A Systematic Review of Diagnoses with Rosettes Under Dermoscopy.

INTRODUCTION: Rosettes are a cluster of shiny white dots in the shape of a four-leaf clover seen under polarized dermoscopic light. Historically, rosettes were primarily reported in actinic keratoses and squamous cell carcinoma. However, rosettes have also been reported in other conditions. OBJECTIVES: The objective of this systematic review to elucidate the breadth of diagnoses exhibiting this unique dermoscopic phenomenon. METHODS: A review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Literature searches were performed in MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials and Web of Science, as well as a manual search of the reference lists of screened articles. RESULTS: A total of 73 articles met the inclusion criteria. Out of these, 47 distinct diagnoses with rosette were identified. Among neoplastic conditions, keratinizing neoplasms had the highest number of articles reported (N = 19). Discoid lupus was the most commonly reported diagnosis within the inflammatory category (N = 6). Molluscum contagiosum was the predominant diagnosis among infectious entities (N = 3), while acroangiodermatitis was the sole diagnosis reported in the vascular category (N = 1). CONCLUSIONS: These findings confirm rosettes are not specific to keratinocytic growths and are observed in a wide range of conditions. Knowledge of the breadth of conditions with rosettes may aid clinicians when developing a differential diagnosis of a growth or an eruption with rosettes under dermoscopy.

Use of an elastic-scattering spectroscopy and artificial intelligence device in the assessment of lesions suggestive of skin cancer: A comparative effectiveness study.

Background: Skin cancer is the most common form of cancer worldwide. As artificial intelligence (AI) expands its scope within dermatology, leveraging technology may aid skin cancer detection. Objective: To assess the safety and effectiveness of an elastic-scattering spectroscopy (ESS) device in evaluating lesions suggestive of skin cancer. Methods: This prospective, multicenter clinical validation study was conducted at 4 US investigational sites. Patients with skin lesions suggestive of melanoma and nonmelanoma skin cancers were clinically assessed by expert dermatologists and evaluated by a device using AI algorithms comparing current ESS lesion readings with training data sets. Statistical analyses included sensitivity, specificity, AUROC, negative predictive value (NPV), and positive predictive value (PPV). Results: Overall device sensitivity was 97.04%, with subgroup sensitivity of 96.67% for melanoma, 97.22% for basal cell carcinoma, and 97.01% for squamous cell carcinoma. No statistically significant difference was found between the device and dermatologist performance (P = .8203). Overall specificity of the device was 26.22%. Overall NPV of the device was 89.58% and PPV was 57.54%. Conclusion: The ESS device demonstrated high sensitivity in detecting skin cancer. Use of this device may assist primary care clinicians in assessing suspicious lesions, potentially reducing skin cancer morbidity and mortality through expedited and enhanced detection and intervention.

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

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.