Machine-Learning-Assisted Analysis of Patient Clinical Biomarkers to Improve Ovarian Cancer Diagnosis

The unavailability of accurate and reliable methods for early ovarian cancer detection represents a major gap in ovarian cancer diagnosis and management. The emergence and recent integration of machine learning with cancer diagnostic techniques, particularly biomarker-based blood tests, have the pot...

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Bibliographic Details
Main Author:	Célia Sahli (17289760) (author)
Other Authors:	Tiffany Thanhtruc Pham (21633026) (author), Kenry (8678085) (author)
Published:	2025
Subjects:	Genetics Cancer Statistics Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified cancer discriminatory power cancer diagnostic techniques ovarian cancer screening ovarian cancer diagnosis reliable diagnostic results relevant quantitative approaches based blood tests patient clinical biomarkers patient menopausal status improve patient outcomes benign ovarian tumors machine learning algorithms ovarian tumors menopausal status benign tumors patient populations statistical approaches reliable methods clinical features 47 blood systematic consideration recent integration premenopausal patients postmenopausal patients particularly biomarker obtained features necessary prior major gap machine learning feature selection critical feature assisted analysis 300 patients
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Machine-Learning-Assisted Analysis of Patient Clinical Biomarkers to Improve Ovarian Cancer Diagnosis

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