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1.
Artigo em Inglês | MEDLINE | ID: mdl-32770737

RESUMO

BACKGROUND: There is no internationally vetted set of anatomic terms to describe human surface anatomy. OBJECTIVE: To establish expert consensus on a standardized set of terms that describe clinically-relevant human surface anatomy. METHODS: We conducted a Delphi consensus on surface anatomy terminology between July 2017 and July 2019. The initial survey included 385 anatomic terms, organized in 7 levels of hierarchy. If agreement exceeded the 75% established threshold, the term was considered 'accepted' and included in the final list. Terms added by the participants were passed on to the next round of consensus. Terms with less than 75% agreement were included in subsequent surveys along with alternative terms proposed by participants until agreement was reached on all terms. RESULTS: The Delphi included 21 participants. We found consensus (≥75% agreement) on 361/385 (93.8%) terms and eliminated one term in the first round. Of 49 new terms suggested by participants, 45 were added via consensus. To adjust for a recently published ICD-ST list of terms, a third survey including 111 discrepant terms was sent to participants. Finally, a total of 513 terms reached agreement via the Delphi method. CONCLUSIONS: We have established a set of 513 clinically-relevant terms for denoting human surface anatomy, towards the use of standardized terminology in dermatologic documentation.

4.
Nat Med ; 2020 Jun 22.
Artigo em Inglês | MEDLINE | ID: mdl-32572267

RESUMO

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.

5.
J Am Acad Dermatol ; 2020 Apr 29.
Artigo em Inglês | MEDLINE | ID: mdl-32360723

RESUMO

BACKGROUND: The number needed to biopsy (NNB) ratio for melanoma diagnosis is calculated by dividing the total number of biopsies by the number of biopsied melanomas. It is the inverse of positive predictive value (PPV), which is calculated by dividing the number of biopsied melanomas by the total number of biopsies. NNB is increasingly used as a metric to compare the diagnostic accuracy of health care practitioners. OBJECTIVE: To investigate the association of NNB with the standard statistical measures of sensitivity and specificity. METHODS: We extracted published diagnostic accuracy data from 5 cross-sectional skin cancer reader studies (median [min-max] readers/study was 29 [8-511]). Because NNB is a ratio, we converted it to PPV. RESULTS: Four studies showed no association and 1 showed a negative association between PPV and sensitivity. All 5 studies showed a positive association between PPV and specificity. LIMITATIONS: Reader study data. CONCLUSIONS: An individual health care practitioner with a lower NNB is likely to have a higher specificity than one with a higher NNB, assuming they practice under similar conditions; no conclusions can be made about their relative sensitivities. We advocate for additional research to define quality metrics for melanoma detection and caution when interpreting NNB.

7.
J Am Acad Dermatol ; 2020 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-32454102

RESUMO

BACKGROUND: There is lack of uniformity in the reflectance confocal microscopy (RCM) terminology for melanocytic lesions. OBJECTIVE: To review published RCM terms for melanocytic lesions and identify redundant, synonymous terms. METHODS: A systematic review of original research articles adhering to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines was conducted until August 15, 2018. Two investigators gathered all published RCM terms used to describe melanoma and melanocytic nevi. Synonymous terms were grouped based on similarity in definition and in histopathologic correlation. RESULTS: Out of 156 full-text screened articles, 59 studies met the inclusion criteria. We identified 209 terms; 191 (91.4%) corresponding to high-magnification/cellular-level terms and 18 (8.6%) corresponding to low-magnification/architectural patterns terms. The overall average use frequency of RCM terms was 3.1 times (range, 1-31). By grouping of individual RCM terms based on likely synonymous definitions and by eliminating terms lacking clear definition, the total number of RCM terms could be potentially reduced from 209 to 40 terms (80.8% reduction). LIMITATIONS: Non-English and non-peer-reviewed articles were excluded. CONCLUSIONS: This systematic review of published RCM terms identified significant terminology redundancy. It provides the basis for subsequent terminology consensus on melanocytic neoplasms.

9.
J Am Acad Dermatol ; 82(3): 622-627, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31306724

RESUMO

BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. OBJECTIVE: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. METHODS: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. RESULTS: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. LIMITATIONS: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. CONCLUSION: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

11.
J Am Acad Dermatol ; 2019 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-31706938

RESUMO

BACKGROUND: Multiple studies have reported on dermoscopic structures in basal cell carcinoma (BCC) and its subtypes, with varying results. OBJECTIVE: To systematically review the prevalence of dermoscopic structures in BCC and its subtypes. METHODS: Databases and reference lists were searched for relevant trials according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were assessed for the relative proportion of BCC dermoscopic features. Random-effects models were used to estimate summary effect sizes. RESULTS: Included were 31 studies consisting of 5950 BCCs. The most common dermoscopic features seen in BCC were arborizing vessels (59%), shiny white structures (49%), and large blue-grey ovoid nests (34%). Arborizing vessels, ulceration, and blue-grey ovoid nests and globules were most common in nodular BCC; short-fine telangiectasia, multiple small erosions, and leaf-like, spoke wheel and concentric structures in superficial BCC; porcelain white areas and arborizing vessels in morpheaform BCC; and arborizing vessels and ulceration in infiltrative BCC. LIMITATIONS: Studies had significant heterogeneity. Studies reporting BCC histopathologic subtypes did not provide clinical data on pigmentation of lesions. CONCLUSION: In addition to arborizing vessels, shiny white structures are a common feature of BCC. A constellation of dermoscopic features may aid in differentiating between BCC histopathologic subtypes.

12.
J Am Acad Dermatol ; 2019 Jun 13.
Artigo em Inglês | MEDLINE | ID: mdl-31202873

RESUMO

BACKGROUND: Reflectance confocal microscopy (RCM) allows accurate, noninvasive, in vivo diagnosis for skin cancer. However, its impact on physicians' diagnostic confidence and management is unknown. OBJECTIVES: We sought to assess the physicians' diagnostic confidence and management before and after RCM of equivocal skin lesions. METHODS: Prospective, 2-center, observational study. During clinical practice, 7 dermatologists recorded their diagnostic confidence level (measured in a scale from 0 to 10), diagnosis, and management before and after RCM of clinically/dermoscopically equivocal lesions that raised concern for skin cancer. We also evaluated the diagnostic accuracy before and after RCM. RESULTS: We included 272 consecutive lesions from 226 individuals (mean age, 53.5 years). Diagnostic confidence increased from 6.2 to 8.1 after RCM (P < .001) when RCM confirmed or changed the diagnosis. Lesion management changed in 33.5% cases after RCM (to observation in 51 cases and to biopsy/excision in 31 cases). After RCM, the number needed to excise was 1.2. Sensitivity for malignancy before and after RCM was 78.2% and 85.1%, respectively. Specificity before and after RCM was 78.8% and 80%, respectively. LIMITATIONS: Small sample size, real-life environment, and different levels of expertise among RCM users. CONCLUSION: Physicians' diagnostic confidence and accuracy increased after RCM when evaluating equivocal tumors, frequently resulting in management changes while maintaining high diagnostic accuracy.

13.
Lancet Oncol ; 20(7): 938-947, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31201137

RESUMO

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.

14.
Semin Cutan Med Surg ; 38(1): E38-E42, 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-31051022

RESUMO

In the past decade, machine learning and artificial intelligence have made significant advancements in pattern analysis, including speech and natural language processing, image recognition, object detection, facial recognition, and action categorization. Indeed, in many of these applications, accuracy has reached or exceeded human levels of performance. Subsequently, a multitude of studies have begun to examine the application of these technologies to health care, and in particular, medical image analysis. Perhaps the most difficult subdomain involves skin imaging because of the lack of standards around imaging hardware, technique, color, and lighting conditions. In addition, unlike radiological images, skin image appearance can be significantly affected by skin tone as well as the broad range of diseases. Furthermore, automated algorithm development relies on large high-quality annotated image data sets that incorporate the breadth of this circumstantial and diagnostic variety. These issues, in combination with unique complexities regarding integrating artificial intelligence systems into a clinical workflow, have led to difficulty in using these systems to improve sensitivity and specificity of skin diagnostics in health care networks around the world. In this article, we summarize recent advancements in machine learning, with a focused perspective on the role of public challenges and data sets on the progression of these technologies in skin imaging. In addition, we highlight the remaining hurdles toward effective implementation of technologies to the clinical workflow and discuss how public challenges and data sets can catalyze the development of solutions.


Assuntos
Algoritmos , Inteligência Artificial , Benchmarking , Dermatologia , Humanos , Aprendizado de Máquina
15.
J Am Acad Dermatol ; 80(6): 1564-1584, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31010690

RESUMO

BACKGROUND: There is currently no universally adopted terminology for defining human surface anatomic location. The lack of precision, accuracy, and reliability of terms used by health care providers, in particular dermatologic surgeons, is unsatisfactory both for epidemiologic research and for high-quality patient care. OBJECTIVE: We sought to create a clinically relevant yet concise surface anatomy terminology for international use including the International Classification of Diseases and to map it to existing disparate terminologies. METHODS: Widely used surface anatomy terminology data sets and diagrams were reviewed. A Delphi consensus convened to create a novel surface anatomy terminology. The new terminology was hierarchically mapped to Systematized Nomenclature of Medicine terms and New York University numbers and physically mapped to 2-dimensional anatomic diagrams for clarity and reproducibility. RESULTS: The final terminology data set contains 519 discrete terms arranged in a 9-level hierarchy and has been adopted by the World Health Organization for the International Classification of Diseases, 11th revision. LIMITATIONS: Specification of most locations requires linking to laterality qualifiers. Fine granularity for larger sites may require the use of additional qualifiers. CONCLUSION: Consistent use of precise and accurate surface anatomy terms is crucial to the practice of dermatology, particularly procedural dermatology. The proposed terminology is designed to form the basis for evolution of a universally adoptable terminology set to improve patient care, interprovider communication, and epidemiologic tracking.


Assuntos
Pontos de Referência Anatômicos , Anatomia/normas , Terminologia como Assunto , Pontos de Referência Anatômicos/anatomia & histologia , Anatomia Artística , Humanos , Classificação Internacional de Doenças , Internacionalidade , Systematized Nomenclature of Medicine , Organização Mundial da Saúde
16.
Australas J Dermatol ; 60(4): e292-e297, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30941757

RESUMO

BACKGROUND/OBJECTIVES: High a naevus counts and atypical naevi are risk factors for cutaneous melanoma. However, many individuals with a high-risk naevus phenotype do not develop melanoma. In this study, we describe the clinical and dermoscopic attributes of naevi associated with melanoma in a high-risk naevus phenotype population. METHODS: This single-centre, hospital-based case-control study included 54 prospectively enrolled adult patients ≥18 years old with a high-risk naevus phenotype (18 cases with a history of melanoma and 36 age- and gender-matched controls without a history of melanoma). We analysed clinical and dermoscopic images of the 20 largest naevi for each participant. RESULTS: Cases had a higher mean age than controls (48.2 vs. 39.1 years, P = 0.007) but there was no difference in the male-to-female ratio between groups. Nearly, all participants (97%) were Fitzpatrick skin type II or III. Naevi in cases were more likely to be truncal, (72.6% vs. 53.6%, P = 0.01), particularly anterior truncal, (29.2% vs. 14.4%, P < 0.001) and larger than 8 mm (17.4% vs. 7.8%%, P = 0.01) compared to controls. CASH score of naevi did not differ between groups. Naevi in cases were more likely to have a multicomponent dermoscopic pattern than in controls (18.4% vs. 12.6%, P = 0.02). CONCLUSION: Larger naevi, truncal naevi, and naevi, with a multicomponent dermoscopic pattern may be risk factors for melanoma among individuals with a high-risk naevus phenotype. Further studies are needed to validate these findings.


Assuntos
Melanoma/patologia , Nevo Pigmentado/patologia , Medição de Risco , Neoplasias Cutâneas/patologia , Adulto , Estudos de Casos e Controles , Dermoscopia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fenótipo , Projetos Piloto , Estudos Prospectivos
17.
Histopathology ; 75(1): 29-38, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30791119

RESUMO

AIMS: Melanocytic naevi are benign lesions of the skin or mucosa that may constitute non-obligate precursors of malignant melanoma, particularly when they show lentiginous and dysplastic features. The aim of this study was to investigate the repertoire of somatic genetic alterations in melanocytic naevi. METHODS AND RESULTS: DNA extracted from 12 melanocytic naevi and DNA from matching normal tissue were separately microdissected and subjected to targeted massively parallel sequencing of ≥300 cancer genes. A median of 5.5 (range 1-12) non-synonymous somatic mutations were detected, with 10 cases harbouring mutually exclusive BRAF V600E (6/12) or NRAS (4/12) clonal hotspot mutations. One of the two cases lacking BRAF and NRAS mutations was a dysplastic naevus harbouring an HRAS Q61L hotspot mutation. Analysis of the laser-capture microdissected components of a naevus synchronously diagnosed with in-situ and invasive malignant melanoma revealed a truncal, clonal BRAF V600E mutation, and the acquisition of a CDKN2A homozygous deletion in the invasive component, in conjunction with additional clonal mutations affecting NF2, FAT4 and KDR in both in-situ and invasive malignant components. CONCLUSION: Melanocytic naevi harbour recurrent BRAF V600E or NRAS hotspot mutations with low mutational burdens. Our findings also show that progression from naevi to malignant melanoma may be driven by the acquisition of additional genetic alterations, including CDKN2A homozygous deletions.


Assuntos
Nevo Pigmentado/genética , Neoplasias Cutâneas/genética , Adolescente , Adulto , Idoso , Transformação Celular Neoplásica/genética , Transformação Celular Neoplásica/patologia , Análise Mutacional de DNA , DNA de Neoplasias/genética , Feminino , GTP Fosfo-Hidrolases/genética , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Masculino , Melanoma/genética , Melanoma/patologia , Proteínas de Membrana/genética , Pessoa de Meia-Idade , Mutação , Nevo Pigmentado/patologia , Proteínas Proto-Oncogênicas B-raf/genética , Proteínas Proto-Oncogênicas p21(ras)/genética , Neoplasias Cutâneas/patologia
19.
IEEE J Biomed Health Inform ; 23(2): 474-478, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30703051

RESUMO

Dermoscopy is a non-invasive skin imaging technique that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. While studies on the automated analysis of dermoscopy images date back to the late 1990s, because of various factors (lack of publicly available datasets, open-source software, computational power, etc.), the field progressed rather slowly in its first two decades. With the release of a large public dataset by the International Skin Imaging Collaboration in 2016, development of open-source software for convolutional neural networks, and the availability of inexpensive graphics processing units, dermoscopy image analysis has recently become a very active research field. In this paper, we present a brief overview of this exciting subfield of medical image analysis, primarily focusing on three aspects of it, namely, segmentation, feature extraction, and classification. We then provide future directions for researchers.


Assuntos
Dermoscopia , Interpretação de Imagem Assistida por Computador , Humanos , Melanoma/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem
20.
JAMA Dermatol ; 155(3): 347-352, 2019 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-30624578

RESUMO

Importance: Melanoma incidence and the use of systemic treatments for it are rising. Current treatment monitoring uses clinical examination and radiologic examinations; however, cutaneous involvement and cutaneous metastasis may not be well visualized. Reflectance confocal microscopy (RCM) is a US Food and Drug Administration-approved, noninvasive technology that enables visualization of the skin with quasihistological resolution. Objective: To evaluate the feasibility of using RCM to monitor advanced melanomas treated with immunotherapy. Design, Setting, and Participants: This case report study took place from March 2017 to June 2018 and included 2 patients with locally advanced melanoma who were not candidates for surgery or were not willing to have surgery and who were started on an immunotherapy regimen at a tertiary care cancer hospital. Main Outcomes and Measures: Clinical and RCM findings correlated with histopathology. Results: In the patients, locally advanced melanoma with cutaneous involvement was treated with immunotherapy (pembrolizumab in 1 patient and an ipilimumab-nivolumab combination in the other) with resulting clearance of the lesions. Use of RCM showed the disappearance of clear melanoma features seen at baseline; these findings correlated with histopathology. The response was not seen with radiologic images, such as magnetic resonance imaging and computed tomography. Conclusions and Relevance: Although RCM will not replace larger field imaging (such as magnetic resonance imaging, positron emission tomography, and computed tomography) in the management and follow-up of melanoma or other tumors, for imaging of cutaneous involvement and disease monitoring, RCM holds promise as a novel noninvasive technique.


Assuntos
Anticorpos Monoclonais Humanizados/uso terapêutico , Melanoma/tratamento farmacológico , Melanoma/patologia , Couro Cabeludo , Neoplasias Cutâneas/tratamento farmacológico , Neoplasias Cutâneas/patologia , Idoso , Biópsia por Agulha , Dermoscopia/métodos , Feminino , Seguimentos , Humanos , Imuno-Histoquímica , Imunoterapia/métodos , Masculino , Melanoma/diagnóstico por imagem , Microscopia Confocal/métodos , Pessoa de Meia-Idade , Imagem Multimodal/métodos , Invasividade Neoplásica/patologia , Estadiamento de Neoplasias , Neoplasias Cutâneas/diagnóstico por imagem , Resultado do Tratamento
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