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- Longo, C., et al.
(author)
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Delphi Consensus Among International Experts on the Diagnosis, Management, and Surveillance for Lentigo Maligna
- 2023
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In: Dermatology Practical & Conceptual. - 2160-9381. ; 13:3
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Journal article (peer-reviewed)abstract
- Introduction: Melanoma of the lentigo maligna (LM) type is challenging. There is lack of consensus on the optimal diagnosis, treatment, and follow-up. Objectives: To obtain general consensus on the diagnosis, treatment, and follow-up for LM. Methods: A modified Delphi method was used. The invited participants were either members of the International Dermoscopy Society, academic experts, or authors of published articles relating to skin cancer and melanoma. Participants were required to respond across three rounds using a 4-point Likert scale). Consensus was defined as >75% of participants agreeing/strongly agreeing or disagreeing/strongly disagreeing. Results: Of the 31 experts invited to participate in this Delphi study, 29 participants completed Round 1 (89.9% response rate), 25/31 completed Round 2 (77.5% response rate), and 25/31 completed Round 3 (77.5% response rate). Experts agreed that LM diagnosis should be based on a clinical and dermatoscopic approach (92%) followed by a biopsy. The most appropriate primary treatment of LM was deemed to be margin-controlled surgery (83.3%), although non-surgical modalities, especially imiquimod, were commonly used either as alternative off-label primary treatment in selected patients or as adjuvant therapy following surgery; 62% participants responded life-long clinical follow-up was needed for LM. Conclusions: Clinical and histological diagnosis of LM is challenging and should be based on macroscopic, dermatoscopic, and RCM examination followed by a biopsy. Different treatment modalities and follow-up should be carefully discussed with the patient.
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- Errichetti, E., et al.
(author)
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Standardization of dermoscopic terminology and basic dermoscopic parameters to evaluate in general dermatology (non-neoplastic dermatoses): an expert consensus on behalf of the International Dermoscopy Society
- 2020
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In: British Journal of Dermatology. - : Oxford University Press (OUP). - 0007-0963 .- 1365-2133. ; 182:2, s. 454-467
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Journal article (peer-reviewed)abstract
- Background Over the last few years, several articles on dermoscopy of non-neoplastic dermatoses have been published, yet there is poor consistency in the terminology among different studies. Objectives We aimed to standardize the dermoscopic terminology and identify basic parameters to evaluate in non-neoplastic dermatoses through an expert consensus. Methods The modified Delphi method was followed, with two phases: (i) identification of a list of possible items based on a systematic literature review and (ii) selection of parameters by a panel of experts through a three-step iterative procedure (blinded e-mail interaction in rounds 1 and 3 and a face-to-face meeting in round 2). Initial panellists were recruited via e-mail from all over the world based on their expertise on dermoscopy of non-neoplastic dermatoses. Results Twenty-four international experts took part in all rounds of the consensus and 13 further international participants were also involved in round 2. Five standardized basic parameters were identified: (i) vessels (including morphology and distribution); (ii) scales (including colour and distribution); (iii) follicular findings; (iv) 'other structures' (including colour and morphology); and (v) 'specific clues'. For each of them, possible variables were selected, with a total of 31 different subitems reaching agreement at the end of the consensus (all of the 29 proposed initially plus two more added in the course of the consensus procedure). Conclusions This expert consensus provides a set of standardized basic dermoscopic parameters to follow when evaluating inflammatory, infiltrative and infectious dermatoses. This tool, if adopted by clinicians and researchers in this field, is likely to enhance the reproducibility and comparability of existing and future research findings and uniformly expand the universal knowledge on dermoscopy in general dermatology.
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- Tschandl, P., et al.
(author)
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Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study
- 2019
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In: The Lancet Oncology. - 1470-2045 .- 1474-5488. ; 20:7, s. 938-947
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Journal article (peer-reviewed)abstract
- Background: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. Methods: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. Findings: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 [SD 3·42] vs 19·92 [4·27]). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06–7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9–12·9 vs 3·6%, 0·8–6·3; p<0·0001). Interpretation: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. Funding: None. © 2019 Elsevier Ltd
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| 5. |
- Tschandl, P., et al.
(author)
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Human-computer collaboration for skin cancer recognition
- 2020
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In: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 26, s. 1229-1234
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Journal article (peer-reviewed)abstract
- The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice. A systematic evaluation of the value of AI-based decision support in skin tumor diagnosis demonstrates the superiority of human-computer collaboration over each individual approach and supports the potential of automated approaches in diagnostic medicine.
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- Claeson, Magdalena, 1976, et al.
(author)
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Clinicopathological factors associated with death from thin (<= 1 center dot 00 mm) melanoma
- 2020
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In: British Journal of Dermatology. - : Oxford University Press (OUP). - 0007-0963 .- 1365-2133. ; 182:4, s. 927-931
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Journal article (peer-reviewed)abstract
- Background Thin cutaneous melanomas (<= 1 center dot 00 mm) are increasing worldwide, causing around a quarter of all melanoma deaths in the U.S.A. and Australia. Identification of predictive factors for potentially fatal thin melanomas could allow better use of resources for follow-up. Objectives To identify the clinicopathological factors associated with fatal thin melanomas. Methods This large, nested case-case study extracted data from the population-based Queensland Cancer Registry, Australia. Our cohort consisted of Queensland residents aged 0-89 years who were diagnosed with a single, locally invasive thin melanoma (<= 1 center dot 00 mm) between 1995 and 2014. Fatal cases (eligible patients who died from melanoma) were individually matched to three nonfatal cases (eligible patients who were not known to have died from melanoma) according to sex, age, year of diagnosis and follow-up interval. Using conditional logistic regression, we calculated odds ratios (ORs) for melanoma-specific death, adjusting for all collected clinicopathological variables. Results In the cohort, 27 660 eligible patients were diagnosed with a single, thin melanoma. The final case-case series included 424 fatal cases and 1189 nonfatal cases. Fatal cases were sixfold as likely to arise on the scalp as on the back [OR 6 center dot 39, 95% confidence interval (CI) 2 center dot 57-15 center dot 92] and six times as likely to be of thickness 0 center dot 80-1 center dot 00 mm as of < 0 center dot 30 mm (OR 6 center dot 00, 95% CI 3 center dot 55-10 center dot 17). Conclusions Scalp location is a strong prognostic factor of death from thin melanoma. Further, this study provides support that melanomas with a thickness of 0 center dot 80-1 center dot 00 mm are the more hazardous thin lesions. Patients with these tumour characteristics require specific attention during follow-up. What's already known about this topic? Thin invasive melanomas (<= 1 center dot 00 mm) contribute a substantial proportion of melanoma fatalities, owing to the high volume of disease. There is a need to find prognostic factors that will better identify fatal thin melanomas at the time of diagnosis. What does this study add? In this large population-based study, fatal thin tumours were sixfold as likely to be located on the scalp as on the back. Thin melanomas of 0 center dot 80-1 center dot 00 mm thickness were six times as likely to be associated with death as tumours < 0 center dot 30 mm. Scalp location and increasing thickness are strong predictive factors of fatal thin melanomas, indicating that patients with these tumour characteristics require close follow-up.
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- Forsea, A M, et al.
(author)
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Factors driving the use of dermoscopy in Europe : a pan-European survey
- 2016
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In: British Journal of Dermatology. - : Oxford University Press (OUP). - 1365-2133 .- 0007-0963. ; 175:6, s. 1329-1337
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Journal article (peer-reviewed)abstract
- BACKGROUND: When used correctly, dermoscopy is an essential tool for helping clinicians in the diagnosis of skin diseases and the early detection of skin cancers. Despite its proven benefits, there is a lack of data about how European dermatologists use dermoscopy in everyday practice.OBJECTIVES: To identify the motivations, obstacles and modifiable factors influencing the use of dermoscopy in daily dermatology practice across Europe.METHODS: All registered dermatologists in 32 European countries were invited to complete an online survey of 20 questions regarding demographic and practice characteristics, dermoscopy training and self-confidence in dermoscopic skills, patterns of dermoscopy use, reasons for not using dermoscopy and attitudes relating to dermoscopy utility.RESULTS: We collected 7480 valid answers, of which 89% reported use of dermoscopy. The main reasons for not using dermoscopy were lack of equipment (58% of nonusers) and lack of training (42%). Dermoscopy training during residency was reported by 41% of dermoscopy users and by 12% of nonusers (P < 0·001). Dermatologists working in public hospitals were the least likely to use dermoscopy. High use of dermoscopy across the spectrum of skin diseases was reported by 62% of dermoscopy users and was associated with dermoscopy training during residency, the use of polarized light and digital dermoscopy devices, longer dermoscopy practice, younger age and female gender.CONCLUSIONS: Expanding access to dermoscopy equipment, especially in public healthcare facilities and establishing dermoscopy training during dermatology residency would further enhance the substantially high dermoscopy use across European countries.
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