Morphometric differentiation of squamous cell carcinoma and adenocarcinoma of the cervix

Wesoła, Marta; Jeleń, Michał

doi:10.5114/pjp.2015.57255

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…An estimated deaths from cervical cancer worldwide is 266,000 in 2012 [8]. adenocarcinoma and Squamous cell carcinoma (SCC) are most common types of cervical cancer [40]. Especially, the former is the dominant pathological type, which constitutes more than 80% of all cases of cervical malignancy [40,9].…”

Section: Introductionmentioning

confidence: 99%

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

Tian,

Yang,

Wang

et al. 2019

Preprint

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Goal: Squamous cell carcinoma of cervix is one of the most prevalent cancer worldwide in females. Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist. However, human evaluation of pathology slide is highly depending on the experience of pathologist, thus big inter-and intra-observer variability exists. Digital pathology, in combination with deep learning provides an opportunity to improve the objectivity and efficiency of histopathologic slide analysis. Methods: In this study, we obtained 800 haematoxylin and eosin stained slides from 300 patients suffered from cervix squamous carcinoma. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established deep learning models using popular convolution neural network architectures (inception-v3, InceptionResnet-v2 and Resnet50). Then random forest was introduced to feature extractions and slide-based classification. Results: The overall performance of our proposed models on slide-based tumor discrimination were outstanding with an AUC scores ¿ 0.94. While, location identifications of lesions in whole slide images were mediocre (FROC scores ¿ 0.52) duo to the extreme complexity of tumor tissues. Conclusion: For the first time, our analysis workflow highlighted a quantitative visual-based slide analysis of cervix squamous carcinoma. Significance: This study demonstrates a pathway to assist pathologist and accelerate the diagnosis of patients by utilizing new computational approaches.

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Section: Introductionmentioning

confidence: 99%

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

Tian,

Yang,

Wang

et al. 2019

Preprint

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“…Cervical (uterine cervix) cancer accounts for about 12% of cancer-related death of women [ 30 , 31 ]. SCCs account for 80–85% of all cervical cancers [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

CD109 and squamous cell carcinoma

Fang

Liu

et al. 2018

J Transl Med

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Squamous cell carcinoma (SCC) is well-known for its high rate of metastasis with poor prognosis. CD109 is a glycosylphosphatidylinositol-anchored cell-surface glycoprotein. Recently, CD109 emerges as a potential biomarker and a therapeutic target for SCCs. Accumulating studies have reported that CD109 is highly expressed in human SCCs of multiple organs, and may contribute to the progression of SCCs. In this review, we summarized the findings on expression pattern of CD109 in SCCs, and discussed the molecular mechanisms underlying the roles of CD109 in pathogenesis of SCCs.

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Morphometric differentiation of squamous cell carcinoma and adenocarcinoma of the cervix

Cited by 2 publications

References 10 publications

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

CD109 and squamous cell carcinoma

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