Dermoscopy of Lentiginous Melanomas and Equivocal Benign Pigmented Macules of the Scalp: A Case-Control Multicentric Study.

INTRODUCTION: Although the dermoscopic features of facial lentiginous melanomas (LM), including lentigo maligna and lentigo maligna melanoma, have been extensively studied, the literature about those located on the scalp is scarce. This study aims to describe the dermoscopic features of scalp LM and assess the diagnostic accuracy of dermoscopy to discriminate them from equivocal benign pigmented macules. METHODS: Consecutive cases of scalp LM and histopathology-proven benign but clinically equivocal pigmented macules (actinic keratoses, solar lentigos, seborrhoeic keratoses, and lichen planus-like keratoses) from four referral centres were included. Dermoscopic features were analysed by two blinded experts. The diagnostic performance of a predictive model was assessed. RESULTS: 56 LM and 44 controls were included. Multiple features previously described for facial and extrafacial LM were frequently identified in both groups. Expert's sensitivity to diagnose scalp LM was 76.8% (63.6-87.0) and 78.6% (65.6-88.4), with specificity of 54.5% (38.9-69.6) and 56.8% (41.0-71.7), and fair agreement (kappa coefficient 0.248). The strongest independent predictors of malignancy were (OR, 95% CI) chaos of colour (15.43, 1.48-160.3), pigmented reticular lines (14.96, 1.68-132.9), increased density of vascular network (3.45, 1.09-10.92), and perifollicular grey circles (2.89, 0.96-8.67). The predictive model achieved 85.7% (73.8-93.6) sensitivity, 61.4% (45.5-75.6) specificity, and 81.5 (73.0-90.0) area under curve to discriminate benign and malignant lesions. A diagnostic flowchart was proposed, which should improve the diagnostic performance of dermoscopy. CONCLUSION: Both facial and extrafacial dermoscopic patterns can be identified in scalp LM, with considerable overlap with benign pigmented macules, leading to low specificity and interobserver agreement on dermoscopy.

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial.

BACKGROUND: Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim was to test in the clinic whether there was equivalence between AI algorithms and clinicians for the diagnosis and management of pigmented skin lesions. METHODS: In this multicentre, prospective, diagnostic, clinical trial, we included specialist and novice clinicians and patients from two tertiary referral centres in Australia and Austria. Specialists had a specialist medical qualification related to diagnosing and managing pigmented skin lesions, whereas novices were dermatology junior doctors or registrars in trainee positions who had experience in examining and managing these lesions. Eligible patients were aged 18-99 years and had a modified Fitzpatrick I-III skin type; those in the diagnostic trial were undergoing routine excision or biopsy of one or more suspicious pigmented skin lesions bigger than 3 mm in the longest diameter, and those in the management trial had baseline total-body photographs taken within 1-4 years. We used two mobile phone-powered AI instruments incorporating a simple optical attachment: a new 7-class AI algorithm and the International Skin Imaging Collaboration (ISIC) AI algorithm, which was previously tested in a large online reader study. The reference standard for excised lesions in the diagnostic trial was histopathological examination; in the management trial, the reference standard was a descending hierarchy based on histopathological examination, comparison of baseline total-body photographs, digital monitoring, and telediagnosis. The main outcome of this study was to compare the accuracy of expert and novice diagnostic and management decisions with the two AI instruments. Possible decisions in the management trial were dismissal, biopsy, or 3-month monitoring. Decisions to monitor were considered equivalent to dismissal (scenario A) or biopsy of malignant lesions (scenario B). The trial was registered at the Australian New Zealand Clinical Trials Registry ACTRN12620000695909 (Universal trial number U1111-1251-8995). FINDINGS: The diagnostic study included 172 suspicious pigmented lesions (84 malignant) from 124 patients and the management study included 5696 pigmented lesions (18 malignant) from the whole body of 66 high-risk patients. The diagnoses of the 7-class AI algorithm were equivalent to the specialists' diagnoses (absolute accuracy difference 1·2% [95% CI -6·9 to 9·2]) and significantly superior to the novices' ones (21·5% [13·1 to 30·0]). The diagnoses of the ISIC AI algorithm were significantly inferior to the specialists' diagnoses (-11·6% [-20·3 to -3·0]) but significantly superior to the novices' ones (8·7% [-0·5 to 18·0]). The best 7-class management AI was significantly inferior to specialists' management (absolute accuracy difference in correct management decision -0·5% [95% CI -0·7 to -0·2] in scenario A and -0·4% [-0·8 to -0·05] in scenario B). Compared with the novices' management, the 7-class management AI was significantly inferior (-0·4% [-0·6 to -0·2]) in scenario A but significantly superior (0·4% [0·0 to 0·9]) in scenario B. INTERPRETATION: The mobile phone-powered AI technology is simple, practical, and accurate for the diagnosis of suspicious pigmented skin cancer in patients presenting to a specialist setting, although its usage for management decisions requires more careful execution. An AI algorithm that was superior in experimental studies was significantly inferior to specialists in a real-world scenario, suggesting that caution is needed when extrapolating results of experimental studies to clinical practice. FUNDING: MetaOptima Technology.

Dermoscopic features and screening strategies for the detection of small-diameter melanomas.

BACKGROUND: Around 70% of cutaneous malignant melanomas (MMs) develop de novo, and small-diameter or 'tiny' lesions are expected to represent the earliest manifestation of most MMs. AIM: To describe the clinical, histopathological and dermoscopic features of tiny MMs, and to investigate the impact of imaging tools, including total body photography (TBP) and sequential digital dermoscopy imaging (SDDI) in their detection. METHODS: Consecutive MMs diagnosed over 2 years in a referral centre were retrospectively included. Tiny MMs were defined as MMs with a diameter of ≤ 5 mm on dermoscopy. Dermoscopic features and the performance of four imaging methods were evaluated. RESULTS: Of the 312 MMs included, 86 (27.6%) measured ≤ 5 mm, and 44.2% of these were invasive. Tiny MMs were more frequently excised for being new and/or changing compared with nontiny MMs (77.9% vs. 50.9%; P < 0.001). Half of the tiny MMs would have been missed by the dermoscopic seven-point checklist (48.2%) or the three-point checklist (49.4%), while Menzies' method and the revised pattern analysis correctly identified respectively 65.9% and 63.5% of the tiny MMs. The most frequent positive features for tiny MMs were asymmetry in structure or colour (77.6%), brown dots (65.9%), irregular dots and globules (76.5%) and atypical pigment network (44.7%). Dermoscopic features predictive of invasion in tiny MMs were atypical vascular pattern (OR = 26.5, 95% CI 1.5-475.5, P < 0.01), shiny white lines (OR = 12.4, 95% CI 0.7-237.8, P = 0.04) and grey/blue structures (OR = 3.7, 95% CI 1.3-10.5, P = 0.01). CONCLUSION: Tiny MMs are frequently invasive and represent a clinical, dermoscopic and histopathological challenge. Dermoscopy alone has suboptimal diagnostic accuracy. Early diagnosis relies on the detection of new or changing lesions aided by TBP and SDDI.

Complex melanoma of the scalp: Diagnosis and management aided by confocal microscopy.

Melanomas of lentigo maligna subtype are a steadily growing problem and frequently represent a clinical challenge. A case is reported of a complex melanoma of the scalp illustrating the critical role of confocal microscopy for optimal diagnosis and management.