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Updated: Aug 28, 2025
Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and
Diane E Mahoney1, Janet D Pierce1
1University of Kansas.
Background:
In the United States, ovarian cancer remains the deadliest gynecologic cancer because most women are diagnosed with advanced disease. Although early-stage ovarian tumors are considered asymptomatic, women experience symptoms throughout disease.
Objectives:
This review identifies ovarian cancer symptom clusters and explores the applicability of the National Institutes of Health Symptom Science Model (NIH-SSM) for prompt symptom recognition and clinical intervention.
Methods:
A focused CINAHL® and PubMed® database search was conducted for studies published from January 2000 to May 2022 using combinations of key terms.
Findings:
The NIH-SSM can guide the delivery of precision-focused interventions that address racial disparities and foster equity in symptom- focused care. Enhanced understanding of symptom biology can support clinical oncology nurses in ambulatory and inpatient settings.
