AIMC Topic: Ovarian Neoplasms
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An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.
Scientific reports
Jul 1, 2025
Ovarian Cancer is a malignancy that develops from ovarian cells and is frequently characterized by aberrant cell proliferation that leads to the creation of tumors within the ovaries. The high death rate and often delayed discovery of Ovarian Cancer ...
Identifying ovarian cancer with machine learning DNA methylation pattern analysis.
Scientific reports
Jul 1, 2025
The majority of patients with epithelial ovarian cancer (EOC) continue to be diagnosed at an advanced stage despite great advances in this disease treatment. To impact overall survival, we need better methods of EOC early diagnosis. We performed a ca...
Identification of a novel chemotherapy benefit index for patients with advanced ovarian cancer based on Bayesian network analysis.
PloS one
May 27, 2025
BACKGROUND: This study aims to evaluate the efficacy of chemotherapy and optimize treatment strategies for patients with advanced ovarian cancer.
Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma.
Journal of ovarian research
May 19, 2025
BACKGROUND: Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC)...
Exploring potential methylation markers for ovarian cancer from cervical scraping samples.
Human genomics
May 17, 2025
BACKGROUND: Ovarian cancer has the highest mortality rate among gynecological cancers, making early detection crucial, as the five-year survival rate drops from 92% with early-stage diagnosis compared to 31% with late-stage diagnosis. Current diagnos...
Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-fr...
Ovarian Cancer Detection in Ascites Cytology with Weakly Supervised Model on Nationwide Data Set.
The American journal of pathology
Apr 30, 2025
Conventional ascitic fluid cytology for detecting ovarian cancer is limited by its low sensitivity. To address this issue, this multicenter study developed patch image (PI)-based fully supervised convolutional neural network (CNN) models and clusteri...
Statistical algorithms for the analysis of deleterious genetic mutations.
Bio Systems
Apr 30, 2025
We present algorithms for model selection and parameter estimation concerning deleterious genetic mutations. Three models are considered: single gene mutation, double cross-effect mutations or no genetic cause. Each of these models include unknown pa...
A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy.
Scientific reports
Apr 25, 2025
Ovarian cancer is associated with high rates of patient mortality and morbidity. Laparoscopic assessment of tumor localization can be used for treatment planning in newly diagnosed high-grade serous ovarian carcinoma (HGSOC). While spread to multiple...
A random forest-based predictive model for classifying BRCA1 missense variants: a novel approach for evaluating the missense mutations effect.
Journal of human genetics
Apr 18, 2025
The right classification of variants is the key to pre-symptomatic detection of disease and conducting preventive actions. Since BRCA1 has a high incidence and penetrance in breast and ovarian cancers, a high-performance predictive tool can be employ...
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