn2072-6694
dc.identifier.urihttps://doi.org/10.3390/cancers16173077
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90107
dc.identifier.wosidWOS:001311077200001
dc.issue.numero17
dc.language.isoen
dc.revistaCancers
dc.rightsacceso restringido
dc.subjectdermoscopy
dc.subjectartificial intelligence
dc.subjectmelanoma
dc.subjectimaging biomarkers
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleThe Impact of Melanoma Imaging Biomarker Cues on Detection Sensitivity and Specificity in Melanoma versus Clinically Atypical Nevi
dc.typeartículo
dc.volumen16
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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