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Carcinoma, Ovarian Epithelial

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Assessing the association between 25-OH vitamin D levels and ROMA score in a population of obese women.

Journal of biological regulators and homeostatic agents 28078870
The “Risk of Malignancy Algorithm” (ROMA) combines the diagnostic power of the CA125 and HE4 markers with menopausal status to predict the risk for developing epithelial ovarian cancer (EOC). The aim of this study was to evaluate the association betw...
Adult Algorithms Biomarkers, Tumor Bone Density Carcinoma, Ovarian Epithelial Enzyme-Linked Immunosorbent Assay Female Humans Middle Aged Neoplasms, Glandular and Epithelial Obesity Ovarian Neoplasms Risk Factors ROC Curve Sensitivity and Specificity Vitamin D Young Adult
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Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

Annals of surgical oncology 25752894
BACKGROUND: The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, convent...
Algorithms Area Under Curve Bayes Theorem Carcinoma, Ovarian Epithelial Cytoreduction Surgical Procedures Decision Trees Female Humans Neoplasms, Glandular and Epithelial Neural Networks, Computer Ovarian Neoplasms Predictive Value of Tests Prognosis Proportional Hazards Models ROC Curve Support Vector Machine Survival Rate
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A chemotherapy response classifier based on support vector machines for high-grade serous ovarian carcinoma.

Oncotarget 26675546
Long-term outcome of high-grade serous epithelial ovarian carcinoma (HGSOC) remains poor as a result of recurrence and the emergence of drug resistance. Almost all the patients were given the same platinum-based chemotherapy after debulking surgery e...
Antineoplastic Agents Carcinoma, Ovarian Epithelial Cohort Studies Decision Support Techniques Disease-Free Survival Drug Resistance, Neoplasm Female Humans Middle Aged Neoplasm Recurrence, Local Neoplasms, Glandular and Epithelial Ovarian Neoplasms Response Evaluation Criteria in Solid Tumors Support Vector Machine
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Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society 26512784
OBJECTIVES: Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diag...
Biomarkers, Tumor Blotting, Western Carcinoma, Ovarian Epithelial Cystadenocarcinoma, Serous Databases, Genetic Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Immunoenzyme Techniques Machine Learning Monitoring, Intraoperative Neoplasm Staging Neoplasms, Glandular and Epithelial Ovarian Neoplasms Predictive Value of Tests Prognosis Real-Time Polymerase Chain Reaction Reverse Transcriptase Polymerase Chain Reaction RNA, Messenger Support Vector Machine Survival Rate
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MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods.

Proteomics. Clinical applications 30471200
PURPOSE: Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix-a...
Carcinoma, Ovarian Epithelial Discriminant Analysis Female Humans Linear Energy Transfer Machine Learning Middle Aged Molecular Imaging Ovarian Neoplasms Proteomics Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Support Vector Machine Tissue Array Analysis
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Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers.

Clinical cancer research : an official journal of the American Association for Cancer Research 30979733
PURPOSE: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.
Adult Aged Aged, 80 and over Algorithms Artificial Intelligence Bayes Theorem Biomarkers, Tumor Carcinoma, Ovarian Epithelial Female Humans Machine Learning Middle Aged Neoplasm Grading Neoplasm Staging Neural Networks, Computer Ovarian Neoplasms Preoperative Care Prognosis ROC Curve Support Vector Machine Survival Rate Young Adult
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