| 1. |
- Errichetti, E., et al.
(författare)
-
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
-
Ingår i: British Journal of Dermatology. - : Oxford University Press (OUP). - 0007-0963 .- 1365-2133. ; 182:2, s. 454-467
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 2. |
- Sgouros, D., et al.
(författare)
-
Dermatoscopic features of thin (<= 2 mm Breslow thickness) vs. thick (>2 mm Breslow thickness) nodular melanoma and predictors of nodular melanoma versus nodular non-melanoma tumours: a multicentric collaborative study by the International Dermoscopy Society
- 2020
-
Ingår i: Journal of the European Academy of Dermatology and Venereology. - : Wiley. - 0926-9959 .- 1468-3083. ; 34:11, s. 2541-2547
-
Tidskriftsartikel (refereegranskat)abstract
- Background Thin nodular melanoma (NM) often lacks conspicuous melanoma-specific dermatoscopic criteria and escapes clinical detection until it progresses to a thicker and more advanced tumour. Objective To investigate the dermatoscopic morphology of thin (<= 2 mm Breslow thickness) vs. thick (>2 mm) NM and to identify dermatoscopic predictors of its differential diagnosis from other nodular tumours. Methods Retrospective, morphological case-control study, conducted on behalf of the International Dermoscopy Society. Dermatoscopic images of NM and other nodular tumours from 19 skin cancer centres worldwide were collected and analysed. Results Overall, 254 tumours were collected (69 NM of Breslow thickness <= 2 mm, 96 NM >2 mm and 89 non-melanoma nodular lesions). Light brown coloration (50.7%) and irregular brown dots/globules (42.0%) were most frequently observed in <= 2 mm NMs. Multivariate analysis revealed that dotted vessels (3.4-fold), white shiny streaks (2.9-fold) and irregular blue structureless area (2.4-fold) were predictors for thinner NM compared to non-melanoma nodular tumours. Overall, irregular blue structureless area (3.4-fold), dotted vessels (4.6-fold) and serpentine vessels (1.9-fold) were predictors of all NM compared to non-melanoma nodular lesions. Limitations Absence of a centralized, consensus pathology review and cases selected form tertiary centres maybe not reflecting the broader community. Conclusions Our study sheds light into the dermatoscopic morphology of thin NM in comparison to thicker NM and could provide useful clues for its differential diagnosis from other non-melanoma nodular tumours.
|
|
| 3. |
- Tschandl, P., et al.
(författare)
-
Human-computer collaboration for skin cancer recognition
- 2020
-
Ingår i: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 26, s. 1229-1234
-
Tidskriftsartikel (refereegranskat)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.
|
|
