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1.
BMJ Case Rep ; 14(2)2021 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-33526526

RESUMO

Pilomatrixoma is a benign subcutaneous tumour arising from the sebaceous glands. Mutation in the CTNNB1 gene is seen, suggesting beta-catenin misregulation may be the cause of pilomatrixoma. The preoperative diagnosis may be improved by the awareness of the fact that pilomatrixoma is a common and benign skin tumour of the head and neck region. It presents as a well-defined mass, which may be firm to hard in consistency, usually attached to the skin, but not to the underlying tissue. The colour of overlying skin appears a reddish-brown tinge, indicating that it could be a case of pilomatrixoma. Here, we report a case of pilomatrixoma of the cheek in a woman along with the CT findings and histopathological appearances. Dental surgeons should consider it as one of the differential diagnosis in superficial head and neck swelling with calcification.


Assuntos
Calcinose/diagnóstico por imagem , Doenças do Cabelo/diagnóstico por imagem , Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Pilomatrixoma/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Adulto , Calcinose/patologia , Feminino , Doenças do Cabelo/patologia , Doenças do Cabelo/cirurgia , Neoplasias de Cabeça e Pescoço/patologia , Neoplasias de Cabeça e Pescoço/cirurgia , Humanos , Pilomatrixoma/patologia , Pilomatrixoma/cirurgia , Neoplasias Cutâneas/patologia , Neoplasias Cutâneas/cirurgia , Tomografia Computadorizada por Raios X
2.
Anticancer Res ; 41(2): 895-903, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33517295

RESUMO

BACKGROUND/AIM: This study analysed the prevalence of the characteristics evaluated in dermatoscopy for melanocytic infiltrations of the conjunctiva with various degrees of malignancy. PATIENTS AND METHODS: A total of 160 conjunctival pigmented lesions were studied. Each lesion was scored using dermatoscopic patterns and the characteristics of malignancy described by Kittler. Also, the Authors' own clues were added to the evaluation. RESULTS: In melanomas, the following characteristics were identified: asymmetry of the pattern and colour, larger average number of colours, the presence of grey colour, structureless area, polymorphic vessels and feeder vessels. A pattern of black dots and a black colour was typical of malignant lesions and pre-cancerous (premalignant) lesions - primary acquired melanosis (PAM) with atypia. Cysts were observed only in the group of naevi. CONCLUSION: The patterns evaluated with dermatoscopy are present in pigmented lesions of the conjunctiva. There are, however, some characteristics which allow differentiation between melanoma and pigmented naevus and melanosis and also between PAM.


Assuntos
Neoplasias da Túnica Conjuntiva/diagnóstico por imagem , Melanoma/diagnóstico por imagem , Nevo Pigmentado/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Criança , Neoplasias da Túnica Conjuntiva/patologia , Dermoscopia , Feminino , Humanos , Masculino , Melanoma/patologia , Pessoa de Meia-Idade , Nevo Pigmentado/patologia , Neoplasias Cutâneas/patologia , Adulto Jovem
3.
Sci Data ; 8(1): 34, 2021 01 28.
Artigo em Inglês | MEDLINE | ID: mdl-33510154

RESUMO

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.


Assuntos
Melanoma , Neoplasias Cutâneas , Inteligência Artificial , Humanos , Melanoma/diagnóstico por imagem , Melanoma/patologia , Melanoma/fisiopatologia , Metadados , Pele/patologia , Neoplasias Cutâneas/diagnóstico por imagem , Neoplasias Cutâneas/patologia , Neoplasias Cutâneas/fisiopatologia
4.
Zhonghua Zhong Liu Za Zhi ; 43(1): 147-154, 2021 Jan 23.
Artigo em Chinês | MEDLINE | ID: mdl-33472329

RESUMO

Objective: To explore the application of sentinel lymph node biopsy (SLNB) and its prognostic value in the treatment of acral melanoma. Methods: We retrospective analyzed 118 patients who underwent sentinel lymph node biopsy from Mar 2012 to Jun 2019 with effective follow-up data available in our institute. We ruled out palpable regional lymph node metastasis with preoperative imaging of MRI and ultrasonography, used the (99)Tc(m)-Dextran (Dx) as a tracer, with intraoperative γ-ray probe positioning for SLN capture. Wide resection and reconstruction in primary lesion followed by complete lymph node dissection were underwent SLN positive patients. Cox regression model were used to analyze the prognostic factors. Results: The patients had an average disease history of 53.6 months (2-360 months), the primary lesion located at hands and feet in 84 cases, while 27 cases were subungual and 7 cases were cutaneous. The mean Breslow depth was 3.6 mm, and 72 cases (61.0%) combined with ulceration. The average number of SLN was 2.8, the SLN positive rate was 24.6% (29/118), and the false-negative rate was 2.5% (3/118). There were 24 cases (20.3%) developed clinically positive metastasis, including 7 cases displayed distant metastasis combined with lymph node metastasis (5.9%), 8 cases with clinically positive lymph node metastasis alone (6.8%), and 9 cases with distant metastasis (7.6%). There were 33 patients in stage Ⅰ, 56 patients in stage Ⅱ and 29 patients in stage Ⅲ, with a 5-years overall survival rate of 69.5%. The Breslow depth is an independent risk factor of SLN positive. While Breslow depth, SLN status, SLN positive number and clinically detectable metastasis are independent prognostic factors of the overall survival (P<0.05). Conclusions: Patients without clinically positive regional lymph node metastasis under imaging and physical examinations, SLNB can provide accurate pathologic staging and play an accurate prediction role in the prognostic evaluation. SLNB should be carried out routinely in clinical practice.


Assuntos
Melanoma , Linfonodo Sentinela , Neoplasias Cutâneas , Humanos , Excisão de Linfonodo , Linfonodos/diagnóstico por imagem , Linfonodos/cirurgia , Melanoma/diagnóstico por imagem , Melanoma/cirurgia , Prognóstico , Estudos Retrospectivos , Biópsia de Linfonodo Sentinela , Neoplasias Cutâneas/diagnóstico por imagem , Neoplasias Cutâneas/cirurgia
5.
Ultrasonics ; 110: 106268, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33068826

RESUMO

The segmentation of cancer-suspicious skin lesions using ultrasound may help their differential diagnosis and treatment planning. Active contour models (ACM) require an initial seed, which when manually chosen may cause variations in segmentation accuracy. Fully-automated skin segmentation typically employs layer-by-layer segmentation using a combination of methods; however, such segmentation has not yet been applied on cancerous lesions. In the current work, fully automated segmentation is achieved in two steps: an automated seeding (AS) step using a layer-by-layer method followed by a growing step using an ACM. The method was tested on images of nevi, melanomas, and basal cell carcinomas from two ultrasound imaging systems (N=60), with all lesions being successfully located. For the seeding step, manual seeding (MS) was used as a reference. AS approached the accuracy of MS when the latter used an optimal bounding rectangle based on the ground truth (Sørensen-Dice coefficient (SDC) of 72.3 vs 74.6, respectively). The effect of varying the manual seed was also investigated; a 0.7 decrease in seed height and width caused a mean SDC of 54.6. The results show the robustness of automated seeding for skin lesion segmentation.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Neoplasias Cutâneas/diagnóstico por imagem , Ultrassonografia/métodos , Diagnóstico Diferencial , Humanos
7.
Vestn Oftalmol ; 136(6): 32-41, 2020.
Artigo em Russo | MEDLINE | ID: mdl-33084277

RESUMO

Tumor borders are one of the most significant characteristics of any tumor, including that of the skin. PURPOSE: To compare histological borders of periorbital skin tumors with their autofluorescence borders built from the analysis of non-induced protoporphyrin IX autofluorescence. MATERIAL AND METHODS: The study group included 8 patients with skin tumors of the eyelids, periorbital region, eyebrow and zygomatic regions aged 54-88 years. The tumors varied in size from 2 to 8 mm and all displayed signs of basal cell carcinoma (BCC). At admission, all the patients underwent non-induced autofluorescence diagnosis. The images were processed with the «CancerPlot¼ program. During radio excision, the autofluorescent border of each neoplasm was marked with a surgical incision of about 5 mm long and 2 mm deep. RESULTS: Upon pathomorphological examination, solid BCC was identified in 7 cases. The remaining case was senile keratosis. All reference incisions were located in healthy tissues not farther than 1 mm from the tumor (or keratosis locus, correspondingly). CONCLUSION: By the example of facial BCC, an evident correlation was established between histological borders of the tumor and its native (non-induced) protoporphyrin IX autofluorescence.


Assuntos
Carcinoma Basocelular , Neoplasias Cutâneas , Idoso , Idoso de 80 Anos ou mais , Carcinoma Basocelular/diagnóstico por imagem , Pálpebras , Humanos , Pessoa de Meia-Idade , Pele , Neoplasias Cutâneas/diagnóstico por imagem
8.
An Bras Dermatol ; 95(6): 748-750, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33041156

RESUMO

Polypoid melanoma is a variant of nodular melanoma, whose poor prognosis depends on its thickness and the presence of ulceration at the time of diagnosis. The authors report two cases of polypoid melanoma, presenting as broad, cauliflower-like, polypoid masses. Dermoscopy was characterized by a multicolored pattern, atypical polymorphic vessels, and the fiber sign. Clinical and dermoscopic features can help to diagnose polypoid melanoma and exclude other possible differential diagnoses. However, histology remains mandatory to confirm the diagnostic suspicion.


Assuntos
Brassica , Melanoma , Neoplasias Cutâneas , Dermoscopia , Diagnóstico Diferencial , Humanos , Melanoma/diagnóstico por imagem , Pele , Neoplasias Cutâneas/diagnóstico por imagem
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1524-1527, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018281

RESUMO

Developing a fast and accurate classifier is an important part of a computer-aided diagnosis system for skin cancer. Melanoma is the most dangerous form of skin cancer which has a high mortality rate. Early detection and prognosis of melanoma can improve survival rates. In this paper, we propose a deep convolutional neural network for automated melanoma detection that is scalable to accommodate a variety of hardware and software constraints. Dermoscopic skin images collected from open sources were used for training the network. The trained network was then tested on a dataset of 2150 malignant or benign images. Overall, the classifier achieved high average values for accuracy, sensitivity, and specificity of 82.95%, 82.99%, and 83.89% respectively. It outperfomed other exisitng networks using the same dataset.


Assuntos
Diagnóstico por Computador , Melanoma , Neoplasias Cutâneas , Dermoscopia , Humanos , Melanoma/diagnóstico por imagem , Redes Neurais de Computação , Neoplasias Cutâneas/diagnóstico por imagem
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1824-1827, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018354

RESUMO

Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.


Assuntos
Neoplasias Cutâneas , Atenção , Humanos , Microscopia Confocal , Redes Neurais de Computação , Neoplasias Cutâneas/diagnóstico por imagem
13.
BMC Bioinformatics ; 21(Suppl 11): 270, 2020 Sep 14.
Artigo em Inglês | MEDLINE | ID: mdl-32921304

RESUMO

BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of skin cancer are reported annually worldwide ( https://apps.who.int/gho/data/?theme=main ) and those numbers continue to grow. In our opinion, due to the increasing incidence of the disease, it is necessary to find new, easy to use and sensitive methods for the early diagnosis of melanoma in a large number of people around the world. Over the last decade, neural networks show highly sensitive, specific, and accurate results. OBJECTIVE: This study presents a review of PubMed papers including requests «melanoma neural network¼ and «melanoma neural network dermatoscopy¼. We review recent researches and discuss their opportunities acceptable in clinical practice. METHODS: We searched the PubMed database for systematic reviews and original research papers on the requests «melanoma neural network¼ and «melanoma neural network dermatoscopy¼ published in English. Only papers that reported results, progress and outcomes are included in this review. RESULTS: We found 11 papers that match our requests that observed convolutional and deep-learning neural networks combined with fuzzy clustering or World Cup Optimization algorithms in analyzing dermatoscopic images. All of them require an ABCD (asymmetry, border, color, and differential structures) algorithm and its derivates (in combination with ABCD algorithm or separately). Also, they require a large dataset of dermatoscopic images and optimized estimation parameters to provide high specificity, accuracy and sensitivity. CONCLUSIONS: According to the analyzed papers, neural networks show higher specificity, accuracy and sensitivity than dermatologists. Neural networks are able to evaluate features that might be unavailable to the naked human eye. Despite that, we need more datasets to confirm those statements. Nowadays machine learning becomes a helpful tool in early diagnosing skin diseases, especially melanoma.


Assuntos
Aprendizado Profundo , Detecção Precoce de Câncer , Interpretação de Imagem Assistida por Computador/métodos , Melanoma/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Confiabilidade dos Dados , Humanos , Sensibilidade e Especificidade
14.
Hautarzt ; 71(9): 691-698, 2020 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-32720165

RESUMO

ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI): With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer. REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS: Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.


Assuntos
Inteligência Artificial , Dermatologia/métodos , Melanoma/diagnóstico por imagem , Aplicativos Móveis , Neoplasias Cutâneas/diagnóstico por imagem , Smartphone , Telemedicina/instrumentação , Humanos , Interpretação de Imagem Assistida por Computador , Oncologia , Melanoma/diagnóstico , Neoplasias Cutâneas/diagnóstico
15.
J Dtsch Dermatol Ges ; 18(7): 682-690, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32657017

RESUMO

BACKGROUND: The melanin fluorescence of skin lesions is measurable with two-photon excitation, a process termed dermatofluoroscopy, which has shown a shift from the green spectra in benign melanocytic lesions to the red spectra in melanoma. This study addressed the question as to which kind of pigmented lesions can be correctly diagnosed as melanin-bearing malignant tumors. METHODS: 476 pigmented lesions including 101 cutaneous melanomas were analyzed with dermatofluoroscopy, measuring the melanin fluorescence in a grid-like fashion with a separation of measurement points of 0.2 mm. The results of the dermatofluoroscopy are presented as a diagnostic score with a cut-off score of ≥ 28 for the diagnosis of melanin-bearing malignant tumors, and were compared to the gold standard of histopathology. RESULTS: A highly significant difference (p < 0.0001) between the diagnostic scores of different skin tumors was found. Dermatofluoroscopy scores showed the highest sensitivity for melanomas (92.1 %). Interestingly, most pigmented basal cell carcinomas (BCCs, 88.9 %) were diagnosed as melanin-bearing malignant tumors. A higher sensitivity for the correct diagnosis was observed in older patients (≥ 53 years, p = 0.003), in patients with skin tanning (p = 0.025), and in patients with freckles during childhood (p = 0.046). CONCLUSIONS: Two-photon fluorescence is an innovative technique for the diagnosis of pigmented skin lesions, and shows a high sensitivity for detection of melanomas and pigmented BCCs.


Assuntos
Carcinoma Basocelular/diagnóstico por imagem , Dermoscopia , Fluoroscopia , Melanoma/diagnóstico por imagem , Nevo Pigmentado/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Diagnóstico Diferencial , Fluorescência , Humanos , Melanócitos , Microscopia de Fluorescência por Excitação Multifotônica , Sensibilidade e Especificidade , Pele/patologia
16.
Tokai J Exp Clin Med ; 45(2): 58-62, 2020 Jul 20.
Artigo em Inglês | MEDLINE | ID: mdl-32602102

RESUMO

Here, we report the case of cutaneous metastases from testicular diffuse large B-cell malignant lymphoma (DLBCL) concurrent with Bowen disease evaluated with 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT). A 60-year-old male underwent orchiectomy to remove his left testicle because of DLBCL. Multiple skin lesions appeared 1 month postoperatively. Furthermore, an intractable erythematous plaque localized to the right lower leg was present from 2 years before the operation. 18F-FDG PET-CT images revealed multiple skin lesions with marked FDG uptakes in the face, neck, and thigh of this patient, as well as a lower leg lesion with minimal FDG uptake. Biopsy of both lesions revealed cutaneous metastases from DLBCL and Bowen disease (BD) of the lower leg lesion. 18F-FDG PET-CT images following chemotherapy and resection of BD demonstrated no FDG uptake.


Assuntos
Doença de Bowen , Linfoma Difuso de Grandes Células B/diagnóstico por imagem , Linfoma Difuso de Grandes Células B/terapia , Neoplasias Cutâneas/diagnóstico por imagem , Neoplasias Cutâneas/secundário , Neoplasias Testiculares/diagnóstico por imagem , Neoplasias Testiculares/terapia , Doença de Bowen/cirurgia , Fluordesoxiglucose F18 , Humanos , Linfoma Difuso de Grandes Células B/tratamento farmacológico , Linfoma Difuso de Grandes Células B/cirurgia , Masculino , Pessoa de Meia-Idade , Compostos Radiofarmacêuticos
17.
Clin Nucl Med ; 45(10): 827-829, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32701814

RESUMO

Cutaneous leiomyomas are rare, sporadic, or inherited benign tumors arising from smooth muscle cells of the skin associated with various disorders. We present a case of multiple cutaneous leiomyomas showing increased FDG uptake with SUVmax of 19.9. This case indicates cutaneous leiomyoma should be considered as a rare differential diagnosis in patients with hypermetabolic cutaneous lesions. Careful correlation with clinical history is needed to avoid misdiagnosis.


Assuntos
Fluordesoxiglucose F18/metabolismo , Leiomiomatose/metabolismo , Síndromes Neoplásicas Hereditárias/metabolismo , Neoplasias Cutâneas/metabolismo , Neoplasias Uterinas/metabolismo , Transporte Biológico , Diagnóstico Diferencial , Feminino , Humanos , Leiomiomatose/diagnóstico por imagem , Masculino , Síndromes Neoplásicas Hereditárias/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Neoplasias Uterinas/diagnóstico por imagem
19.
An Acad Bras Cienc ; 92(1): e20190554, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32491128

RESUMO

Skin is the outermost and largest organ of the human body that protects us from the external agents. Among the various types of diseases affecting the skin, melanoma (skin cancer) is the most dangerous and deadliest disease. Though it is one of the dangerous forms of cancer, it has a high survival rate if and only if it is diagnosed at the earliest. In this study, skin cancer classification (SCC) system is developed using dermoscopic images. It is considered as a classification problem with the help of Bendlet Transform (BT) as features and Support Vector Machine (SVM) as a classifier. First, the unwanted information's such as hair and noises are removed using median filtering approach. Then, directional representation based feature extraction system that precisely classifies curvature, location and orientation is employed. Finally, two SVM classifiers are designed for the classification. The performance of the SCC system based on Bendlet is superior to other image representation systems such as Wavelets, Curvelets, Contourlets and Shearlets.


Assuntos
Dermoscopia/métodos , Melanoma/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Máquina de Vetores de Suporte , Diagnóstico por Computador , Humanos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
20.
Nat Med ; 26(8): 1229-1234, 2020 08.
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.


Assuntos
Inteligência Artificial , Neoplasias Cutâneas/diagnóstico por imagem , Telemedicina , Interface Usuário-Computador , Tomada de Decisão Clínica , Humanos , Redes Neurais de Computação , Médicos , Neoplasias Cutâneas/patologia
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