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. Author manuscript; available in PMC: 2023 Mar 15.
Published in final edited form as: Clin J Oncol Nurs. 2022 Sep 15;26(5):533–542. doi: 10.1188/22.CJON.533-542

Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and Management

Diane E Mahoney 1, Janet D Pierce 1
PMCID: PMC9951395  NIHMSID: NIHMS1869780  PMID: 36108208

Abstract

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.

Keywords: ovarian cancer, symptom clusters, quality of life, symptom management

AN ESTIMATED 20,000 WOMEN WILL BE DIAGNOSED with ovarian cancer and about 13,000 women will die of the disease in the United States in 2022 (Siegel et al., 2022). Four out of five women are diagnosed with advanced disease, which is attributed to the failure to identify ovarian tumors at an early stage because of a lack of adequate screening methods (Howlader et al., 2019; Torre et al., 2018). Although early-stage ovarian cancer is generally considered an asymptomatic disease and thus termed “a silent killer,” women can experience symptoms prior to diagnosis (Bankhead et al., 2005; Jayde et al., 2009; Koldjeski et al., 2003; Lurie et al., 2010). However, in clinic settings, communication of symptoms and discrepancies in the description of symptoms between patients and clinicians present challenges to facilitating prompt diagnostic intervention. For example, women report not notifying their clinician of symptoms because of a lack of knowledge, intermittent occurrence, perceived degree of seriousness, and the assumption that symptoms are part of the normal aging process (Olsen et al., 2007; Williams et al., 2019).

Following diagnosis, women with ovarian cancer often undergo surgery and a series of chemotherapy regimens that result in additional symptoms. Ovarian tumors have an approximately 80% recurrence rate that can intensify symptom burden when women undergo additional cycles of treatment. Therefore, innovation in ovarian cancer symptom management approaches could help clinical oncology nurses distinguish women at risk, precisely screen for symptoms, develop effective preventive therapies, and improve overall treatment outcomes to optimize quality of life (QOL). In addition, multifactorial ovarian cancer disparities remain prevalent throughout various aspects of this disease. Symptom-related racial disparities are well described in cancer care and associated with poor treatment outcomes in underrepresented populations (Bulls et al., 2021; Samuel et al., 2018; Umaretiya et al., 2021). In-depth examination of racial differences in symptom burden in patients with ovarian cancer can facilitate improved precision in symptom-focused care among diverse populations of women. The purpose of this review is to (a) describe the symptoms that are associated with ovarian cancer throughout the course of disease and (b) explore the applicability of the National Institutes of Health Symptom Science Model (NIH-SSM) for improving symptom-focused interventions and care for women with ovarian cancer. Proactive symptom management is central to clinical oncology nursing at the bedside, chairside, and advanced practice levels.

Background

By implementing the NIH-SSM, nurses can direct attention to understanding the biologic underpinnings of self-reported symptoms and the extent to which symptoms reflect or predict biologic risk and inform therapeutic interventions (Hickey et al., 2019). The NIH-SSM can foster innovation in clinical oncology nursing practice to recognize early ovarian cancer symptoms, initiate diagnostic measures, individualize therapeutic interventions, and lengthen survivorship. Symptom management is a primary focus of nursing research and practice. The National Institute of Nursing Research has charged nurses to lead the way in deepening knowledge about behavioral and biologic mechanisms of symptoms related to diseases through development of the NIH-SSM (Cashion & Grady, 2015). The goal of this model is to advance novel clinical interventions targeted to alleviate symptoms. The NIH-SSM begins with the identification of complex symptoms or symptom clusters. These symptoms then undergo measurement of phenotypic characterization. Based on specific identifiable phenotypic attributes, new biomarkers (biologic, physiologic, and omics discoveries) are investigated to strengthen the development of clinical application approaches (i.e., therapeutic interventions) that are tailored to reduce, improve, and prevent recognizable symptoms. Patient responses to subsequent interventions further inform clinical oncology nurses on how to best monitor disease, modify treatment decisions, and optimize health outcomes. The National Institute of Nursing Research leads the Symptom Science Center, with a mission to promote the understanding of biologic and behavioral processes of symptoms to improve patient outcomes (NIH, 2020).

Symptom science supports precision health because greater understanding of symptom origins can help nurse researchers determine precise therapeutic interventions that are tailored to the phenotypic and genotypic characteristics of patients. Personalized strategies to reduce symptom burden across diverse symptoms and among varying diseases are precision health–centered. Hickey et al. (2019) developed the Nursing Science Precision Health (NSPH) Model, which further explains integration of the NIH-SSM within precision health. In the NSPH Model, complex symptoms further refine precision in symptom assessment. Phenotypic characterization incorporates precision in characterizing phenotypes, including lifestyle and environmental factors. Biomarker discovery integrates precision in characterization of genotype and other biomarkers. Clinical application captures precision in therapeutic targets through individualized interventional designs and delivery (Hickey et al., 2019).

Advances in cancer treatment modalities present new opportunities for clinical oncology nurses to tailor ovarian cancer care in symptom identification, prediction, prevention, and management. Each of these has been shown to influence survivorship of women and thus serve as critical areas where innovative ovarian cancer research is needed. The transferability for precision health approaches in ovarian cancer symptom management can address important knowledge gaps in clinical oncology nursing care. A glossary of relevant terms is provided in Table 1.

TABLE 1.

GLOSSARY OF KEY TERMS

TERM DEFINITION
Biomarker A biologic molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease; may be used to see how well the body responds to a treatment for a disease or condition
Genomics The study of the complete set of DNA (including all of its genes) in a person or other organism; the genome contains all the information needed for a person to develop and grow; studying the genome may help to understand how genes interact with each other and the environment and how certain diseases form, which may lead to new ways to prevent, diagnosis, and treat disease.
Metabolomics The study of substances called metabolites in cells and tissues; metabolites, which are found in blood, urine, and other body fluids, are small molecules that are made when the body breaks down food, drugs, chemicals, or its own tissue; metabolomics may help to find new ways to diagnose and treat diseases, such as cancer.
Microbiomics The study of collections of all the microorganisms that live in an environment, including the human body or part of the body (e.g., digestive system); may play a role in a person’s health; studying the human microbiome may help to prevent and treat future disease.
Phenotype The physical, biochemical, and behavioral traits that can be observed in a person (e.g., height, eye color, hair color, blood type, behavior, presence of certain diseases); based on a person’s genes and some environmental factors, such as diet, exercise, and smoking
Proteomics The study of the structure and function of proteins, including the way they work and interact with each other inside cells
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Note. Based on information from National Cancer Institute, n.d.

Methods

A focused literature review was conducted using CINAHL® and PubMed® databases for studies published from January 2000 to May 2022. Inclusion criteria were studies that provided data regarding symptoms of ovarian cancer. Search terms included early diagnosis and ovarian cancer symptoms, late diagnosis and ovarian cancer symptoms, ovarian cancer symptom*, ovarian cancer symptomatology, ovarian cancer survivor* and quality of life, ovarian cancer survivor* and symptom*, ovarian cancer symptom* and treatment, ovary cancer symptom* and chemotherapy, ovarian cancer symptom* and radiation, and ovarian cancer symptom* and surgery. Editorials, opinions, and commentaries were excluded, and search results were limited to those published in the English language. Following review, extraction, and synthesis, 24 articles met the inclusion criteria. The findings are summarized (see Table 2) and presented within the NSPH Model of the NIH-SSM framework.

TABLE 2.

LITERATURE REVIEW OF SELECTED STUDIES (N = 24)

STUDY PURPOSE DESIGN FINDINGS
Ahmed- Lecheheb & Joly, 2016 To determine QOL among ovarian cancer survivors A systematic review of 31 studies assessing QOL and symptoms among ovarian cancer survivors Survivors experienced a sequalae of physical and psychological symptoms that had a negative impact on QOL in some studies and no impact on QOL in other studies.
Attanucci et al., 2004 To differentiate ovarian cancer symptoms experienced by women with early-stage disease as compared to those with late-stage disease and benign ovarian conditions A retrospective case-control study of 147 women diagnosed with invasive/borderline ovarian cancer and 76 women with benign ovarian conditions Women with early-stage ovarian cancer were more likely to report a palpable abdominal mass, pelvic pressure, constipation, and urinary frequency than the control group.
Bankhead et al., 2008 To identify symptoms diagnostic of ovarian cancer A prospective mixed-methods study of 124 women referred for suspicion of ovarian cancer Loss of appetite, feeling full quickly, abdominal distention, and postmenopausal bleeding were associated with receiving a diagnosis of ovarian cancer as compared to being diagnosed with benign gynecologic conditions.
Chan et al., 2003 To investigate associations among symptoms, timing, and coping strategies of women newly diagnosed with ovarian cancer A prospective, descriptive, observational study of 80 women who were newly diagnosed with ovarian cancer 90% of women experienced symptoms prior to diagnosis. Symptoms included abdominal discomfort/distention; palpable abdominal mass; and bowel, menstrual, and urinary complaints.
Dan et al., 2022 To identify symptom clusters of women with ovarian cancer at varying treatment stages A prospective, descriptive, observational study of 430 women who underwent surgery and chemotherapy Symptom clusters were diverse across time points (3 days prior to surgery, 2 days postsurgery, and after chemotherapy). Postchemotherapy symptom clusters were categorized as pain-related, emotional, cognitive, disease-related, treatment-related, and gastrointestinal.
Fox & Lyon, 2007 To describe symptom clusters and QOL associations among women with ovarian cancer A retrospective secondary data analysis of 76 women with advanced ovarian cancer Symptoms of fatigue and depression accounted for 41% of the explained variance in QOL.
Goff et al., 2004 To determine the frequency of ovarian cancer symptoms among women seen in primary care A prospective case-control study of 44 women diagnosed with ovarian cancer and 84 women with benign conditions; 1,709 women presenting in primary care clinics served as the control group. Women with ovarian cancer were more likely to experience abdominal pain, bloating, constipation, pelvic pain, and urinary urgency more frequently and at a higher severity than the control group.
Goff et al., 2007 To evaluate symptoms in women with ovarian cancer A prospective case-control study of 149 women with ovarian cancer and 528 women in the control group Women with ovarian cancer were more likely to report abdominal pain, bloating, increased abdominal size, feeling full, pelvic pain, and urinary complaints than women in the control group.
Greimel et al., 2011 To assess QOL in long- and short-term ovarian cancer survivors A prospective, descriptive, observational study of 33 long- and short-term ovarian cancer survivors Short-term survivors reported lower QOL and higher levels of symptoms. At 1 year postdiagnosis, long-term survivors reported better global QOL but persistent insomnia.
Hamilton et al., 2009 To identify and quantify ovarian cancer symptoms in primary care A prospective case-control study of 212 women diagnosed with ovarian cancer and 1,060 women in the control group Abdominal pain/distention and urinary frequency were independently associated with having a diagnosis of ovarian cancer as compared to the control group.
Hwang et al., 2016 To characterize ovarian cancer symptoms and QOL in women undergoing chemotherapy A prospective, descriptive, observational study of 192 women diagnosed with ovarian cancer who received adjuvant chemotherapy postsurgery Symptom clusters were abdominal discomfort, fatigue, pain, flu-like symptoms, fluid accumulation, peripheral neuropathy, and psychological distress, which affected all aspects of QOL.
Kim et al., 2018 To determine ovarian cancer symptom clusters and QOL based on stage of cancer survivorship A prospective, descriptive, observational study of 182 women diagnosed with ovarian cancer at acute, extended, and permanent survival stages Fatigue and diarrhea was the most common symptom cluster; QOL varied based on differing symptom clusters by survival stage.
Koldjeski et al., 2003 To examine early ovarian cancer symptoms in newly diagnosed women A prospective, descriptive, observational study of 19 women diagnosed with ovarian cancer 95% of the women experienced early symptoms of abdominal pain, indigestion, bloating, urinary complaints, and fatigue.
Lee et al., 2022 To evaluate postchemotherapy symptoms and health-related QOL in women with platinum-resistant/refractory recurrent ovarian cancer A prospective, descriptive, observational study of 910 women diagnosed with recurrent ovarian cancer Women reported high symptom burden related to the disease and chemotherapy treatment. Less than 40% of women reported symptom improvement, and 15% reported higher QOL with chemotherapy. Symptoms were categorized as abdominal, psychological, chemotherapy, well-being, and disease- or treatment-related.
Liavaag et al., 2007 To explore fatigue, QOL, somatic morbidity, and mental morbidity in women with ovarian cancer A prospective case-control study of 189 women diagnosed with ovarian cancer and 945 women in the control group Women with ovarian cancer had poorer QOL, higher anxiety levels, and chronic fatigue as compared to the control group.
Lim et al., 2012 To examine the relationship between reporting early ovarian cancer symptoms and the lead time to diagnosis A prospective case-control study of 194 women newly diagnosed with ovarian cancer and 268 women in the control group Abdominal/pelvic discomfort, bloating, feeling full, loss of appetite, and palpable mass in abdomen were independently associated with receiving a diagnosis of ovarian cancer as compared to the control group.
Lurie et al., 2009 To determine a set of ovarian symptoms that can assist healthcare providers in early disease diagnosis A prospective case-control study of 432 women diagnosed with invasive ovarian cancer and 491 women in the control group Abdominal pain, palpable mass, distended abdomen, and vaginal bleeding were predictive of early disease.
Nho et al., 2017 To investigate symptom clusters and their effects on QOL in women with ovarian cancer undergoing chemotherapy A prospective, descriptive, observational study of 210 women diagnosed with ovarian cancer and treated with chemotherapy One symptom cluster included fatigue, sleep disturbance, depression, and anxiety; another included pain and peripheral neuropathy. High degrees of symptom severity correlated with poor QOL.
Rietveld et al., 2019 To assess the relationship between gastrointestinal symptoms and QOL in ovarian cancer survivors A prospective, descriptive, observational study of 191 women diagnosed with ovarian cancer Survivors experienced substantial gastrointestinal symptoms, including flatulence, change in bowel habits, and bloating. High levels of symptoms were associated with poor QOL.
Rossing et al., 2010 To evaluate sensitivity, specificity, and positive predictive value of the Ovarian Symptom Screening Index A prospective case-control study of 812 women diagnosed with ovarian cancer and 1,313 women in the control group When administered the symptom index, women with ovarian cancer most commonly reported abdominal/pelvic pain, bloating, and feeling full quickly.
Stavraka et al., 2012 To examine physical and psychological symptoms experienced by women with ovarian cancer post-treatment compared to symptoms documented in the medical record A prospective, descriptive, observational study of 116 women diagnosed with ovarian cancer Women reported sexual dysfunction, insomnia, peripheral neuropathy, pain, fatigue, extremity weakness, and psychological symptoms that were not documented in the medical record.
Vine et al., 2003 To characterize ovarian cancer symptoms among women with and without disease A prospective case-control study of 267 women diagnosed with ovarian cancer and 287 women in the control group Women diagnosed within 2–4 months of symptom onset reported abdominal/pelvic discomfort, abdominal distention, bloating, feeling full quickly, weight changes, and respiratory difficulties as compared to the control group. Women diagnosed within 5–7 months of symptom onset reported urinary complaints, nausea, indigestion, vaginal bleeding, dyspareunia, and fatigue as compared to the control group.
Webb et al., 2004 To identify ovarian cancer symptoms associated with early and advanced disease A retrospective secondary data analysis of 811 women with early-stage and advanced ovarian cancer Women with early-stage disease were more likely to report a palpable abdominal mass and urinary complaints than women with advanced disease.
Webber et al., 2019 To investigate the prevalence of ovarian cancer symptoms postchemotherapy A prospective, descriptive, observational study of 1,360 women diagnosed with ovarian cancer Women most commonly reported peripheral neuropathy, fatigue, insomnia, and mood disturbance.
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QOL—quality of life

Results

Precision in Symptoms Experienced Prediagnosis or at Time of Diagnosis

Retrospective and prospective studies of ovarian cancer have been conducted to compare the symptoms of women without disease to the symptoms of women for as many as two years before diagnosis, either while under suspicion of disease (at time of diagnostic testing or surgery) or when newly diagnosed. An increase in the likelihood of having ovarian cancer was found in women who had experienced at least one of the following symptoms: abdominal/pelvic pain, abdominal distention, abdominal bloating/increased abdominal size, palpable abdominal mass, loss of appetite, feeling full quickly, indigestion, constipation, urinary frequency/urgency, or fatigue (Attanucci et al., 2004; Bankhead et al., 2008; Chan et al., 2003; Goff et al., 2004, 2007; Hamilton et al., 2009; Koldjeski et al., 2003; Lim et al., 2012; Rossing et al., 2010; Vine et al., 2003; Webb et al., 2004). One symptom cluster included bloating, vague abdominal pain, specific painful spots in abdomen, indigestion, palpable abdominal mass, urinary complaints, and fatigue (Koldjeski et al., 2003; Lurie et al., 2009). A second symptom cluster was comprised of bloating, pelvic/abdominal discomfort, increase in abdominal size, palpable abdominal mass, loss of appetite or feeling full quickly, and weight loss (Lim et al., 2012; Vine et al., 2003). A third symptom cluster consisted of bloating, increased abdominal size, and urinary complaints (Goff et al., 2004).

Precision in Symptoms Experienced During Treatment

Researchers have evaluated ovarian cancer symptom clusters in women with primary and recurrent disease undergoing chemotherapy (Hwang et al., 2016; Nho et al., 2017). Treatment plans for women with ovarian cancer often incorporate cyclic chemotherapy regimens (adjuvant or neoadjuvant therapy), which correlate with numerous side effects or symptom clusters that heighten symptom burden. One symptom cluster observed was abdominal bloating, indigestion, abdominal pain, and weight loss (Hwang et al., 2016; Lee et al., 2022). A second symptom cluster included sleep disturbance, fatigue, anxiety, and depression (Dan et al., 2022; Fox & Lyon, 2007; Lee et al., 2022; Nho et al., 2017; Webber et al., 2019). A third symptom cluster was lack of energy, fatigue, pain, difficulty concentrating, appetite change, and nausea (Dan et al., 2022; Hwang et al., 2016). The fourth symptom cluster was pain and/or peripheral neurologic symptoms (Hwang et al., 2016; Nho et al., 2017; Webber et al., 2019). In addition, women who experienced higher symptom burden reported lower QOL scores as compared to those with lower symptom burden (Fox & Lyon, 2007; Hwang et al., 2016; Nho et al., 2017). These women had a shorter progression-free survival (Lee et al., 2022). Women with higher levels of anxiety or depression also experienced higher symptom burden (Hwang et al., 2016).

Precision in Symptoms Experienced Throughout Survivorship

Although survival rates modestly increased with technological advances, fewer women with ovarian cancer attained survivorship compared to those with other gynecologic cancers (National Cancer Institute, 2018; Siegel et al., 2021). For women diagnosed with ovarian cancer, short-term survivors have been described as those who died within five years of diagnosis, and long-term survivors have been described as those who lived 10 years or more after diagnosis (Greimel et al., 2011). Others have categorized ovarian cancer survivorship into less than two years since diagnosis (acute survival), between two to five years after diagnosis (extended survival), and survival beyond five years (permanent survival) (Kim et al., 2018). Among available cross-sectional and longitudinal studies, symptom occurrence negatively influenced the QOL of ovarian cancer survivors. One symptom cluster was fatigue and diarrhea (Kim et al., 2018) or fatigue alone (Liavaag et al., 2007). Another symptom cluster was fatigue and depression (Fox & Lyon, 2007). An additional cluster included insomnia and sexual dysfunction (Ahmed-Lecheheb & Joly, 2016; Greimel et al., 2011; Stavraka et al., 2012). Lastly, Rietveld et al. (2019) found that ovarian cancer survivors experienced considerable gastrointestinal symptoms, including flatulence, change in bowel habits, and bloating regardless of disease recurrence status.

Precision in Phenotypic Characterization of Symptoms

Phenotypic data include a variety of information collected from patients, such as questionnaires, clinical parameters, and lifestyle considerations. Inconsistencies have been reported between the symptoms self-reported by women with ovarian cancer and what is documented in the health record (Hay et al., 2016). In a study by Stavraka et al. (2012), women documented experiencing various symptoms (e.g., fatigue; insomnia; sexual dysfunction; urinary, neurologic, and psychological symptoms) that were neither reported to their healthcare providers nor recorded in the health record. However, in a study by Gajjar et al. (2012), primary care providers rated abdominal swelling, abdominal pain, and pelvic pain of highest relevance when characterizing symptoms that raised suspicions of ovarian cancer diagnosis, whereas other gastrointestinal symptoms (feeling full quickly, changes in bowel habits, and indigestion) were rated of less significance.

Patient-reported outcome measures (PROMs) can provide quality symptom evaluations but are not consistently used in clinic settings (Weldring & Smith, 2013). The Measure of Ovarian Symptoms and Treatment (MOST) concerns is a PROM 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 (Beesley et al., 2021) and provides symptom surveillance to support clinical follow-up (Campbell et al., 2021). MOST was originally developed to assess symptom burden in women with recurrent ovarian cancer during chemotherapy (King et al., 2018). The psychometric properties of MOST have demonstrated strong construct, convergent, and divergent validity (Campbell et al., 2021; King et al., 2018). Patients with cancer have described positive experiences with self-reporting symptoms using electronic methods (e.g., mobile devices, wireless tablets, remote access) integrated into electronic health record (EHR) systems (Carrasco & Symes, 2018).

Precision in Characterization of Symptom Biomarkers

Although the identification of symptom-associated biomarkers in the field of ovarian cancer research is still in early stages, omics technologies now offer new possibilities for testing and targeting individualized treatment. The suffix “-omics,” used in terms such as genomics, microbiomics, proteomics, and metabolomics, delineates the study or application of entire collective sets of biologic molecules (e.g., DNA, microbes, proteins, metabolites) for selected plans of care (Lal et al., 2018; Misra et al., 2018; Srivastava & Creek, 2019). For example, cancer genomics is grounded in precision oncology, which emphasizes application of personalized treatment targets based on the unique tumor molecular characteristics of different individuals (Schwartzberg et al., 2018). Precision oncology relies on large databases that curate genetic and molecular features gathered from genomewide association studies and tumor-based omics-type sequencing. Genomic-sequencing technologies have overwhelmingly represented White individuals and have not incorporated underserved populations where ovarian cancer disparities are prevalent. Therefore, any potential genetic variants that will increase symptom burden risk are likely to be missed if they cluster primarily within specific underserved groups (Kaufman et al., 2019; Peterson et al., 2019).

Precision in the Identification of Clinical Interventions for Symptom Management

Supportive care measures that address symptoms are shown to improve QOL for patients with cancer and reduce emergency department visits, hospital admissions, and hospital length of stay (Weinstein et al., 2022). Clinical oncology nursing interventions can help empower women with ovarian cancer to gain a greater sense of control over their illness and treatment. In one study, women voiced concerns about the impact of ovarian cancer treatment on sexual function but did not feel this issue was adequately addressed by clinicians (Stafford et al., 2022). Clinically meaningful approaches are needed for healthcare providers to effectively assess for symptoms throughout disease and survivorship. Discrepancies in symptom assessment have also been reported by patients with cancer (Xiao et al., 2013). Effective communication between women and clinicians can optimize intervention success and enhance QOL for women with ovarian cancer (Hay et al., 2016; Tanay & Armes, 2019).

Although few clinical studies have investigated symptom assessment in racial and ethnic underserved populations, disparities have been observed in cancer research (Griggs, 2020). In breast cancer populations, characterization of symptoms has differed between Black and White women, with Black women reporting dissatisfaction concerning information on what symptoms to expect during treatment (Hu, Chehal, et al., 2021; Samuel et al., 2018). Black women are more likely to report clinically meaningful physical symptoms while receiving chemotherapy, but they reported that their symptoms were inadequately addressed by clinicians (Hu, Kaplan, et al., 2021; Nyrop et al., 2020). In cases in which women have reported symptoms like abdominal distention, bloating, and pain, clinicians have sometimes ruled them nonspecific or attributed them to another cause, such as irritable bowel syndrome (Evans et al., 2007; Williams et al., 2019). In particular, the general reporting of gastrointestinal symptoms has contributed to ovarian cancer diagnosis delays for women (Williams et al., 2019). In clinical practice, such discrepancies in symptom assessment can lead to ineffective symptom monitoring and management. The addition of PROM tools in EHRs can offer improved communication of symptoms among nurses and patients across cancer populations with diverse demographics (Trojan et al., 2021).

Discussion

In this era of precision health, the NIH-SSM can support clinical oncology nurses in identifying the unmet needs of women with ovarian cancer during treatment and follow-up care. This article demonstrates how the NIH-SSM can be applied to advanced clinical-based symptom assessment and management in practice settings. Published clinical studies addressing ovarian cancer symptom clusters are limited, which substantiates the importance of nurses introducing symptom-focused research initiatives in the workplace. Documenting ongoing symptom monitoring in EHRs can optimize treatment planning and help advanced practice oncology nurses make necessary treatment adjustments based on unique symptom phenotypes. It can also assist clinical oncology nurses in providing patient education on the symptom-targeted therapy plan. EHRs can provide an expansive source of symptom-related phenotypic data that reflect biologic, behavioral, and environmental characteristics that may be linked with multifactorial ovarian cancer disparities (Hickey et al., 2019). Multi-omics, which have become more commonly used to investigate the mechanisms of ovarian carcinogenesis, diagnostic and prognostic biomarkers, and therapeutic tumor targets, are needed in ovarian cancer symptom assessment (Ye et al., 2021). Omics technologies can also offer innovative clinical tools to understand the symptom biology of ovarian cancer. The ability to accurately detect and predict symptom clusters aids in clinical decision-making and timely diagnostic intervention (Huang et al., 2016). For example, early ovarian cancer symptom screening questionnaires in some clinic settings have triggered evaluation and disease diagnosis (Andersen et al., 2014; Goff et al., 2007). Nonetheless, some researchers suggest that implementing symptom screening methods alone may result in too few women being targeted for ovarian cancer diagnostic workup (Friedman et al., 2005; Rossing et al., 2010).

The absence of biologic measures to differentiate the origin of symptoms is a barrier in early disease detection. Therefore, distinguishing key biomarkers associated with early disease can contribute to the development of noninvasive clinical screening tests that validate PROMs in routine EHR symptom screenings. Symptom assessments in the absence of definitive biomarkers result in missed opportunities for diagnostic testing (Daly & Ozols, 2004). Consequently, the NIH-SSM provides a framework for symptom assessment, monitoring, and management across the ovarian cancer care continuum, including prevention and early detection, diagnosis and treatment, management of recurrent disease, and survivorship of women. In addition, precision health–focused interventions that address ovarian cancer disparities can help to determine which social determinants of health variables may correlate with specific symptom-associated biomarkers in vulnerable populations (Anderson et al., 2019).

Implications for Nursing

The Oncology Nursing Society (ONS) has charged clinical oncology nurses, including practitioners, scientists, and educators, to expand their omics knowledge base by becoming fluent in current terminology and incorporating this technology and expertise into clinical practice (ONS, n.d.-b). To increase the application of genomics into nursing practice, ONS provides a Genomics and Precision Oncology Learning Library and virtual workshops for clinical oncology nurses (ONS, n.d.-a). The NIH-SSM can empower clinical oncology nurses to lead initiatives that use precision health approaches to reduce symptom burden in numerous cancer populations at the bedside, chairside, and advanced practice levels in ambulatory and inpatient settings. Nurses are essential in optimizing communication of symptoms to address patients’ personalized needs. Comprehensive clinical care coordination is central to clinical oncology nursing, and precision health strategies foster the delivery of high-quality care to women with ovarian cancer. Precision health fundamentally supports collaborative, interprofessional healthcare teams, and nurses are valuable leaders who can promote, protect, and optimize health through patient-centered care. Innovative methods using precision health are necessary to accurately assess symptoms associated with ovarian cancer throughout all phases of disease. Although clinical oncology nurses play a key role in symptom management, the NIH-SSM can further guide them in the implementation of tailored symptom assessments and valuable nursing interventions to enhance patients’ QOL.

Conclusion

Women with ovarian cancer experience a wide range of symptoms that negatively affect QOL. Optimal patient–clinician communication of symptoms is paramount for early symptom evaluation. 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. 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 QOL.

IMPLICATIONS FOR PRACTICE.

■ Apply the National Institutes of Health Symptom Science Model (NIH-SSM) to improve patient symptom assessment in early- and late-stage disease.

■ Increase knowledge in the application of omics technologies to optimize clinical decision-making, diagnosis, and intervention for patients with ovarian cancer.

■ Encourage use of the NIH-SSM to promote delivery of patient-centered care.

REFERENCES

  1. Ahmed-Lecheheb D, & Joly F (2016). Ovarian cancer survivors’ quality of life: A systematic review. Journal of Cancer Survivorship, 10(5), 789–801. 10.1007/s11764-016-0525-8 [DOI] [PubMed] [Google Scholar]
  2. Andersen MR, Lowe KA, & Goff BA (2014). Value of symptom-triggered diagnostic evaluation for ovarian cancer. Obstetrics and Gynecology, 123(1), 73–79. 10.1097/AOG.0000000000000051 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Anderson RT, Peres LC, Camacho F, Bandera EV, Funkhouser E, Moorman PG, … Schildkraut JM (2019). Individual, social, and societal correlates of health-related quality of life among African American survivors of ovarian cancer: Results from the African American cancer epidemiology study. Journal of Women’s Health, 28(2), 284–293. 10.1089/jwh.2018.7025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Attanucci CA, Ball HG, Zweizig SL, & Chen AH (2004). Differences in symptoms between patients with benign and malignant ovarian neoplasms. American Journal of Obstetrics and Gynecology, 190(5), 1435–1437. 10.1016/j.ajog.2004.01.077 [DOI] [PubMed] [Google Scholar]
  5. Bankhead CR, Collins C, Stokes-Lampard H, Rose P, Wilson S, Clements A, … Austoker J (2008). Identifying symptoms of ovarian cancer: A qualitative and quantitative study. BJOG, 115(8), 1008–1014. 10.1111/j.1471-0528.2008.01772.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bankhead CR, Kehoe ST, & Austoker J (2005). Symptoms associated with diagnosis of ovarian cancer: A systematic review. BJOG, 112(7), 857–865. 10.1111/j.1471-0528.2005.00572.x [DOI] [PubMed] [Google Scholar]
  7. Beesley VL, Ross TL, King MT, Campbell R, Nagle CM, Obermair A, … Friedlander ML (2021). Evaluating patient-reported symptoms and late adverse effects following completion of first-line chemotherapy for ovarian cancer using the MOST (Measure of Ovarian Symptoms and Treatment concerns). Gynecologic Oncology, 164(2), 437–445. 10.1016/j.ygyno.2021.12.006 [DOI] [PubMed] [Google Scholar]
  8. Bulls HW, Chang P-H, Brownstein NC, Zhou J-M, Hoogland AI, Gonzalez BD, … Jim HSL (2021). Patient-reported symptom burden in routine oncology care: Examining racial and ethnic disparities. Cancer Reports, 5(3), e1478. 10.1002/cnr2.1478 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Campbell R, King MT, Ross TL, Cohen PA, Friedlander ML, & Webb PM (2021). Development and validation of the Measure of Ovarian Symptoms and Treatment concerns for surveillance (MOST-S26): An instrument to complement the clinical follow-up of women with ovarian cancer after completion of first-line treatment. Gynecology Oncology, 163(2), 398–407. 10.1016/j.ygyno.2021.08.022 [DOI] [PubMed] [Google Scholar]
  10. Carrasco S, & Symes L (2018). Patient use of electronic methods to self-report symptoms: An integrative literature review. Oncology Nursing Forum, 45(3), 399–416. 10.1188/18.ONF.399-416 [DOI] [PubMed] [Google Scholar]
  11. Cashion AK, & Grady PA (2015). The National Institutes of Health/National Institutes of Nursing Research intramural research program and the development of the National Institutes of Health Symptom Science Model. Nursing Outlook, 63(4), 484–487. 10.1016/j.outlook.2015.03.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chan YM, Ng TY, Lee PWH, Ngan HYS, & Wong LC (2003). Symptoms, coping strategies, and timing of presentations in patients with newly diagnosed ovarian cancer. Gynecologic Oncology, 90(3), 651–656. 10.1016/s0090-8258(03)00394-9 [DOI] [PubMed] [Google Scholar]
  13. Daly MB, & Ozols RF (2004). Symptoms of ovarian cancer—Where to set the bar? JAMA, 291(22), 2755–2756. 10.1001/jama.291.22.2755 [DOI] [PubMed] [Google Scholar]
  14. Dan X, Tian Y, Zhou L, Du J, & He Y (2022). Longitudinal change in symptom clusters in patients with ovarian cancer. Journal of Nursing Research, 30(2), e196. 10.1097/jnr.0000000000000478 [DOI] [PubMed] [Google Scholar]
  15. Evans J, Ziebland S, & McPherson A (2007). Minimizing delays in ovarian cancer diagnosis: An expansion of Andersen’s model of ‘total patient delay.’ Family Practice, 24(1), 48–55. 10.1093/fampra/cml063 [DOI] [PubMed] [Google Scholar]
  16. Fox SW, & Lyon D (2007). Symptom clusters and quality of life in survivors of ovarian cancer. Cancer Nursing, 30(5), 354–361. 10.1097/01.NCC.0000290809.61206.ef [DOI] [PubMed] [Google Scholar]
  17. Friedman GD, Skilling JS, Udaltsova NV, & Smith LH (2005). Early symptoms of ovarian cancer: A case-control study without recall bias. Family Practice, 22(5), 548–553. 10.1093/fampra/cmi044 [DOI] [PubMed] [Google Scholar]
  18. Gajjar K, Ogden G, Mujahid MI, & Razvi K (2012). Symptoms and risk factors of ovarian cancer: A survey in primary care. ISRN Obstetrics and Gynecology, 2012, 754197. 10.5402/2012/754197 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Goff BA, Mandel LS, Drescher CW, Urban N, Gough S, Schurman KM, … Andersen MR (2007). Development of an ovarian cancer symptom index: Possibilities for earlier detection. Cancer, 109(2), 221–227. 10.1002/cncr.22371 [DOI] [PubMed] [Google Scholar]
  20. Goff BA, Mandel LS, Melancon CH, & Muntz HG (2004). Frequency of symptoms of ovarian cancer in women presenting to primary care clinics. JAMA, 291(22), 2705–2712. 10.1001/jama.291.22.2705 [DOI] [PubMed] [Google Scholar]
  21. Greimel E, Daghofer F, & Petru E (2011). Prospective assessment of quality of life in long-term ovarian cancer survivors. International Journal of Cancer, 128(12), 3005–3011. 10.1002/ijc.25642 [DOI] [PubMed] [Google Scholar]
  22. Griggs JJ (2020). Disparities in palliative care in patients with cancer. Journal of Clinical Oncology, 38(9), 974–979. 10.1200/JCO.19.02108 [DOI] [PubMed] [Google Scholar]
  23. Hamilton W, Peters TJ, Bankhead C, & Sharp D (2009). Risk of ovarian cancer in women with symptoms in primary care: Population based case-control study. BMJ, 339, b2998. 10.1136/bmj.b2998 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hay CM, Courtney-Brooks M, Lefkowits C, Hagan TL, Edwards RP, & Donovan HS (2016). Symptom management in women with recurrent ovarian cancer: Do patients and clinicians agree on what symptoms are most important? Gynecologic Oncology, 143(2), 367–370. 10.1016/j.ygyno.2016.08.235 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hickey KT, Bakken S, Byrne MW, Bailey DCE, Demiris G, Docherty SL, … Grady PA (2019). Precision health: Advancing symptom and self-management science. Nursing Outlook, 67(4), 462–475. 10.1016/j.outlook.2019.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, … Cronin KA (Eds.). (2019). SEER cancer statistics review, 1975–2016. National Cancer Institute. https://seer.cancer.gov/archive/csr/1975_2016 [Google Scholar]
  27. Hu X, Chehal PK, Kaplan C, Krukowski RA, Lan RH, Stepanski E, … Graetz I (2021). Characterization of clinical symptoms by race among women with early-stage, hormone receptor–positive breast cancer before starting chemotherapy. JAMA Network Open, 4(6), e2112076. 10.1001/jamanetworkopen.2021.12076 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hu X, Kaplan C, Stepanski E, Schwartzberg LS, Vidal GA, Walker MS, … Graetz I (2021). Differences by race in patient-reported symptoms during chemotherapy among women with early-stage, hormone receptor–positive breast cancer. Journal of Clinical Oncology, 39(15, Suppl.), 6528–6528. 10.1200/JCO.2021.39.15_suppl.6528 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Huang J, Gu L, Zhang L, Lu X, Zhuang W, & Yang Y (2016). Symptom clusters in ovarian cancer patients with chemotherapy after surgery: A longitudinal survey. Cancer Nursing, 39(2), 106–116. 10.1097/NCC.0000000000000252 [DOI] [PubMed] [Google Scholar]
  30. Hwang K-H, Cho O-H, & Yoo Y-S (2016). Symptom clusters of ovarian cancer patients undergoing chemotherapy, and their emotional status and quality of life. European Journal of Oncology Nursing, 21, 215–222. 10.1016/j.ejon.2015.10.007 [DOI] [PubMed] [Google Scholar]
  31. Jayde V, White K, & Blomfield P (2009). Symptoms and diagnostic delay in ovarian cancer: A summary of the literature. Contemporary Nurse, 34(1), 55–65. 10.5172/conu.2009.34.1.055 [DOI] [PubMed] [Google Scholar]
  32. Kaufman M, Cruz A, Thompson J, Reddy S, Bansal N, Cohen JG, … Farias-Eisner R (2019). A review of the effects of healthcare disparities on the experience and survival of ovarian cancer patients of different racial and ethnic backgrounds. Journal of Cancer Metastasis and Treatment, 5, 13. 10.20517/2394-4722.2018.25 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kim M, Kim K, Lim C, & Kim J-S (2018). Symptom clusters and quality of life according to the survivorship stage in ovarian cancer survivors. Western Journal of Nursing Research, 40(9), 1278–1300. 10.1177/0193945917701688 [DOI] [PubMed] [Google Scholar]
  34. King MT, Stockler MR, O’Connell RL, Buizen L, Joly F, Lanceley A, … Friedlander ML (2018). Measuring what matters MOST: Validation of the Measure of Ovarian Symptoms and Treatment, a patient-reported outcome measure of symptom burden and impact of chemotherapy in recurrent ovarian cancer. Quality of Life Research, 27(1), 59–74. 10.1007/s11136-017-1729-8 [DOI] [PubMed] [Google Scholar]
  35. Koldjeski D, Kirkpatrick MK, Swanson M, Everett L, & Brown S (2003). Ovarian cancer: Early symptom patterns. Oncology Nursing Forum, 30(6), 927–933. 10.1188/03.ONF.927-933 [DOI] [PubMed] [Google Scholar]
  36. Lal CV, Bhandari V, & Ambalavanan N (2018). Genomics, microbiomics, proteomics, and metabolomics in bronchopulmonary dysplasia. Seminars in Perinatology, 42(7), 425–431. 10.1053/j.semperi.2018.09.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lee YC, King MT, O’Connell RL, Lancele A, Joly F, Hilpert F, … Friedlander ML (2022). Symptom burden and quality of life with chemotherapy for recurrent ovarian cancer: The Gynecologic Cancer InterGroup–Symptom Benefit Study. International Journal of Gyneco-logic Cancer, 32(6), 761–768. 10.1136/ijgc-2021-003142 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Liavaag AH, Dorum A, Fosså SD, Tropé C, & Dahl AA (2007). Controlled study of fatigue, quality of life, and somatic and mental morbidity in epithelial ovarian cancer survivors: How lucky are the lucky ones? Journal of Clinical Oncology, 25(15), 2049–2056. 10.1200/JCO.2006.09.1769 [DOI] [PubMed] [Google Scholar]
  39. Lim AWW, Mesher D, Gentry-Maharaj A, Balogun N, Jacobs I, Menon U, & Sasieni P (2012). Predictive value of symptoms for ovarian cancer: Comparison of symptoms reported by questionnaire, interview, and general practitioner notes. Journal of the National Cancer Institute, 104(2), 114–124. 10.1093/jnci/djr486 [DOI] [PubMed] [Google Scholar]
  40. Lurie G, Thompson PJ, McDuffie KE, Carney ME, & Goodman MT (2009). Prediagnostic symptoms of ovarian carcinoma: A case-control study. Gynecologic Oncology, 114(2), 231–236. 10.1016/j.ygyno.2009.05.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lurie G, Wilkens LR, Thompson PJ, Matsuno RK, Carney ME, & Goodman MT (2010). Symptom presentation in invasive ovarian carcinoma by tumor histological type and grade in a multiethnic population: A case analysis. Gynecologic Oncology, 119(2), 278–284. 10.1016/j.ygyno.2010.05.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Misra BB, Langefeld CD, Olivier M, & Cox LA (2018). Integrated omics: Tools, advances, and future approaches. Journal of Molecular Endocrinology, 62(1), R21–R24. 10.1530/JME-18-0055 [DOI] [PubMed] [Google Scholar]
  43. National Cancer Institute. (n.d.). NCI dictionary of cancer terms. https://www.cancer.gov/publications/dictionaries/cancer-terms
  44. National Cancer Institute. (2018). SEER relative survival rates by time since diagnosis, 2000–2018. https://bit.ly/3PskApw
  45. National Institutes of Health. (2020). National Institute of Nursing Research: The NIH almanac. https://www.nih.gov/about-nih/what-we-do/nih-almanac/national-institute-nursing-research-ninr
  46. Nho J-H, Kim SR, & Nam J-H (2017). Symptom clustering and quality of life in patients with ovarian cancer undergoing chemotherapy. European Journal of Oncology Nursing, 30, 8–14. 10.1016/j.ejon.2017.07.007 [DOI] [PubMed] [Google Scholar]
  47. Nyrop KA, Deal AM, Reeve BB, Basch E, Chen YT, Park JH, … Muss HB (2020). Congruence of patient- and clinician-reported toxicity in women receiving chemotherapy for early breast cancer. Cancer, 126(13), 3084–3093. 10.1002/cncr.32898 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Olsen CM, Cnossen J, Green AC, & Webb PM (2007). Comparison of symptoms and presentation of women with benign, low malignant potential and invasive ovarian tumors. European Journal of Gynaecological Oncology, 28(5), 376–380. [PubMed] [Google Scholar]
  49. Oncology Nursing Society. (n.d.-a). Genomics and Precision Oncology Learning Library. https://www.ons.org/learning-libraries/precision-oncology [DOI] [PubMed]
  50. Oncology Nursing Society. (n.d.-b). Genomics taxonomy. https://www.ons.org/genomics-taxonomy
  51. Peterson RE, Kuchenbaecker K, Walters RK, Chen C-Y, Popejoy AB, Periyasamy S, … Duncan LE (2019). Genome-wide association studies in ancestrally diverse populations: Opportunities, methods, pitfalls, and recommendations. Cell, 179(3), 589–603. 10.1016/j.cell.2019.08.051 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Rietveld MJA, Husson O, Vos MCC, van de Poll-Franse LV, Ottevanger PBN, & Ezendam NPM (2019). Presence of gastro-intestinal symptoms in ovarian cancer patients during survivorship: A cross-sectional study from the PROFILES registry. Supportive Care in Cancer, 27(6), 2285–2293. 10.1007/s00520-018-4510-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Rossing MA, Wicklund KG, Cushing-Haugen KL, & Weiss NS (2010). Predictive value of symptoms for early detection of ovarian cancer. Journal of the National Cancer Institute, 102(4), 222–229. 10.1093/jnci/djp500 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Samuel CA, Schaal J, Robertson L, Kollie J, Baker S, Black K, … Cykert S (2018). Racial differences in symptom management experiences during breast cancer treatment. Supportive Care in Cancer, 26(5), 1425–1435. 10.1007/s00520-017-3965-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Schwartzberg L, Kim ES, Liu D, & Schrag D (2018). Precision oncology: Who, how, what, when, and when not? American Society of Clinical Oncology Educations Book, 37, 160–169. 10.1200/EDBK_174176 [DOI] [PubMed] [Google Scholar]
  56. Siegel RL, Miller KD, Fuchs HE, & Jemal A (2021). Cancer statistics, 2021. CA: A Cancer Journal for Clinicians, 71(1), 7–33. 10.3322/caac.21654 [DOI] [PubMed] [Google Scholar]
  57. Siegel RL, Miller KD, Fuchs HE, & Jemal A (2022). Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, 72(1), 7–33. 10.3322/caac.21708 [DOI] [PubMed] [Google Scholar]
  58. Srivastava A, & Creek DJ (2019). Discovery and validation of clinical biomarkers of cancer: A review combining metabolomics and proteomics. Proteomics, 19(10), e1700448. 10.1002/pmic.201700448 [DOI] [PubMed] [Google Scholar]
  59. Stafford L, Russell H, Knoetze E, Wilson V, & Little R (2022). Sexual functioning after ovarian cancer: Are women receiving the information and support they need? Supportive Care in Cancer, 30(6), 4583–4586. 10.1007/s00520-021-06733-5 [DOI] [PubMed] [Google Scholar]
  60. Stavraka C, Ford A, Ghaem-Maghami S, Crook T, Agarwal R, Gabra H, & Blagden S (2012). A study of symptoms described by ovarian cancer survivors. Gynecologic Oncology, 125(1), 59–64. 10.1016/j.ygyno.2011.12.421 [DOI] [PubMed] [Google Scholar]
  61. Tanay MA, & Armes J (2019). Lived experiences and support needs of women who developed chemotherapy-induced peripheral neuropathy following treatment for breast and ovarian cancer. European Journal of Cancer Care, 28(3), e13011. 10.1111/ecc.13011 [DOI] [PubMed] [Google Scholar]
  62. Torre LA, Trabert B, DeSantis CE, Miller KD, Samimi G, Runowicz CD, … Siegel RL (2018). Ovarian cancer statistics, 2018. CA: A Cancer Journal for Clinicians, 68(4), 284–296. 10.3322/caac.21456 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Trojan A, Leuthold N, Thomssen C, Rody A, Winder T, Jakob A, … Jackisch C (2021). The effect of collaborative reviews of electronic patient-reported outcomes on the congruence of patient-and clinician-reported toxicity in cancer patients receiving systemic therapy: Prospective, multicenter, observational clinical trial. Journal of Medical Internet Research, 23(8), e29271. 10.2196/29271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Umaretiya PJ, Wolfe J, & Bona K (2021). Naming the problem: A structural racism frame-work to examine disparities in palliative care. Journal of Pain and Symptom Management, 63(5), e461–e463. 10.1016/j.jpainsymman.2021.07.035 [DOI] [PubMed] [Google Scholar]
  65. Vine MF, Calingaert B, Berchuck A, & Schildkraut JM (2003). Characterization of prediagnostic symptoms among primary epithelial ovarian cancer cases and controls. Gynecologic Oncology, 90(1), 75–82. 10.1016/s0090-8258(03)00175-6 [DOI] [PubMed] [Google Scholar]
  66. Webb PM, Purdie DM, Grover S, Jordan S, Dick ML, & Green AC (2004). Symptoms and diagnosis of borderline, early and advanced epithelial ovarian cancer. Gynecologic Oncology, 92(1), 232–239. 10.1016/j.ygyno.2003.09.005 [DOI] [PubMed] [Google Scholar]
  67. Webber K, Carolus E, Mileshkin L, Sommeijer D, McAlpine J, Bladgen S, … Friedlander M (2019). OVQUEST—Life after the diagnosis and treatment of ovarian cancer—An international survey of symptoms and concerns in ovarian cancer survivors. Gynecologic Oncology, 155(1), 126–134. 10.1016/j.ygyno.2019.08.009 [DOI] [PubMed] [Google Scholar]
  68. Weinstein E, Kemmann M, Douglas SL, Daly B, & Levitan N (2022). Quality and cost outcomes of an integrated supportive care program. Supportive Care in Cancer, 30(1), 535–542. 10.1007/s00520-021-06450-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Weldring T, & Smith SMS (2013). Patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs). Health Services Insights, 6, 61–68. 10.4137/HSI.S11093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Williams P, Murchie P, & Bond C (2019). Patient and primary care delays in the diagnostic pathway of gynaecological cancers: A systematic review of influencing factors. British Journal of General Practice, 69(679), e106–e111. 10.3399/bjgp19X700781 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Xiao C, Polomano R, & Bruner DW (2013). Comparison between patient-reported and clinician-observed symptoms in oncology. Cancer Nursing, 36(6), E1–E16. 10.1097/NCC.0b013e318269040f [DOI] [PubMed] [Google Scholar]
  72. Ye M, Lin Y, Pan S, Wang ZW, & Zhu X (2021). Applications of multi-omics approaches for exploring the molecular mechanism of ovarian carcinogenesis. Frontiers in Oncology, 11, 745808. 10.3389/fonc.2021.745808 [DOI] [PMC free article] [PubMed] [Google Scholar]

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