RESUMO
Pseudoxanthoma elasticum (PXE) is an autosomal recessive genetic disorder characterized by aberrant fragmentation and calcification of elastic fibers, leading to characteristic cutaneous, ophthalmic, and cardiovascular manifestations. PXE demonstrates significant phenotypic variability; involvement of the oral mucosa may be the only clue to the diagnosis. Reports on mucous membrane involvement in PXE are scarce. Here, we present a case of PXE-like changes in the oral cavity. A 70-year-old male patient presented with a painless leukoplakic lesion on the soft palate. Biopsy revealed numerous degenerated fibers in the lamina propria. Verhoeff-van Gieson and von Kossa staining confirmed their identity as calcified elastic fibers. A histopathological diagnosis of PXE-like changes was made; the patient was referred to ophthalmology where angioid streaks were visualized fundoscopically. PXE-like changes in the absence of the characteristic genetic mutation have also been reported with or without systemic manifestations. Furthermore, PXE-like changes have been reported in up to 10% of oral biopsy specimens undertaken without clinical suspicion for PXE. Therefore, the significance of such changes in isolation is unclear. Clinicians and pathologists should be aware of the potential oral manifestations of PXE to facilitate prompt diagnosis and subspecialist referral.
Assuntos
Pseudoxantoma Elástico , Masculino , Humanos , Idoso , Pseudoxantoma Elástico/diagnóstico , Pseudoxantoma Elástico/patologia , Pele/patologia , Tecido Elástico/patologia , Palato Mole/patologia , MutaçãoRESUMO
Myofibroblastoma is a rare, benign mesenchymal tumor first described as a neoplasm of the breast. Extramammary myofibroblastoma is a histopathologically and genetically identical lesion occurring outside the breast. Herein is presented a case of extramammary myofibroblastoma arising in the oral cavity. A 59-year-old woman presented with a 1.5 cm nodule on the buccal surface of the lower lip. Wide local excision was performed. Histopathologic examination revealed haphazard fascicles of monomorphic spindle cells with hyalinized collagen bundles without fat. The spindled cells were diffusely positive for CD34, and focally for progesterone receptor. Desmin, smooth muscle actin, estrogen receptor, androgen receptor, S100, and STAT6 were negative. Rb1 expression was lost in tumor cells. Thus, the diagnosis of extramammary myofibroblastoma was made. Differential diagnoses include spindle-cell lipoma and angiofibroma. All three tumors are members of the 13q14 deletion/RB1 loss family. Indolent but locally aggressive (solitary fibrous tumor, desmoid fibromatosis) and frankly malignant (low-grade peripheral nerve sheath tumor, dermatofibrosarcoma protuberans) entities can be excluded by immunohistochemistry and careful microscopic examination. Extensive sampling extramammary myofibroblastoma is important to exclude the possibility of malignancy. Clinicians and pathologists alike should be aware of this entity and its potential to arise rarely in unusual locations.
Assuntos
Angiofibroma , Lipoma , Neoplasias de Tecido Muscular , Feminino , Humanos , Pessoa de Meia-Idade , Neoplasias de Tecido Muscular/metabolismo , Mama/patologia , Angiofibroma/patologia , Lipoma/patologia , Lábio/patologia , Biomarcadores Tumorais/metabolismoRESUMO
Melanoma detection, prognosis, and treatment represent challenging and complex areas of cutaneous oncology with considerable impact on patient outcomes and healthcare economics. Artificial intelligence (AI) applications in these tasks are rapidly developing. Neural networks with increasing levels of sophistication are being implemented in clinical image, dermoscopic image, and histopathologic specimen classification of pigmented lesions. These efforts hold promise of earlier and highly accurate melanoma detection, as well as reliable prognostication and prediction of therapeutic response. Herein, we provide a brief introduction to AI, discuss contemporary investigational applications of AI in melanoma, and summarize challenges encountered with AI.
Assuntos
Inteligência Artificial , Melanoma , Humanos , Melanoma/diagnóstico por imagem , Redes Neurais de ComputaçãoRESUMO
BACKGROUND: Facial aging is a concern for many patients. Wrinkles, loss of volume, and discoloration are common physical manifestations of aging skin. Genetic heritage, prior ultraviolet light exposure, and Fitzpatrick skin type may be associated with the rate and type of facial aging. Although many clinical trials assess the correlates of skin aging, there is heterogeneity in the outcomes assessed, which limits the quality of evaluation and comparison of treatment modalities. To address the inconsistency in outcomes, in this project we will develop a core set of outcomes that are to be evaluated in all clinical trials relevant to facial aging. METHODS/DESIGN: A long list of measureable outcomes will be created from four sources: (1) systematic medical literature review, (2) patient interviews, (3) other published sources, and (4) stakeholder involvement. Two rounds of Delphi processes with homogeneous groups of physicians and patients will be performed to prioritize and condense the list. At a consensus meeting attended by physicians, patients, and stakeholders, outcomes will be further condensed on the basis of participant scores. By the end of the meeting, members will vote and decide on a final recommended set of core outcomes. Subsequent to this, specific measures will be selected or created to assess these outcomes. DISCUSSION: The aim of this study is to develop a core outcome set and relevant measures for clinical trials relevant to facial aging. We hope to improve the reliability and consistency of outcome reporting of skin aging, thereby enabling improved evaluation of treatment efficacy and patient satisfaction. TRIAL REGISTRATION: Core Outcome Measures in Effectiveness Trials (COMET) Initiative, accessible at http://www.comet-initiative.org/studies/details/737 . Core Outcomes Set Initiative, (CSG-COUSIN) accessible at https://www.uniklinikum-dresden.de/de/das-klinikum/universitaetscentren/zegv/cousin/meet-the-teams/project-groups/core-outcome-set-for-the-appearance-of-facial-aging . Protocol version date is 28 July 2016.