Spitzoid Melanoma: Dermoscopic Report and Diagnostic Discussion

Tullia Cuzzi. Instituto Nacional de Infectologia Evandro Chagas. Documento produzido em parceria ou por autor vinculado à Fiocruz, mas não consta a informação no documento.Submitted by Repositório Arca ([email protected]) on 2019-04-24T16:40:49Z No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Approved for entry into archive by Janaína Nascimento ([email protected]) on 2019-11-01T14:53:32Z (GMT) No. of bitstreams: 2 ve_Campos-do-Carmo_Gabriella_etal_INI_2012.pdf: 460800 bytes, checksum: 517b1db3299fccfeffd9c9e3051d61fe (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Made available in DSpace on 2019-11-01T14:53:32Z (GMT). No. of bitstreams: 2 ve_Campos-do-Carmo_Gabriella_etal_INI_2012.pdf: 460800 bytes, checksum: 517b1db3299fccfeffd9c9e3051d61fe (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2012Federal University of Rio de Janeiro. Sector of Dermatology. Rio de Janeiro, RJ, Brazil.Federal University of Rio de Janeiro. Sector of Dermatology. Rio de Janeiro, RJ, Brazil.Fluminense Federal University. Department of Pathology. Niterói, RJ, Brazil.Federal University of Rio de Janeiro. Sector of Pathology. Rio de Janeiro, RJ, Brazil.We present a case of a 16-year-old young man who came for a dermatologic appointment due to acne. He presented a pigmented asymptomatic lesion on the back of his right thigh. Dermoscopic examination revealed uncommon aspects, highly suspect of nodular melanoma, in particular a blue-whitish veil, striae and asymmetric globules. The lesion was promptly removed and the material referred for histopathologic examination. Microscopic findings showed an atypical spitzoid tumor, compatible with spitzoid melanoma. In this report, the importance of dermoscopy as an auxiliary method in the early diagnosis of cutaneous melanomas is emphasized. Its daily use by the dermatologist is an important tool in the decision-making process in cases of urgent removal of suspect lesions

Primary synchronic melanomas: dermoscopic aspects

Synchronic melanomas are rare and poorly described in the literature. They are classified in this way when a second melanoma is observed on the first examination or up to three months after the first diagnosis. An unusual case and the dermoscopic examination of a patient with two primary synchronic melanomas is described. The dermatoscopy is a very useful tool in the early diagnosis of melanomas. Some dermatoscopy patterns suggestive of melanomas on the face are asymmetric pigmented anexiais openings, rhomboidal structures and blue-gray globules and dots. The dermatoscopy characteristic pattern of melanoma in the region of plant is the default in parallel ridges that has high sensitivity and specificity in detecting acrais melanomas.Melanomas sincrônicos são raros e pouco descritos na literatura. São classificados dessa maneira quando um segundo melanoma é observado no primeiro exame ou até três meses após o primeiro diagnóstico. Descrevemos um inusitado caso e o exame dermatoscópico de um paciente com dois melanomas primários sincrônicos. A dermatoscopia é uma ferramenta muito útil no diagnóstico precoce dos melanomas. Alguns padrões dermatoscópicos sugestivos de melanomas na face são aberturas anexiais assimétricas pigmentadas, estruturas romboidais e pontos e glóbulos cinza-azulados. Já na região plantar, o padrão dermatoscópico característico de melanoma é o padrão em cristas paralelas que apresenta alta sensibilidade e especificidade na detecção de melanomas acrais

Dermatoscopic Findings of Seborrheic Keratosis in Melanoma

Cutaneous melanoma may in some instances be confused with seborrheic keratosis, which is a very common neoplasia, more often mistaken for actinic keratosis and verruca vulgaris. Melanoma may clinically resemble seborrheic keratosis and should be considered as its possible clinical simulator.  We report a case of melanoma with dermatoscopic characteristics of seborrheic keratosis and emphasize the importance of the dermatoscopy algorithm in differentiating between a melanocytic and a non-melanocytic lesion, of the excisional biopsy for the establishment of the diagnosis of cutaneous tumors, and of the histopathologic examination in all surgically removed samples.</p

Collision skin lesions-results of a multicenter study of the International Dermoscopy Society (IDS)

Background: Collision lesions as two independent and unrelated skin tumors often manifest an atypical morphology. Objective: To determine the combinations of collision skin lesions (CSLs). Methods: Twenty-one pigmented lesion clinics in nine countries included 77 histopathologically proven CSLs in this retrospective observational study. Results: Seventy-seven CSLs from 75 patients (median age 59.8 years) were analyzed; 24.7% of CSLs were located on the head and neck area, 5.2% on the upper extremities, 48.1% on the trunk, and 11.7% on the lower extremities; 40.3% revealed a melanocytic component (median age 54.7 years), followed by 45.5% with a basal cell carcinoma (BCC) (median age 62.4 years) and 11.7% with a seborrheic keratosis (median age 64.7 years). CSLs with a BCC component were more often found on the head and neck area compared to tumors with a melanocytic component (34.3% versus 16.1%). Lesions with a melanocytic component were more often detected on the trunk compared to lesions with a BCC (64.5% versus 37.1%). Patients with CSLs with epidermal-epidermal cell combination were older than patients with epidermal-dermal cell combination (63 versus 55.2 years), were more often male than female (63% versus 43.3%), more often had the lesion on the head and neck area (32.6% versus 13.3%), and less often on the upper (2.2 % versus 10%) or lower extremities (8.7% versus 16.6%). Conclusions: CSLs consist of a heterogeneous group of lesions of varying cell types. They are associated with advancing age and cumulative UV-exposure. CSLs manifest a complex morphology making it challenging to diagnose correctly

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost