Diagnostic value of progesterone receptor and p53 expression in uterine smooth muscle tumors

Hewedi, Iman H.; Radwan, Nehal A.; Shash, Lobna S

doi:10.1186/1746-1596-7-1

Cited by 67 publications

(52 citation statements)

References 22 publications

(38 reference statements)

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“…Notably, high ER and PR expressions are associated with a benign profile, while low expression represents a diagnostic marker related to a potentially adverse outcome [10] . Moreover, in contrast with LMS, AUSMT are characterized by low IHC p53 expression [11] . Significance was also observed in the expression of p16 among LMS, AUSMT, and leiomyomas (LM) [12] .…”

Section: Introductionmentioning

confidence: 99%

Atypical Uterine Smooth Muscle Tumors: A Retrospective Evaluation of Clinical and Pathologic Features

et al. 2017

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Background: Clinical characteristics combined with new biomarkers help discriminate between atypical uterine smooth muscle tumors (AUSMT) and leiomyosarcomas (LMS). Patients and Methods: We retrospectively collected a series of leiomyomas (LM), AUSMT, and LMS. Estrogen receptors (ER), progesterone receptors (PR), p16, Ki-67, and p53 expression were assessed by immunohistochemistry. For AUSMT patients, immunohistochemistry evaluations were performed at the time of diagnosis and at recurrences. Results: A total of 27 cases of AUSMT, 22 LM, and 31 LMS were identified. The expression of ER and PR decreased from LM to LMS (ER+: LM 95.5%, AUSMT 88.9%, LMS 41.9%, p < 0.001; PR+: LM 100%, AUSMT 88.9%, LMS 38.2%, p = 0.002). By contrast, p16 and p53 expression increased (p16+: LM 4.5%, AUSMT 40.7%, LMS 45.2%, p = 0.004; p53: LM 9.1%, AUSMT 33.3%, LMS 58.1%, p = 0.001). At a median follow-up of 33.47 months, 40.7% of patients with AUSMT experienced recurrent disease, 6 patients relapsed as AUSMT and 5 as LMS. In univariate analysis was observed that ER status (p = 0.027) and p53 expression (p = 0.015) predicted risk of relapse. Conclusions: Treatment of AUSMT should be centralized in dedicated centers. International collaborations are needed to optimize research strategy, which may lead to the identification of new useful biomarkers and to improvement in the clinical management of this rare disease.

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Section: Introductionmentioning

confidence: 99%

Atypical Uterine Smooth Muscle Tumors: A Retrospective Evaluation of Clinical and Pathologic Features

et al. 2017

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“…Currently, there is no clear-cut treatment algorithm for IMTs, although macroscopic surgical excision remains the treatment of choice [4]. The use of radiotherapy, corticosteroids, and chemotherapy has also been described in the literature for recurrent or unresectable tumors.…”

Section: Discussionmentioning

confidence: 99%

Efficiency of Crizotinib on an ALK-Positive Inflammatory Myofibroblastic Tumor of the Central Nervous System: A Case Report

Chennouf

Arslanian

Roberge

et al. 2017

Cureus

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“…Differently from traditional staining techniques, the imaging technologies leveraging on immunohistochemistry (IHC) are able to reveal textures at a much finer spatial scale, because they can highlight very small molecular complexes such as proteins, carbohydrates or lipids [21]. Hence, such images can be exploited not only for tissue texture but also for cell texture analysis.…”

Section: Texture Analysis In Biological Imagingmentioning

confidence: 99%

Mining textural knowledge in biological images: Applications, methods and trends

Cataldo

Ficarra

2017

Computational and Structural Biotechnology Journal

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Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.

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Diagnostic value of progesterone receptor and p53 expression in uterine smooth muscle tumors

Cited by 67 publications

References 22 publications

Atypical Uterine Smooth Muscle Tumors: A Retrospective Evaluation of Clinical and Pathologic Features

Atypical Uterine Smooth Muscle Tumors: A Retrospective Evaluation of Clinical and Pathologic Features

Efficiency of Crizotinib on an ALK-Positive Inflammatory Myofibroblastic Tumor of the Central Nervous System: A Case Report

Mining textural knowledge in biological images: Applications, methods and trends

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