Results 31 to 40 of about 57,979 (231)

Endometrial thickness cut-off value by transvaginal ultrasonography for screening of endometrial pathology in premenopausal and postmenopausal women [PDF]

open access: yesObstetrics & Gynecology Science, 2019
ObjectiveTo assess the clinical usefulness and diagnostic accuracy of ultrasonographic measurement of endometrial thickness (ET) in women with endometrial hyperplasia or cancer (EH+).MethodsThis retrospective cohort study included 29,995 consecutive ...
Yu Ran Park   +6 more
doaj   +1 more source

Immunotherapy in gynecological cancers

open access: yesExploration of Targeted Anti-tumor Therapy, 2021
Immunotherapy has changed the natural history of several malignancies that, a decade ago, had a very poor prognosis, such as lung cancer and melanoma.
Domenica Lorusso   +8 more
doaj   +1 more source

Uterine adenocarcinoma with abdominal metastases in an ovariohysterectomised female cat [PDF]

open access: yes, 2011
An adenocarcinoma of the uterine stump with abdominal metastases is described in a 12-year-old incompletely ovariohysterectomised female Domestic Short Haired (DSH) cat.
Belter LF   +4 more
core   +1 more source

ARID1A and PI3-kinase pathway mutations in the endometrium drive epithelial transdifferentiation and collective invasion

open access: yesNature Communications, 2019
PIK3CA mutations and ARID1A loss co-exist in endometrial neoplasms. Here, the authors show that these co-mutations drive gene expression profiles correlated with differential chromatin accessibility and ARID1A binding in the endometrial epithelium ...
Mike R. Wilson   +15 more
doaj   +1 more source

Occupational exposure to pesticides and endometrial cancer in the Screenwide case-control study

open access: yesEnvironmental Health, 2023
Background Endometrial cancer is the most common gynaecological tumour in developed countries and disease burden is expected to increase over the years. Identifying modifiable risk factors may help developing strategies to reduce the expected increasing ...
Arnau Peñalver-Piñol   +22 more
doaj   +1 more source

Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer [PDF]

open access: yesarXiv, 2023
Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2.8% in women. Precise histologic evaluation and molecular classification of endometrial cancer is important for effective patient management and determining the best treatment modalities.
arxiv  

Linking type 2 diabetes and gynecological cancer: An introductory overview [PDF]

open access: yes, 2018
Type 2 diabetes (T2D) is a chronic disease with a growing prevalence and a leading cause of death in many countries. Several epidemiological studies observed an association between T2D and increased risk of many types of cancer, such as gynecologic ...
Anastasi, Emanuela   +5 more
core   +1 more source

A new case of primary signet-ring cell carcinoma of the cervix with prominent endometrial and myometrial involvement: Immunohistochemical and molecular studies and review of the literature

open access: yesWorld Journal of Surgical Oncology, 2012
Background As a rule, endocervical tumours with signet-ring cell are classed as metastatic extra-genital neoplasms. In a patient aged 45 years, we describe primary cervical signet-ring cell carcinoma (PCSRCC) characterized by prominent endometrial and ...
Giordano Giovanna   +3 more
doaj   +1 more source

Update on Endometrial Stromal Tumours of the Uterus

open access: yesDiagnostics, 2021
Endometrial stromal tumours (ESTs) are rare, intriguing uterine mesenchymal neoplasms with variegated histopathological, immunohistochemical and molecular characteristics.
Iolia Akaev   +2 more
doaj   +1 more source

Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach [PDF]

open access: yesarXiv, 2023
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment.
arxiv  

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