A Fact-Finding Survey of the Concerns of Patients with Cancer: A Network Analysis

A Fact-Finding Survey of the Concerns of Patients with Cancer: A Network Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Fact-Finding Survey of the Concerns of Patients with Cancer: A Network Analysis Kazumasa Yamamoto, Yuko Tanabe, Kiyomi Nonogaki, Hiroki Okumura, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4849633/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Despite advances in supportive cancer care, patients experience various interrelated concerns affecting their quality of life. This study aimed to elucidate the frequency, severity, and complex interrelationships of diverse physical, psychological, and social concerns among patients with cancer. Methods In this cross-sectional study, a 74-item questionnaire assessing symptoms and problems across 12 categories was administered to 300 patients with various cancer types. Each item was rated from 0 (none) to 3 (severe). Sex and cancer type differences were analyzed. Network analysis examined and visualized the centrality and clustering of patient concerns. Results Overall, 127 males and 173 females (median age, 66 years) participated in this study. Cancer types included breast (28.0%), gastrointestinal (27.3%), urologic (17.3%), hepatobiliary/pancreatic (14.7%), gynecological (6.7%), and others (6.0%). Females reported significantly higher overall distress than males (30.4 vs. 22.5, p < 0.01). The most common concerns were physical decline (81.7%), fatigue (80.5%), muscle weakness (65.9%), numbness/pain (63.0%), and hair loss (54.9%). Items with the highest centrality were muscle weakness, nutritional management, fatigue, changes in appearance, and physical decline. Network structures differed between sexes, with males exhibiting higher centrality in sexual function and social concerns and females in psychological symptoms. Conclusions This study elucidated the complex symptom interrelationships among the concerns of patients with cancer. Females experienced a greater symptom burden than males. Fatigue, weakness, and nutritional management were central symptoms linked to other concerns. These intricate symptom networks highlight the need for multidisciplinary interventions targeting multiple interconnected concerns to optimize supportive care. Therefore, sex-specific approaches are warranted. Cancer symptoms network analysis sex differences symptom clusters symptom centrality patient care Figures Figure 1 Introduction Considerable therapeutic progress in oncology has improved patient outcomes and increased the diversity and complexity of concerns of patients with cancer [ 1 , 2 ]. Although advancements such as new antiemetic drugs have reduced treatment-related distress [ 3 , 4 ], various physical, psychological, and social challenges still profoundly impact the quality of life and wellbeing of patients [ 5 – 8 ]. Moreover, the frequency and severity of such concerns may vary based on cancer type, sex, and age [ 9 – 12 ]. For example, females appear more susceptible to psychosocial issues such as body image changes and relationship strain than males [ 9 , 13 , 14 ]. Sex also influences medication side effects, with females experiencing a higher risk of severe adverse drug reactions than males [ 15 – 16 ]. Notably, cancer-related symptoms tend to co-occur in clusters rather than in isolation [ 17 ]. The concept of “symptom clusters” allows for better understanding of the interrelatedness of concurrent symptoms with shared underlying pathogenic mechanisms [ 18 – 20 ]. Nevertheless, research on symptom clusters is still emerging. Network analysis provides a novel approach for visualizing and quantifying the complex architecture of symptom clusters [ 21 , 22 ]. This method models individual symptoms as nodes in a network, with connections between the nodes representing the strength of their associations. Centrality indices, such as degree centrality, identify highly interconnected nodes that may be core symptoms that influence others in the network [ 22 ]. Recently, a few studies have used network analysis to map cancer symptom clusters [ 23 – 25 ]; however, they have examined a limited range of concerns. For example, Kalantari et al. included 38 symptoms in their network analysis of 987 patients with breast, gastrointestinal, gynecological, and lung cancers [ 23 ]. As the symptoms of patients with cancer are diverse and complex, extensive investigation is required. Thus, in this study, we aimed to explore the prevalence and severity of diverse symptoms and problems among patients with cancer, characterize symptom interrelationships, and identify core concerns using network analysis. This novel approach allows for a more comprehensive understanding of the intricate web of symptom interactions. Understanding these complex symptom networks is crucial for developing holistic integrated interventions to ease multidimensional symptom burden and enhance quality of life for patients with cancer. This study's significance lies in its potential to inform targeted, effective therapeutic strategies and improve patient care outcomes in oncology. Methods Study Participants This cross-sectional study recruited patients with cancer visiting Toranomon Hospital in Tokyo, Japan, between August 2021 and December 2022. The recruited participants met the following inclusion criteria: (i) ≥ 18 years old and (ii) diagnosed with cancer. Written informed consent was obtained from all participants. This study was approved by the Institutional Review Board of Toranomon Hospital, Japan. Measures A self-reported questionnaire was developed based on a literature review and multidisciplinary input from oncology clinicians, nurses, and pharmacists to capture the breadth of concerns of patients with cancer. It consisted of 74 questions across 12 categories (Supplementary Table 1 in the Online Resource). Patient consent forms and questionnaire responses were cross-referenced with medical records to gather additional clinical information. Patients were asked to rate their current distress levels using a 4-point scale: 0 (none), 1 (mild), 2 (moderate), and 3 (severe). The total score ranged from 0 to 222, with higher scores indicating greater symptom distress. Participants also provided sociodemographic information. Clinical data on cancer type, stage, and treatment history were extracted from their medical records. Statistical Analysis Descriptive statistics summarized patient characteristics. The overall and item-specific symptom prevalence (percentage of the sample endorsing concern at any severity level) and average distress ratings were calculated. The frequency and mean degree of distress were tabulated based on cancer type and sex. Differences in total distress scores between male and female patients were assessed using a t- test, whereas differences between diseases were evaluated using a one-way analysis of variance. A two-tailed p-value < 0.05 was considered statistically significant. All statistical analyses were performed using JMP, version 16 (SAS Institute, Cary, NC, USA). Network Analysis A network analysis of the 74 symptoms included in the questionnaire was conducted. Each symptom was represented as a node in the network, with edges representing the correlation coefficient between two nodes. Thicker edges indicated a stronger correlation between connected nodes, whereas larger nodes represented a higher number of edges and greater connectivity to other symptoms. NetworkX 2.8.6 and Pyvis 0.2.1 were used to calculated and visualize data with complex network structures in Python. Node Centrality Node centrality is an indicator used to identify the core symptoms from a mechanistic perspective. In our analysis, we used the degree centrality index, which measures the total number of direct connections of a specific symptom node within a network. Degree centrality helps identify nodes in the network that have the most direct impact or influence on other symptoms. Nodes with a higher degree of centrality were more influential in the network. Community Detection Algorithms To explore symptom clustering within the network, we employed the Girvan–Newman community detection algorithm [ 26 ]. This algorithm is commonly applied in both online and offline network analyses and involves recalculating the edge betweenness throughout the process. Initially, edge betweenness values were calculated for each edge in the graph. Subsequently, the edges with the highest betweenness values were sequentially removed, leaving the nodes in place. This process is repeated until no edges remain, thereby facilitating community identification within the network. Each community was represented by a different color. Results Patient Characteristics Between August 2021 and December 2022, 316 patients with cancer participated in this study; 16 individuals (5%) declined participation, resulting in a final enrollment of 300 patients (127 males and 173 females). The median age of the patients was 66 years. The most common type of cancer was breast (84 patients, 28.0%), followed by gastrointestinal (82 patients, 27.3%) and urologic (52 patients, 17.3%). More than 90% of patients had metastatic or recurrent disease, and > 80% underwent chemotherapy (Table 1 ). Table 1 Patient characteristics (n = 300) Median age (range) 66 (25–90) Older patients (≥ 65) 165 (55.0) Non-older patients (< 65) 135 (45.0) Sex, n (%) Male 127 (42.3) Female 173 (57.7) Type of cancer, n (%) Breast cancer 84 (28.0) Gastrointestinal cancer 82 (27.3) Urologic cancer 52 (17.3) Hepatobiliary/pancreatic cancer 44 (14.3) Gynecologic cancer 20 (6.7) Other 18 (6.0) Treatment, n (%) Chemotherapy 254 (84.7) Chemoradiation 3 (1.0) Hormonal 29 (9.7) Observation 6 (2.0) BSC 8 (2.7) Extent of disease Metastatic/Recurrence 274 (91.3) Local 26 (8.7) Abbreviation: BSC, best supportive care. Prevalence and Severity of Patient Concerns The prevalence rates of individual concerns are presented in Table 2 . The five most frequently reported issues were physical decline (81.7%), fatigue (80.5%), muscle weakness (65.9%), numbness/abnormal sensations (63.0%), and hair loss (54.9%). Appearance changes and hair loss were more common in females, whereas the impact on spousal relationships and financial strain were relatively more prevalent in males. Total symptom distress scores ranged from 0 to 222, with a mean of 26.4 (standard deviation = 20.2). As displayed in Table 3 , females reported a significantly higher overall symptom burden than males (30.4 vs. 22.5, p < 0.01) and scored higher in most individual problem areas. Distress levels tended to be higher for female-dominant cancers such as breast and gynecological malignancies; however, the differences across cancer types were not significant. Table 2 Most frequent symptoms All population Female Male 1 Physical decline 81.7% Physical decline 83.2% Physical decline 79.5% 2 Fatigue 80.5% Fatigue 83.1% Fatigue 77.0% 3 Muscle weakness 65.9% Muscle weakness 67.6% Muscle weakness 63.5% 4 Numbness 63.0% Numbness 67.3% Numbness 57.1% 5 Hair loss 54.9% Hair loss 66.5% Impact on spouse 49.2% 6 Other pain a 52.0% Appearance change 59.2% Other pain a 46.7% 7 Anxiety 50.5% Anxiety 56.1% Constipation 45.6% 8 Constipation 48.7% Other pain a 55.8% Depression 43.2% 9 Depression 47.8% Depression 51.2% Anxiety 42.7% 10 Impact on spouse 46.3% Constipation 50.9% Finance 39.8% a Other pain refers to pain other than a headache or abdominal pain. Table 3 Mean of the total score by category Category Mean of the total score P value Overall 27.06 ± 19.18 Sex Female 30.79 ± 20.57 p < 0.01 Male 22.53 ± 16.09 Cancer type Breast cancer 30.45 ± 20.99 p = 0.12 Gynecologic cancer 30.35 ± 19.90 Hepatobiliary/pancreatic cancer 27.66 ± 16.86 Gastrointestinal cancer 26.63 ± 16.86 Urologic cancer 20.86 ± 19.21 Other Cancer 26.00 ± 15.90 Age Older patients (≥ 65) 25.63 ± 18.96 p = 0.15 Non-older patients (< 65) 28.81 ± 18.96 Network Structure and Clustering of Concerns Figure 1 shows the network diagram. Three clusters were identified in the overall population and are represented by three different colors. A cluster of 8 items (pink) primarily included problems related to changes in appearance, and the largest cluster of 21 items (blue) primarily included problems related to cancer-related fatigue (CRF). A cluster of 11 items (gray) primarily included problems related to social issues. The items with the five highest centrality scores were “muscle weakness (0.15),” “nutrition management (0.12),” “appearance change (0.08),” “fatigue (0.08),” and “physical decline (0.07).” Despite being the 22nd most prevalent concern (29.4%), nutritional status had the second-highest centrality, indicating strong interconnectedness with other symptoms. In the female cohort, we identified a 5-item cluster (pink) that primarily included problems related to changes in appearance, a large 42-item cluster (blue) that primarily included problems related to CRF, and an 8-item cluster (yellow) that primarily included problems related to examinations and treatments. The items with the highest centrality scores were “muscle weakness (0.18),” “nutrition management (0.14),” “depression (0.14),” “fatigue (0.14),” and “physical decline (0.12).” Notably, “nutrition management” had the second-highest centrality score of the 74 items, similar to that in the overall population. For males, a more diffuse network structure emerged, including a 23-item cluster (yellow) of appearance, psychological, and cancer care issues; a 16-item cluster (blue) of fatigue and social concerns; and a 9-item cluster (light blue) related to sexual well-being and treatment/procedure problems. The highest centrality nodes for males were “cancer counseling (0.12),” “sexual life (0.12),” “muscle weakness (0.11),” “other hair loss (0.11),” “appearance change (0.08),” and “nail changes (0.08).” Discussion This study comprehensively investigated the symptoms and concerns of patients with cancer and evaluated their complex interrelationships. Our key findings include the high prevalence of “physical decline,” “fatigue,” and “muscle weakness,” the significant interrelationships among these symptoms, and the notable centrality of “nutrition management,” despite its lower prevalence. Additionally, we uncovered significant sex-related differences, with females experiencing a greater symptom burden than males. We found that “physical decline,” “fatigue,” and “muscle weakness” were the most common concerns. Fatigue is the most common symptom experienced by patients with cancer, with the majority experiencing it between diagnosis and end of life and for several years after treatment ends [ 27 , 28 ], consistent with the results of this study. The cause of CRF is not yet fully understood; however, it is known to be multifactorial and influenced by medical, psychosocial, behavioral, and biological factors [ 29 , 30 ]. Other potential factors include medical comorbidities, medications, nutritional problems, physical ailments, mood disorders, and physical symptoms, making it a complex condition [ 31 ]. In the network and cluster analyses conducted in this study, “physical decline,” “fatigue,” and “muscle weakness,” which may reflect CRF, had high centrality scores. Although various interventions have been investigated for treating CRF, including physical activity, psychosocial support, and pharmacological interventions, no “gold standard” treatment is currently available [ 32 ]. For example, although several systematic reviews and meta-analyses show that exercise is effective in improving fatigue, muscle strength, and activity levels [ 33 , 34 ], no standardized prescription exists. The guidelines also recommend informational support, counseling, and psychoeducation for patients with cancer and their caregivers to help them understand CRF and learn how to prevent, manage, or avoid chronic fatigue [ 35 – 37 ], and a comprehensive approach is important. The high correlation and centrality of “physical decline,” “fatigue,” and “muscle weakness” in this study suggested an unclear causal relationship. However, the strong correlation of “fatigue” and “muscle weakness” with various items (high centrality) supports the current guidelines advocating for a comprehensive approach to address CRF. Notably, the centrality score for “nutrition management” was high in this study. Although “nutrition management” was not the most frequent concern, ranking 22nd of 74 overall concerns with a frequency of 45.0%, it had the second-highest centrality score in both the overall population and the female subgroup. Malnutrition in patients with cancer has been associated with decreased overall survival, reduced therapy benefits, increased toxicity, and diminished quality of life [ 38 – 43 ]. In the present study, nutritional management was associated with items such as “weight loss,” “depression,” “anxiety,” “fatigue,” “muscle weakness,” “appearance changes,” “cancer counseling,” and “rehabilitation,” forming a diverse cluster. At present, evidence for nutritional management in patients with cancer is limited, and it remains unclear when and how to intervene and whether such nutritional interventions improve clinical outcomes [ 44 , 45 ]. The positioning of nutritional symptoms as a hub in the network implies that dietary interventions may have a cascading effect on resolving other interconnected concerns. Finally, our study revealed significant sex-related differences, with females affected more frequently and severely. Specifically, females experienced higher frequencies than males in 60 of 74 items and higher mean values in 62 items. In the network and cluster analyses, “muscle weakness” had the highest centrality score, regardless of sex. On the other hand, “sexual life” (0.12 vs. 0.00) and “cancer counseling” (0.12 vs. 0.04) had the highest centrality scores for the male cohort, whereas “depression” (0.14 vs. 0.04) and “anxiety” (0.11 vs. 0.04) had higher centrality scores for the female cohort, indicating sex differences in centrality. Although previous studies have shown sex differences in symptoms and concerns [ 9 , 10 , 13 , 14 ], differences between males and females have also been observed in network and cluster analyses. Males may be more susceptible to the loss of social functions and roles, whereas females may have psychological symptoms at the core of their distress. This study also suggested the need to consider sex differences when providing comprehensive care to patients with cancer. This study is novel in several respects. First, it provides a comprehensive examination of 74 symptoms and concerns in patients with cancer, a scope that is broader than many previous studies. Previous research has often focused on a limited number of symptoms or specific types of cancer, whereas our study included a wide range of symptoms across various cancer types. Second, the use of network and cluster analyses to identify interrelationships and centrality of symptoms is relatively novel in this context. This approach allowed us to uncover the complex interactions between symptoms, highlighting key symptoms that may serve as targets for intervention. For example, the high centrality of “nutrition management” suggests that interventions in this area could potentially alleviate multiple related symptoms. Additionally, the identification of significant sex-related differences adds a critical dimension to our understanding of cancer symptomatology. While previous studies have reported sex differences, our network analysis provides a more nuanced view of how these differences manifest in symptom interrelationships. This may have important implications for developing personalized interventions that address the specific needs of male and female patients differently. This study was a comprehensive survey of the concerns of patients with cancer; however, it had some limitations. Although this study included patients with various cancer types and treatments, unifying them into the same treatment and patient population was difficult owing to the study design. Future studies of patients with matched background factors, such as sex, age, and cancer type, should provide further insights. Additionally, as this study was conducted at a single medical institution in Tokyo, regional imbalance may exist. Given that the questionnaire was administered in Japanese, it may not be easily generalizable to diverse cultural and linguistic backgrounds. Conclusions This study performed network and cluster analyses, demonstrating that the various concerns of patients with cancer formed closely interrelated clusters. Females experienced a greater symptom burden than males. Fatigue, weakness, and nutritional management were central symptoms linked to other concerns. The delineation of these densely connected symptom clusters highlights the necessity of an integrated, multidisciplinary approach for supportive oncology care to ease the multifaceted distress experienced by patients with cancer. Therefore, a multidisciplinary care team should concurrently address multiple symptoms within a cluster, rather than a single symptom in isolation. Declarations Acknowledgements The authors thank the participants for their contribution to this study. We would like to express our gratitude to the team at Sobal Corporation for conducting the network analysis and cluster analysis. Their expertise and support were invaluable to this research. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Competing interests The authors have declared no conflicts of interest regarding this study. Authors’ Contributions Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work: Kazumasa Yamamoto, Yuko Tanabe, Kiyomi Nonogaki, Yuji Miura. Drafting the work or critically reviewing it for important intellectual content: Kazumasa Yamamoto, Yuji Miura. Final approval of the version to be published: Kazumasa Yamamoto, Yuji Miura. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: All authors. Ethics approval This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Toranomon Hospital, Tokyo, Japan (Approval No. 2239). Consent to participate Written informed consent was obtained from all participants. Participants were provided with detailed information about the study's aims, the nature of their involvement, and data confidentiality, ensuring voluntary participation and protection of their rights. Consent to publish Not applicable. Data availability Data supporting the findings of this study are available from the corresponding author upon request. References Ribas A, Wolchok JD (2018) Cancer immunotherapy using checkpoint blockade. Science 359:1350–1355. https://doi.org/10.1126/science.aar4060 Biankin AV, Piantadosi S, Hollingsworth SJ (2015) Patient-centric trials for therapeutic development in precision oncology. Nature 526:361–370. https://doi.org/10.1038/nature15819 Hashimoto H, Abe M, Tokuyama O, et al. 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J Nutr Health Aging 28:100274. https://doi.org/10.1016/j.jnha.2024.100274 Parsons HM, Forte ML, Abdi HI, et al (2023) Nutrition as prevention for improved cancer health outcomes: a systematic literature review. JNCI Cancer Spectr 7:pkad035. https://doi.org/10.1093/jncics/pkad035 Hiatt RA, Clayton MF, Collins KK, et al (2023) The Pathways to Prevention program: nutrition as prevention for improved cancer outcomes. J Natl Cancer Inst 115:886–895. https://doi.org/10.1093/jnci/djad079 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Supplementary Table 1. Questionnaire Items in the Survey Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4849633","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336390695,"identity":"f7760df1-65a9-47c6-920e-023dc28b9b2e","order_by":0,"name":"Kazumasa Yamamoto","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kazumasa","middleName":"","lastName":"Yamamoto","suffix":""},{"id":336390696,"identity":"858117df-59b2-406c-826e-d88a41515f79","order_by":1,"name":"Yuko Tanabe","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuko","middleName":"","lastName":"Tanabe","suffix":""},{"id":336390698,"identity":"7c404a3d-457f-4e8d-85e0-ba60c03e071f","order_by":2,"name":"Kiyomi Nonogaki","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kiyomi","middleName":"","lastName":"Nonogaki","suffix":""},{"id":336390700,"identity":"5a50d51e-2d7b-489d-a706-c7f445ecde2d","order_by":3,"name":"Hiroki Okumura","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Okumura","suffix":""},{"id":336390702,"identity":"001f38d9-ca20-4e93-8c1c-83340ff1ba80","order_by":4,"name":"Haruka Ozaki","email":"","orcid":"","institution":"Toranomon 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Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Ogura","suffix":""},{"id":336390726,"identity":"c52b8a9c-88a1-483a-a3b2-98e902c48133","order_by":14,"name":"Nobuko Tamura","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nobuko","middleName":"","lastName":"Tamura","suffix":""},{"id":336390730,"identity":"27d53b0f-dd4e-4552-b297-6c8f94db5f12","order_by":15,"name":"Hidetaka Kawabata","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hidetaka","middleName":"","lastName":"Kawabata","suffix":""},{"id":336390732,"identity":"4df33906-5e33-4c8f-9ee6-802e3f3916bd","order_by":16,"name":"Koichi Suyama","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Suyama","suffix":""},{"id":336390734,"identity":"22ed8a81-46d6-4037-8889-32b32f9b62ba","order_by":17,"name":"Yuji Miura","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYHCCBCBkYOAnXYtkA8l2GRwgWuWNhKcbHrbdkze+dvjhB4Yam2hitKTdSGwrNtx2O81YguFYWi5BB0K1JDBuu51gxsDYcJh4LfabZ6d/I01L4gbpHCJtkTzzIO1GwrmE5Bm3c4olEojxC9/xnLSbP8oSbPtnp2/88KHGhrAWhQM8CQheAi5lyEC+gf0AMepGwSgYBaNgJAMAXdtGKzzZsZQAAAAASUVORK5CYII=","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yuji","middleName":"","lastName":"Miura","suffix":""}],"badges":[],"createdAt":"2024-08-02 16:10:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4849633/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4849633/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64247737,"identity":"9fa973ce-cd3e-4c09-868c-41d4f0c66f90","added_by":"auto","created_at":"2024-09-10 20:44:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":973223,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagrams and clusters. (a) Total population. (b) Female. (c) Male. Network diagram illustrating the interrelationships among the symptoms. Each node represents a symptom, and the edges between nodes represent the correlation coefficients between the symptoms. Thicker edges indicate stronger correlations, whereas larger nodes represent higher degrees of connectivity with other symptoms. The three distinct clusters are highlighted in different colors\u003c/p\u003e\n\u003cp\u003eFig. 1-1. All population\u003c/p\u003e\n\u003cp\u003eFig. 1-2. Female\u003c/p\u003e\n\u003cp\u003eFig. 1-3 Male\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4849633/v1/08a1b65bf796e58b51dd1f5f.png"},{"id":64492675,"identity":"fb4a4111-96af-4819-8fe8-6c82b14b6d30","added_by":"auto","created_at":"2024-09-13 21:46:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1424143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4849633/v1/5ae37038-b421-4df0-a2e5-532c8d8ef987.pdf"},{"id":64247736,"identity":"7ecf79ac-065c-4a8b-802c-b3e4884b40e5","added_by":"auto","created_at":"2024-09-10 20:44:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003eQuestionnaire Items in the Survey\u003c/p\u003e","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4849633/v1/4e2ca183378e6b97436a27b1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Fact-Finding Survey of the Concerns of Patients with Cancer: A Network Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eConsiderable therapeutic progress in oncology has improved patient outcomes and increased the diversity and complexity of concerns of patients with cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although advancements such as new antiemetic drugs have reduced treatment-related distress [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], various physical, psychological, and social challenges still profoundly impact the quality of life and wellbeing of patients [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, the frequency and severity of such concerns may vary based on cancer type, sex, and age [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For example, females appear more susceptible to psychosocial issues such as body image changes and relationship strain than males [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Sex also influences medication side effects, with females experiencing a higher risk of severe adverse drug reactions than males [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, cancer-related symptoms tend to co-occur in clusters rather than in isolation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The concept of \u0026ldquo;symptom clusters\u0026rdquo; allows for better understanding of the interrelatedness of concurrent symptoms with shared underlying pathogenic mechanisms [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nevertheless, research on symptom clusters is still emerging. Network analysis provides a novel approach for visualizing and quantifying the complex architecture of symptom clusters [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This method models individual symptoms as nodes in a network, with connections between the nodes representing the strength of their associations. Centrality indices, such as degree centrality, identify highly interconnected nodes that may be core symptoms that influence others in the network [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Recently, a few studies have used network analysis to map cancer symptom clusters [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; however, they have examined a limited range of concerns. For example, Kalantari et al. included 38 symptoms in their network analysis of 987 patients with breast, gastrointestinal, gynecological, and lung cancers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the symptoms of patients with cancer are diverse and complex, extensive investigation is required. Thus, in this study, we aimed to explore the prevalence and severity of diverse symptoms and problems among patients with cancer, characterize symptom interrelationships, and identify core concerns using network analysis. This novel approach allows for a more comprehensive understanding of the intricate web of symptom interactions. Understanding these complex symptom networks is crucial for developing holistic integrated interventions to ease multidimensional symptom burden and enhance quality of life for patients with cancer. This study's significance lies in its potential to inform targeted, effective therapeutic strategies and improve patient care outcomes in oncology.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional study recruited patients with cancer visiting Toranomon Hospital in Tokyo, Japan, between August 2021 and December 2022. The recruited participants met the following inclusion criteria: (i)\u0026thinsp;\u0026ge;\u0026thinsp;18 years old and (ii) diagnosed with cancer. Written informed consent was obtained from all participants. This study was approved by the Institutional Review Board of Toranomon Hospital, Japan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eA self-reported questionnaire was developed based on a literature review and multidisciplinary input from oncology clinicians, nurses, and pharmacists to capture the breadth of concerns of patients with cancer. It consisted of 74 questions across 12 categories (Supplementary Table\u0026nbsp;1 in the Online Resource). Patient consent forms and questionnaire responses were cross-referenced with medical records to gather additional clinical information. Patients were asked to rate their current distress levels using a 4-point scale: 0 (none), 1 (mild), 2 (moderate), and 3 (severe). The total score ranged from 0 to 222, with higher scores indicating greater symptom distress. Participants also provided sociodemographic information. Clinical data on cancer type, stage, and treatment history were extracted from their medical records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarized patient characteristics. The overall and item-specific symptom prevalence (percentage of the sample endorsing concern at any severity level) and average distress ratings were calculated. The frequency and mean degree of distress were tabulated based on cancer type and sex. Differences in total distress scores between male and female patients were assessed using a \u003cem\u003et-\u003c/em\u003etest, whereas differences between diseases were evaluated using a one-way analysis of variance. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using JMP, version 16 (SAS Institute, Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analysis\u003c/h2\u003e \u003cp\u003eA network analysis of the 74 symptoms included in the questionnaire was conducted. Each symptom was represented as a node in the network, with edges representing the correlation coefficient between two nodes. Thicker edges indicated a stronger correlation between connected nodes, whereas larger nodes represented a higher number of edges and greater connectivity to other symptoms. NetworkX 2.8.6 and Pyvis 0.2.1 were used to calculated and visualize data with complex network structures in Python.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNode Centrality\u003c/h2\u003e \u003cp\u003eNode centrality is an indicator used to identify the core symptoms from a mechanistic perspective. In our analysis, we used the degree centrality index, which measures the total number of direct connections of a specific symptom node within a network. Degree centrality helps identify nodes in the network that have the most direct impact or influence on other symptoms. Nodes with a higher degree of centrality were more influential in the network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Detection Algorithms\u003c/h2\u003e \u003cp\u003eTo explore symptom clustering within the network, we employed the Girvan\u0026ndash;Newman community detection algorithm [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This algorithm is commonly applied in both online and offline network analyses and involves recalculating the edge betweenness throughout the process. Initially, edge betweenness values were calculated for each edge in the graph. Subsequently, the edges with the highest betweenness values were sequentially removed, leaving the nodes in place. This process is repeated until no edges remain, thereby facilitating community identification within the network. Each community was represented by a different color.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eBetween August 2021 and December 2022, 316 patients with cancer participated in this study; 16 individuals (5%) declined participation, resulting in a final enrollment of 300 patients (127 males and 173 females). The median age of the patients was 66 years. The most common type of cancer was breast (84 patients, 28.0%), followed by gastrointestinal (82 patients, 27.3%) and urologic (52 patients, 17.3%). More than 90% of patients had metastatic or recurrent disease, and \u0026gt;\u0026thinsp;80% underwent chemotherapy (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (25\u0026ndash;90)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlder patients (\u0026ge;\u0026thinsp;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (55.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-older patients (\u0026lt;\u0026thinsp;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (45.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (42.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (57.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of cancer, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (28.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (27.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrologic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatobiliary/pancreatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGynecologic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254 (84.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemoradiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (9.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtent of disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic/Recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (91.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviation: BSC, best supportive care.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence and Severity of Patient Concerns\u003c/h2\u003e \u003cp\u003eThe prevalence rates of individual concerns are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The five most frequently reported issues were physical decline (81.7%), fatigue (80.5%), muscle weakness (65.9%), numbness/abnormal sensations (63.0%), and hair loss (54.9%). Appearance changes and hair loss were more common in females, whereas the impact on spousal relationships and financial strain were relatively more prevalent in males. Total symptom distress scores ranged from 0 to 222, with a mean of 26.4 (standard deviation\u0026thinsp;=\u0026thinsp;20.2). As displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, females reported a significantly higher overall symptom burden than males (30.4 vs. 22.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and scored higher in most individual problem areas. Distress levels tended to be higher for female-dominant cancers such as breast and gynecological malignancies; however, the differences across cancer types were not significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMost frequent symptoms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAll population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePhysical decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePhysical decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"9\" rowspan=\"10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuscle weakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMuscle weakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMuscle weakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumbness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNumbness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eNumbness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHair loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHair loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eImpact on spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther pain\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAppearance change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eOther pain\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eConstipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOther pain\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact on spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eConstipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eFinance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Other pain refers to pain other than a headache or abdominal pain.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean of the total score by category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean of the total score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.06\u0026thinsp;\u0026plusmn;\u0026thinsp;19.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.79\u0026thinsp;\u0026plusmn;\u0026thinsp;20.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.53\u0026thinsp;\u0026plusmn;\u0026thinsp;16.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.45\u0026thinsp;\u0026plusmn;\u0026thinsp;20.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGynecologic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.35\u0026thinsp;\u0026plusmn;\u0026thinsp;19.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatobiliary/pancreatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.66\u0026thinsp;\u0026plusmn;\u0026thinsp;16.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.63\u0026thinsp;\u0026plusmn;\u0026thinsp;16.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrologic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.86\u0026thinsp;\u0026plusmn;\u0026thinsp;19.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.00\u0026thinsp;\u0026plusmn;\u0026thinsp;15.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlder patients (\u0026ge;\u0026thinsp;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.63\u0026thinsp;\u0026plusmn;\u0026thinsp;18.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-older patients (\u0026lt;\u0026thinsp;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.81\u0026thinsp;\u0026plusmn;\u0026thinsp;18.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Structure and Clustering of Concerns\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the network diagram. Three clusters were identified in the overall population and are represented by three different colors. A cluster of 8 items (pink) primarily included problems related to changes in appearance, and the largest cluster of 21 items (blue) primarily included problems related to cancer-related fatigue (CRF). A cluster of 11 items (gray) primarily included problems related to social issues. The items with the five highest centrality scores were \u0026ldquo;muscle weakness (0.15),\u0026rdquo; \u0026ldquo;nutrition management (0.12),\u0026rdquo; \u0026ldquo;appearance change (0.08),\u0026rdquo; \u0026ldquo;fatigue (0.08),\u0026rdquo; and \u0026ldquo;physical decline (0.07).\u0026rdquo; Despite being the 22nd most prevalent concern (29.4%), nutritional status had the second-highest centrality, indicating strong interconnectedness with other symptoms. In the female cohort, we identified a 5-item cluster (pink) that primarily included problems related to changes in appearance, a large 42-item cluster (blue) that primarily included problems related to CRF, and an 8-item cluster (yellow) that primarily included problems related to examinations and treatments. The items with the highest centrality scores were \u0026ldquo;muscle weakness (0.18),\u0026rdquo; \u0026ldquo;nutrition management (0.14),\u0026rdquo; \u0026ldquo;depression (0.14),\u0026rdquo; \u0026ldquo;fatigue (0.14),\u0026rdquo; and \u0026ldquo;physical decline (0.12).\u0026rdquo; Notably, \u0026ldquo;nutrition management\u0026rdquo; had the second-highest centrality score of the 74 items, similar to that in the overall population. For males, a more diffuse network structure emerged, including a 23-item cluster (yellow) of appearance, psychological, and cancer care issues; a 16-item cluster (blue) of fatigue and social concerns; and a 9-item cluster (light blue) related to sexual well-being and treatment/procedure problems. The highest centrality nodes for males were \u0026ldquo;cancer counseling (0.12),\u0026rdquo; \u0026ldquo;sexual life (0.12),\u0026rdquo; \u0026ldquo;muscle weakness (0.11),\u0026rdquo; \u0026ldquo;other hair loss (0.11),\u0026rdquo; \u0026ldquo;appearance change (0.08),\u0026rdquo; and \u0026ldquo;nail changes (0.08).\u0026rdquo;\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study comprehensively investigated the symptoms and concerns of patients with cancer and evaluated their complex interrelationships. Our key findings include the high prevalence of \u0026ldquo;physical decline,\u0026rdquo; \u0026ldquo;fatigue,\u0026rdquo; and \u0026ldquo;muscle weakness,\u0026rdquo; the significant interrelationships among these symptoms, and the notable centrality of \u0026ldquo;nutrition management,\u0026rdquo; despite its lower prevalence. Additionally, we uncovered significant sex-related differences, with females experiencing a greater symptom burden than males.\u003c/p\u003e \u003cp\u003eWe found that \u0026ldquo;physical decline,\u0026rdquo; \u0026ldquo;fatigue,\u0026rdquo; and \u0026ldquo;muscle weakness\u0026rdquo; were the most common concerns. Fatigue is the most common symptom experienced by patients with cancer, with the majority experiencing it between diagnosis and end of life and for several years after treatment ends [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], consistent with the results of this study. The cause of CRF is not yet fully understood; however, it is known to be multifactorial and influenced by medical, psychosocial, behavioral, and biological factors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Other potential factors include medical comorbidities, medications, nutritional problems, physical ailments, mood disorders, and physical symptoms, making it a complex condition [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In the network and cluster analyses conducted in this study, \u0026ldquo;physical decline,\u0026rdquo; \u0026ldquo;fatigue,\u0026rdquo; and \u0026ldquo;muscle weakness,\u0026rdquo; which may reflect CRF, had high centrality scores. Although various interventions have been investigated for treating CRF, including physical activity, psychosocial support, and pharmacological interventions, no \u0026ldquo;gold standard\u0026rdquo; treatment is currently available [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For example, although several systematic reviews and meta-analyses show that exercise is effective in improving fatigue, muscle strength, and activity levels [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], no standardized prescription exists. The guidelines also recommend informational support, counseling, and psychoeducation for patients with cancer and their caregivers to help them understand CRF and learn how to prevent, manage, or avoid chronic fatigue [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and a comprehensive approach is important. The high correlation and centrality of \u0026ldquo;physical decline,\u0026rdquo; \u0026ldquo;fatigue,\u0026rdquo; and \u0026ldquo;muscle weakness\u0026rdquo; in this study suggested an unclear causal relationship. However, the strong correlation of \u0026ldquo;fatigue\u0026rdquo; and \u0026ldquo;muscle weakness\u0026rdquo; with various items (high centrality) supports the current guidelines advocating for a comprehensive approach to address CRF.\u003c/p\u003e \u003cp\u003eNotably, the centrality score for \u0026ldquo;nutrition management\u0026rdquo; was high in this study. Although \u0026ldquo;nutrition management\u0026rdquo; was not the most frequent concern, ranking 22nd of 74 overall concerns with a frequency of 45.0%, it had the second-highest centrality score in both the overall population and the female subgroup. Malnutrition in patients with cancer has been associated with decreased overall survival, reduced therapy benefits, increased toxicity, and diminished quality of life [\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In the present study, nutritional management was associated with items such as \u0026ldquo;weight loss,\u0026rdquo; \u0026ldquo;depression,\u0026rdquo; \u0026ldquo;anxiety,\u0026rdquo; \u0026ldquo;fatigue,\u0026rdquo; \u0026ldquo;muscle weakness,\u0026rdquo; \u0026ldquo;appearance changes,\u0026rdquo; \u0026ldquo;cancer counseling,\u0026rdquo; and \u0026ldquo;rehabilitation,\u0026rdquo; forming a diverse cluster. At present, evidence for nutritional management in patients with cancer is limited, and it remains unclear when and how to intervene and whether such nutritional interventions improve clinical outcomes [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The positioning of nutritional symptoms as a hub in the network implies that dietary interventions may have a cascading effect on resolving other interconnected concerns.\u003c/p\u003e \u003cp\u003eFinally, our study revealed significant sex-related differences, with females affected more frequently and severely. Specifically, females experienced higher frequencies than males in 60 of 74 items and higher mean values in 62 items. In the network and cluster analyses, \u0026ldquo;muscle weakness\u0026rdquo; had the highest centrality score, regardless of sex. On the other hand, \u0026ldquo;sexual life\u0026rdquo; (0.12 vs. 0.00) and \u0026ldquo;cancer counseling\u0026rdquo; (0.12 vs. 0.04) had the highest centrality scores for the male cohort, whereas \u0026ldquo;depression\u0026rdquo; (0.14 vs. 0.04) and \u0026ldquo;anxiety\u0026rdquo; (0.11 vs. 0.04) had higher centrality scores for the female cohort, indicating sex differences in centrality. Although previous studies have shown sex differences in symptoms and concerns [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], differences between males and females have also been observed in network and cluster analyses. Males may be more susceptible to the loss of social functions and roles, whereas females may have psychological symptoms at the core of their distress. This study also suggested the need to consider sex differences when providing comprehensive care to patients with cancer.\u003c/p\u003e \u003cp\u003eThis study is novel in several respects. First, it provides a comprehensive examination of 74 symptoms and concerns in patients with cancer, a scope that is broader than many previous studies. Previous research has often focused on a limited number of symptoms or specific types of cancer, whereas our study included a wide range of symptoms across various cancer types. Second, the use of network and cluster analyses to identify interrelationships and centrality of symptoms is relatively novel in this context. This approach allowed us to uncover the complex interactions between symptoms, highlighting key symptoms that may serve as targets for intervention. For example, the high centrality of \u0026ldquo;nutrition management\u0026rdquo; suggests that interventions in this area could potentially alleviate multiple related symptoms.\u003c/p\u003e \u003cp\u003eAdditionally, the identification of significant sex-related differences adds a critical dimension to our understanding of cancer symptomatology. While previous studies have reported sex differences, our network analysis provides a more nuanced view of how these differences manifest in symptom interrelationships. This may have important implications for developing personalized interventions that address the specific needs of male and female patients differently.\u003c/p\u003e \u003cp\u003eThis study was a comprehensive survey of the concerns of patients with cancer; however, it had some limitations. Although this study included patients with various cancer types and treatments, unifying them into the same treatment and patient population was difficult owing to the study design. Future studies of patients with matched background factors, such as sex, age, and cancer type, should provide further insights. Additionally, as this study was conducted at a single medical institution in Tokyo, regional imbalance may exist. Given that the questionnaire was administered in Japanese, it may not be easily generalizable to diverse cultural and linguistic backgrounds.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study performed network and cluster analyses, demonstrating that the various concerns of patients with cancer formed closely interrelated clusters. Females experienced a greater symptom burden than males. Fatigue, weakness, and nutritional management were central symptoms linked to other concerns. The delineation of these densely connected symptom clusters highlights the necessity of an integrated, multidisciplinary approach for supportive oncology care to ease the multifaceted distress experienced by patients with cancer. Therefore, a multidisciplinary care team should concurrently address multiple symptoms within a cluster, rather than a single symptom in isolation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participants for their contribution to this study. We would like to express our gratitude to the team at Sobal Corporation for conducting the network analysis and cluster analysis. Their expertise and support were invaluable to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflicts of interest regarding this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubstantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work: Kazumasa Yamamoto, Yuko Tanabe, Kiyomi Nonogaki, Yuji Miura. Drafting the work or critically reviewing it for important intellectual content: Kazumasa Yamamoto, Yuji Miura. Final approval of the version to be published: Kazumasa Yamamoto, Yuji Miura. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Toranomon Hospital, Tokyo, Japan (Approval No. 2239).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants. Participants were provided with detailed information about the study\u0026apos;s aims, the nature of their involvement, and data confidentiality, ensuring voluntary participation and protection of their rights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRibas A, Wolchok JD (2018) Cancer immunotherapy using checkpoint blockade. Science 359:1350\u0026ndash;1355. https://doi.org/10.1126/science.aar4060\u003c/li\u003e\n\u003cli\u003eBiankin AV, Piantadosi S, Hollingsworth SJ (2015) Patient-centric trials for therapeutic development in precision oncology. 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J Natl Cancer Inst 115:886\u0026ndash;895. https://doi.org/10.1093/jnci/djad079\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer symptoms, network analysis, sex differences, symptom clusters, symptom centrality, patient care","lastPublishedDoi":"10.21203/rs.3.rs-4849633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4849633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDespite advances in supportive cancer care, patients experience various interrelated concerns affecting their quality of life. This study aimed to elucidate the frequency, severity, and complex interrelationships of diverse physical, psychological, and social concerns among patients with cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, a 74-item questionnaire assessing symptoms and problems across 12 categories was administered to 300 patients with various cancer types. Each item was rated from 0 (none) to 3 (severe). Sex and cancer type differences were analyzed. Network analysis examined and visualized the centrality and clustering of patient concerns.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 127 males and 173 females (median age, 66 years) participated in this study. Cancer types included breast (28.0%), gastrointestinal (27.3%), urologic (17.3%), hepatobiliary/pancreatic (14.7%), gynecological (6.7%), and others (6.0%). Females reported significantly higher overall distress than males (30.4 vs. 22.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The most common concerns were physical decline (81.7%), fatigue (80.5%), muscle weakness (65.9%), numbness/pain (63.0%), and hair loss (54.9%). Items with the highest centrality were muscle weakness, nutritional management, fatigue, changes in appearance, and physical decline. Network structures differed between sexes, with males exhibiting higher centrality in sexual function and social concerns and females in psychological symptoms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study elucidated the complex symptom interrelationships among the concerns of patients with cancer. Females experienced a greater symptom burden than males. Fatigue, weakness, and nutritional management were central symptoms linked to other concerns. These intricate symptom networks highlight the need for multidisciplinary interventions targeting multiple interconnected concerns to optimize supportive care. Therefore, sex-specific approaches are warranted.\u003c/p\u003e","manuscriptTitle":"A Fact-Finding Survey of the Concerns of Patients with Cancer: A Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-10 20:44:08","doi":"10.21203/rs.3.rs-4849633/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33a691f1-8454-401f-8778-ffe4f58906d6","owner":[],"postedDate":"September 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-13T21:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-10 20:44:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4849633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4849633","identity":"rs-4849633","version":["v1"]},"buildId":"CiT4i_kKBbxQbnFL0ufpk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}