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Ovarian Neoplasms

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Deep Learning-Assisted Intelligent Artificial Vision Platform Based on Dual-Luminescence Eu(III)-Functionalized HOF for the Diagnosis of Breast and Ovarian Cancer.

Analytical chemistry
Developing an advanced analytical method to detect spermine (Spm) and -acetylneuraminic acid (NANA), the biomarkers of breast and ovarian cancers, respectively, is critical for the early diagnosis of the two cancers, which is very meaningful for wome...

Deep learning-based segmentation of multisite disease in ovarian cancer.

European radiology experimental
PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

Can natural language processing be effectively applied for audit data analysis in gynaecological oncology at a UK cancer centre?

International journal of medical informatics
BACKGROUND: The British Gynaecological Cancer Society (BGCS) has highlighted the disparity of ovarian cancer outcomes in the UK compared to other European countries. Therefore, cancer quality assurance audits and subspecialty training are important i...

Identifying Explainable Machine Learning Models and a Novel SFRP2 Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer.

International journal of molecular sciences
Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management st...

Hybrid chain reaction and selective recognition-based homogeneous dual-fluorescence analysis of circulating tumor cells in clinical ovarian cancer samples.

Analytica chimica acta
BACKGROUND: Oncological analysis is important in tumor diagnosis. We constructed a dual-fluorescence and binary visual analysis system for circulating tumor cells (CTCs) using the folate receptor as a biomarker, combined with hybridization chain reac...

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Laboratory investigation; a journal of technical methods and pathology
Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking s...

Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.

European radiology experimental
High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quan...

Self-supervised deep learning for highly efficient spatial immunophenotyping.

EBioMedicine
BACKGROUND: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Labe...

Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC).

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sens...