| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- 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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| 5. |
- Forsea, A M, et al.
(author)
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The impact of dermoscopy on melanoma detection in the practice of dermatologists in Europe : results of a pan-European survey
- 2017
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In: Journal of the European Academy of Dermatology and Venereology. - : Wiley. - 1468-3083 .- 0926-9959. ; 31:7, s. 1148-1156
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Journal article (peer-reviewed)abstract
- BACKGROUND: Dermoscopy is a widely used technique that can increase the sensitivity and specificity of melanoma detection. Information is lacking on the impact of dermoscopy use on the detection of melanoma in the real-life practice of European dermatologists.OBJECTIVE: To identify factors that influence the benefit of using dermoscopy for increasing melanoma detection and lowering the number of unnecessary biopsies in the practice of European dermatologists.METHODS: We conducted a survey of dermatologists registered in 32 European countries regarding the following: the demographic and practice characteristics, dermoscopy training and use, opinions on dermoscopy and the self-estimated impact of dermoscopy use on the number of melanomas detected and the number of unnecessary biopsies performed in practice.RESULTS: Valid answers were collected for 7480 respondents, of which 6602 reported using dermoscopy. Eighty-six per cent of dermoscopy users reported that dermoscopy increased the numbers of melanomas they detected, and 70% reported that dermoscopy decreased the number of unnecessary biopsies of benign lesions they performed. The dermatologists reporting these benefits were more likely to have received dermoscopy training during residency, to use dermoscopy frequently and intensively, and to use digital dermoscopy systems and pattern analysis compared to dermatologists who did not perceive any benefit of dermoscopy for the melanoma recognition in their practice.CONCLUSIONS: Improving dermoscopy training, especially during residency and increasing access to digital dermoscopy equipment are important paths to enhance the benefit of dermoscopy for melanoma detection in the practice of European dermatologists.
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| 6. |
- 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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| 7. |
- Forsea, Ana-Maria, et al.
(author)
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Inequalities in the patterns of dermoscopy use and training across Europe : conclusions of the Eurodermoscopy pan-European survey
- 2020
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In: European journal of dermatology : EJD. - : John Libbey Eurotext. - 1167-1122 .- 1952-4013. ; 30:5, s. 524-531
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Journal article (peer-reviewed)abstract
- BACKGROUND: Dermoscopy is a widely used technique, recommended in clinical practice guidelines worldwide for the early diagnosis of skin cancers. Intra-European disparities are reported for early detection and prognosis of skin cancers, however, no information exists about regional variation in patterns of dermoscopy use across Europe.OBJECTIVE: To evaluate the regional differences in patterns of dermoscopy use and training among European dermatologists.MATERIALS & METHODS: An online survey of European-registered dermatologists regarding dermoscopy training, practice and attitudes was established. Answers from Eastern (EE) versus Western European (WE) countries were compared and their correlation with their respective countries' gross domestic product/capita (GDPc) and total and government health expenditure/capita (THEc and GHEc) was analysed.RESULTS: We received 4,049 responses from 14 WE countries and 3,431 from 18 EE countries. A higher proportion of WE respondents reported dermoscopy use (98% vs. 77%, p<0.001) and training during residency (43% vs. 32%) or anytime (96.5% vs. 87.6%) (p<0.001) compared to EE respondents. The main obstacles in dermoscopy use were poor access to dermoscopy equipment in EE and a lack of confidence in one's skills in WE. GDPc, THEc and GHEc correlated with rate of dermoscopy use and dermoscopy training during residency (Spearman rho: 0.5-0.7, p<0.05), and inversely with availability of dermoscopy equipment.CONCLUSION: The rates and patterns of dermoscopy use vary significantly between Western and Eastern Europe, on a background of economic inequality. Regionally adapted interventions to increase access to dermoscopy equipment and training might enhance the use of this technique towards improving the early detection of skin cancers.
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| 8. |
- Liopyris, Konstantinos, et al.
(author)
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Expert agreement on the presence and spatial localization of melanocytic features in dermoscopy.
- 2023
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In: The Journal of investigative dermatology. - 1523-1747. ; 144:3
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Journal article (peer-reviewed)abstract
- Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. Herein we attempted to evaluate agreement among experts on 'exemplar images' not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least one of 31 melanocytic-specific features were submitted by 25 world experts as 'exemplars'. Using a web-based platform that allows for image mark-up of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with 8 achieving excellent agreement (Gwet's AC >0.75) and 7 of them being melanoma-specific features. These features were: 'Peppering /Granularity' (0.91); 'Shiny White Streaks' (0.89); 'Typical Pigment network' (0.83); 'Blotch Irregular' (0.82); 'Negative Network' (0.81); 'Irregular Globules' (0.78); 'Dotted Vessels' (0.77) and 'Blue Whitish Veil' (0.76). By utilizing an exemplar dataset, good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication and machine learning experiments.
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| 9. |
- Polesie, Sam, et al.
(author)
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Assessment of melanoma thickness based on dermoscopy images: an open, web-based, international, diagnostic study
- 2022
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In: Journal of the European Academy of Dermatology and Venereology. - : Wiley. - 0926-9959 .- 1468-3083. ; 36:11, s. 2002-2007
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Journal article (peer-reviewed)abstract
- Background Preoperative assessment of whether a melanoma is invasive or in situ (MIS) is a common task that might have important implications for triage, prognosis and the selection of surgical margins. Several dermoscopic features suggestive of melanoma have been described, but only a few of these are useful in differentiating MIS from invasive melanoma. Objective The primary aim of this study was to evaluate how accurately a large number of international readers, individually as well as collectively, were able to discriminate between MIS and invasive melanomas as well as estimate the Breslow thickness of invasive melanomas based on dermoscopy images. The secondary aim was to compare the accuracy of two machine learning convolutional neural networks (CNNs) and the collective reader response. Methods We conducted an open, web-based, international, diagnostic reader study using an online platform. The online challenge opened on 10 May 2021 and closed on 19 July 2021 (71 days) and was advertised through several social media channels. The investigation included, 1456 dermoscopy images of melanomas (788 MIS; 474 melanomas <= 1.0 mm and 194 >1.0 mm). A test set comprising 277 MIS and 246 invasive melanomas was used to compare readers and CNNs. Results We analysed 22 314 readings by 438 international readers. The overall accuracy (95% confidence interval) for melanoma thickness was 56.4% (55.7%-57.0%), 63.4% (62.5%-64.2%) for MIS and 71.0% (70.3%-72.1%) for invasive melanoma. Readers accurately predicted the thickness in 85.9% (85.4%-86.4%) of melanomas <= 1.0 mm (including MIS) and in 70.8% (69.2%-72.5%) of melanomas >1.0 mm. The reader collective outperformed a de novo CNN but not a pretrained CNN in differentiating MIS from invasive melanoma. Conclusions Using dermoscopy images, readers and CNNs predict melanoma thickness with fair to moderate accuracy. Readers most accurately discriminated between thin (<= 1.0 mm including MIS) and thick melanomas (>1.0 mm).
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| 10. |
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| 11. |
- Russo, Teresa, et al.
(author)
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Indications for Digital Monitoring of Patients With Multiple Nevi: Recommendations from the International Dermoscopy Society
- 2022
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In: Dermatology Practical and Conceptual. - : Mattioli1885. - 2160-9381. ; 12
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Journal article (peer-reviewed)abstract
- Introduction: In patients with multiple nevi, sequential imaging using total body skin photography (TBSP) coupled with digital dermoscopy (DD) documentation reduces unnecessary excisions and improves the early detection of melanoma. Correct patient selection is essential for optimizing the efficacy of this diagnostic approach. Objectives: The purpose of the study was to identify, via expert consensus, the best indications for TBSP and DD follow-up. Methods: This study was performed on behalf of the International Dermoscopy Society (IDS). We attained consensus by using an e-Delphi methodology. The panel of participants included international experts in dermoscopy. In each Delphi round, experts were asked to select from a list of indications for TBSP and DD. Results: Expert consensus was attained after 3 rounds of Delphi. Participants considered a total nevus count of 60 or more nevi or the presence of a CDKN2A mutation sufficient to refer the patient for digital monitoring. Patients with more than 40 nevi were only considered an indication in case of personal history of melanoma or red hair and/or a MC1R mutation or history of organ transplantation. Conclusions: Our recommendations support clinicians in choosing appropriate follow-up regimens for patients with multiple nevi and in applying the time-consuming procedure of sequential imaging more efficiently. Further studies and real-life data are needed to confirm the usefulness of this list of indications in clinical practice.
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| 12. |
- Sinz, Christoph, et al.
(author)
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Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin.
- 2017
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In: Journal of the American Academy of Dermatology. - : Elsevier BV. - 1097-6787 .- 0190-9622. ; 77:6, s. 1100-1109
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Journal article (peer-reviewed)abstract
- Nonpigmented skin cancer is common, and diagnosis with the unaided eye is error prone.To investigate whether dermatoscopy improves the diagnostic accuracy for nonpigmented (amelanotic) cutaneous neoplasms.We collected a sample of 2072 benign and malignant neoplastic lesions and inflammatory conditions and presented close-up images taken with and without dermatoscopy to 95 examiners with different levels of experience.The area under the curve was significantly higher with than without dermatoscopy (0.68 vs 0.64, P<.001). Among 51 possible diagnoses, the correct diagnosis was selected in 33.1% of cases with and 26.4% of cases without dermatoscopy (P<.001). For experts, the frequencies of correct specific diagnoses of a malignant lesion improved from 40.2% without to 51.3% with dermatoscopy. For all malignant neoplasms combined, the frequencies of appropriate management strategies increased from 78.1% without to 82.5% with dermatoscopy.The study deviated from a real-life clinical setting and was potentially affected by verification and selection bias.Dermatoscopy improves the diagnosis and management of nonpigmented skin cancer and should be used as an adjunct to examination with the unaided eye.
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| 13. |
- Tschandl, Philipp, et al.
(author)
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Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.
- 2019
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In: JAMA dermatology. - : American Medical Association (AMA). - 2168-6084 .- 2168-6068. ; 55:1, s. 58-65
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Journal article (peer-reviewed)abstract
- Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy.The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures.Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P<.001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P=.001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P=.18).Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.
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