98%
921
2 minutes
20
Uterine serous adenocarcinoma (USC) is rare and invasive cancer. This cancer is more often reported in the ovary, the fallopian tube, and the endometrium than uterine cervix. No matter where the tumor is located, the tumor exhibits similar histological characteristics. So when uterine cancer is proven to be serous adenocarcinoma, it is necessary to see if the tumor originated from ovary or endometrium and invaded the cervix. We report a case of a 73-year-old postmenopausal woman with USC arising near the internal os of endocervical canal, clinically misdiagnosed as uterine cervix cancer.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4719093 | PMC |
http://dx.doi.org/10.6118/jmm.2015.21.3.171 | DOI Listing |
Clin Nucl Med
September 2025
Women Health Program, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman.
We report the case of a 47-year-old woman who presented with left inguinal swelling; the biopsy of which showed high-grade serous adenocarcinoma. 68Ga-FAPI PET/CT revealed a tracer-avid lesion in the left adnexal region and an enlarged left inguinal nodal mass (site of biopsy). Multiple focal lesions were also seen at the hepatic dome, along the falciform ligament and at the right lateral abdominal wall, suspicious for peritoneal/metastatic deposits.
View Article and Find Full Text PDFFront Immunol
September 2025
Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Background: Although immune checkpoint blockade (ICB) therapy has transformed the therapeutic landscape for ovarian cancer (OC), the predictive utility of immune checkpoint (IC) expression signatures in stratifying clinical outcomes requires further systematic interrogation.
Methods: Transcriptomic profiles from 147 OC patients within The Cancer Genome Atlas (TCGA) cohort were interrogated to assess the prognostic significance of ICs. These genomic findings were subsequently validated through immunohistochemical analysis of an independent institutional cohort comprising 74 OC tissue specimens.
Cancers (Basel)
August 2025
Department of Obstetrics and Gynecology, Faculty of Medicine, Karadeniz Technical University, Trabzon 61080, Turkey.
: Flow cytometric analysis of cellular DNA content has prognostic importance in ovarian cancer and its measurement could contribute to clinical practice. The aim of this study was to compare serous cystadenoma and serous cystadenocarcinoma cases in terms of their flow cytometric analysis results. : In total, 60 serous ovarian tumor cases (30 cases of ovarian serous cyst adenoma and 30 cases of ovarian serous cystadenocarcinoma) in paraffin blocks were extracted from hospital pathology archives for flow cytometric analysis.
View Article and Find Full Text PDFJ Ovarian Res
August 2025
Department of Woman and Child's Health and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Background: Ovarian cancer remains a major clinical challenge with more than 40.000 annual deaths in Europe and in the United States, highlighting the need for better diagnostic and therapeutic strategies. This study first presents an immunohistochemical evaluation of the extra-domains A and B containing fibronectin (EDA-FN, EDB-FN), fibroblast activation protein (FAP), and carcinoembryonic antigen (CEA) in ovarian cancer specimens.
View Article and Find Full Text PDFBMC Cancer
August 2025
Department of Gynecology, The Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang City, 050000, Hebei Province, China.
Background: The importance of epithelial‒mesenchymal transition (EMT) in tumour invasion and metastasis in high-grade serous ovarian cancer (HGSOC) has been highlighted in numerous studies, but genetic biomarkers for predicting EMT in HGSOC are still lacking.
Methods: The role of EMT hallmarks and the relationship between EMT and the tumour microenvironment in HGSOC were examined based on transcriptomic data from 366 HGSOC patients in the TCGA dataset via the GSVA algorithm, the ESTIMATE method and Pearson correlation analyses. Furthermore, machine learning was applied to determine key EMT signatures and classify EMT subtypes.