Precision Oncology Models Guide Tailored Nursing Interventions for Ovarian Cancer Symptom Clusters

October 11, 2022 by Elisa Becze BA, ELS, Editor

Using the National Institutes of Health Symptom Science Model ( (NIH-SSM) and Nursing Science Precision Health (NSPH) Model ( helps oncology nurses to recognize symptoms more promptly ( in patients with ovarian cancer and provide precision interventions ( that address racial disparities and foster equity in symptom-focused, patient-centered care, Mahoney and Pierce reported in the October 2022 issue of the Clinical Journal of Oncology Nursing.

How Oncology Nurses Use the NIH-SSM and NSPH Model

The NIH-SSM process involves several steps (

The NSPH Model helps oncology nurses further refines precision in complex symptom assessment ( by characterizing phenotypes, including lifestyle and environmental factors and other social determinants of health, genotypes, and other biomarkers. Nurses apply an understanding of those precise therapeutic targets to individualize clinical intervention designs and delivery.

Precision Symptom Identification in Ovarian Cancer

Although early-stage ovarian cancer is considered asymptomatic, patients may experience side effects throughout the disease continuum, including abdominal fullness, bloating, and discomfort; insomnia; pain and peripheral neuropathy; fatigue; depression and anxiety; and sexual dysfunction. Studies have identified certain symptom clusters ( in patients at diagnosis, during treatment, and into survivorship, Mahoney and Pierce said.

DiagnosisEarly in the disease trajectory, patients may experience abdominal or pelvic pain, abdominal distention, abdominal bloating or increased abdominal size, palpable abdominal mass, loss of appetite, feeling full quickly, indigestion, constipation, urinary frequency or urgency, or fatigue, Mahoney and Pierce reported. Researchers identified three specific ovarian cancer symptom clusters at diagnosis (

During treatmentChemotherapy and other anticancer treatments can add to a patient’s symptom burden, Mahoney and Pierce said. Researchers in various studies reported clusters ( of:

Additionally, researchers found that patients with higher symptom burden had lower quality-of-life scores ( and shorter progression-free survival.

SurvivorshipAll of the studies Mahoney and Pierce reviewed reported negative correlations between symptom occurrence during survivorship and patients’ quality of life. The studies identified the following symptom clusters common in ovarian cancer survivors (

Phenotypes and Biomarkers for Ovarian Cancer Symptom Clusters

Using the NIH-SSM and NSPH model to tailor interventions for those symptoms involves evaluating each patient’s individual phenotypic data and symptom biomarkers (, Mahoney and Pierce said. Symptom phenotyping involves measuring patient-reported symptoms and outcomes, and the authors recommended using the Measure of Ovarian Symptoms and Treatment (MOST) tool ( that “evaluates ovarian cancer symptoms as early as six months after diagnosis and for as many as four years in women who receive primary chemotherapy treatment and provides symptom surveillance to support clinical follow-up.” MOST is validated, patients can complete it electronically, and institutions can incorporate it into their electronic health record systems.

Ultimately, the NIH-SSM and NSPH model also involves exploring ovarian cancer symptom biomarkers. Mahoney and Pierce recognized that symptom biomarkers is an emerging field of study, and they encouraged oncology nurse scientists to conduct studies to identify them across the diverse population of patients with ovarian cancer ( “Genomic-sequencing technologies have overwhelmingly represented White individuals and have not incorporated underserved populations, where ovarian cancer disparities are prevalent,” the authors wrote. “Therefore, any potential genetic variants that will increase symptom burden risk are likely to be missed if they cluster primarily within specific underserved groups.”

Apply It to Your Practice

Mahoney and Pierce identified two opportunities for oncology nurses to improve their practice. First, they asked nurses to consider racial and ethnic disparities in symptom assessment in their institutions and communities and identify approaches to improve access to care and reporting ovarian cancer symptoms. Second, they encouraged nurses to expand their knowledge of multiomics and omics technologies using resources like the taxonomy, learning activities, videos, and more in the ONS Genomics and Precision Oncology Learning Library (

“Clinical oncology nurses can incorporate precision health in the assessment of clinical characteristics, social determinants of health, and environmental factors in relation to ovarian cancer symptom clusters throughout the course of disease,” the authors concluded. “A greater awareness of diverse phenotypes and the biologic mechanisms surrounding symptoms can inform clinical practice and the development of new therapies to target symptom-related racial disparities. Applying the NIH-SSM can assist clinical oncology nurses in providing symptom-focused bedside and ambulatory care that optimizes survivorship in women with ovarian cancer and improves their quality of life.”

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