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Early Risk Prediction of Ovarian Cancer Using Multimodal Clinical and Biomarker Data

Principal Investigator

Name
Vanshika Sharma

Degrees
BE, ME

Institution
Chandigarh University

Position Title
Assistant Professor

Email
vanshika.e17592@cumail.in

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-2020

Initial CDAS Request Approval
Feb 23, 2026

Title
Early Risk Prediction of Ovarian Cancer Using Multimodal Clinical and Biomarker Data

Summary
Ovarian cancer is frequently detected at advanced stages because reliable, affordable early screening methods remain limited. Although molecular and genomic techniques show potential, their high cost and limited scalability restrict population-wide adoption. The PLCO ovarian screening dataset offers valuable longitudinal clinical and biomarker information collected before diagnosis, enabling systematic investigation of early disease signals. Leveraging this resource, the proposed research applies advanced machine learning and data fusion techniques to integrate heterogeneous features efficiently. The goal is to develop computationally lightweight, clinically accessible models that support early ovarian cancer risk assessment and improve timely intervention in real-world screening settings globally today.

Aims

The objective of this research is to develop and evaluate machine learning models for early risk prediction of ovarian cancer using clinical and biomarker screening data from the PLCO trial. The study focuses on constructing robust predictive frameworks that identify subtle prediagnostic patterns preceding clinical onset. It aims to design multimodal feature fusion techniques integrating demographic, clinical, and biomarker variables to enhance early-stage prediction accuracy. Emphasis is placed on model interpretability and transparency to support clinical decision-making. Overall, the research seeks scalable, data-driven methods that can assist clinicians in risk stratification and contribute to improved screening strategies and timely interventions.

Collaborators

Vanshika Sharma Chandigarh university