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已发表论文
基于网络分析识别不同化疗阶段乳腺癌患者的核心症状：一项前瞻性多中心纵向研究
Authors He J, Bai D, Chen H, Chen X, Lu X, Ji W, Xu R, Hou C, Gao J
Received 29 October 2025
Accepted for publication 28 January 2026
Published 11 February 2026 Volume 2026:20 568080
DOI https://doi.org/10.2147/PPA.S568080
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Johnny Chen
Jiali He,* Dingxi Bai,* Huan Chen,* Xinyu Chen, Xianying Lu, Wenting Ji, Ran Xu, Chaoming Hou, Jing Gao
School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Chaoming Hou; Jing Gao, Email 983729484@qq.com; 19942021@cdutcm.edu.cn
Background: Network analysis can identify the core symptoms reported by patients at each time point, providing a basis to develop precise symptom strategies for treatment. However, few studies have established symptom networks for breast cancer patients at various treatment stages.
Objective: The purpose of this study was to identify core symptoms that may be targeted for intervention in different chemotherapy phases for breast cancer patients.
Methods: A multicenter prospective longitudinal survey design was employed in this study. 467 breast cancer patients were recruited in the Breast Surgery Ward from November 2022 and October 2023. The Symptoms Assessment Scale for Breast Cancer Patients Receiving Chemotherapy was used to assess breast cancer patients’ symptoms before chemotherapy (T0), after the first chemotherapy (T1), after three chemotherapy treatments (T2), and after the sixth chemotherapy treatment (T3). Symptom networks and node properties were explored using network analysis.
Results: At T0, difficulty sleeping was a symptom with the highest strength (rs=2.43) and closeness (rc=1.51). Depression was a symptom with the highest betweenness (rb=2.47). At T1, decreased appetite was the symptom with the highest strength centrality (rs=2.22). Pain was the symptom with the highest closeness centrality (rc=1.48). Headache was a symptom with the highest betweenness centrality (rb=1.68). Fatigue was the symptom with the highest strength, closeness, and betweenness at T2 and T3 (T2: rs=1.67; rc=2.11; rb=2.30. T3: rs=1.96; rc=2.68; rb=2.15). Except for betweenness centrality at T1, all other centrality measures showed CS coefficients above 0.25, indicating that the symptom networks were substantially stable.
Conclusion: The core symptoms that need to be targeted for intervention in various chemotherapy stages of breast cancer patients differ. Difficulty sleeping and depression should be addressed at T0; decreased appetite and pain were the core symptoms at T1; and fatigue was the most central symptom at T2 and T3 and should be controlled as a priority.
Plain Language Summary: Breast cancer patients usually have many symptoms at the same time throughout the chemotherapy phase, resulting in a significant symptom burden. To better identify and manage these symptoms, we applied a technique called “network analysis” to construct “symptom networks” for breast cancer patients at different chemotherapy stages. Similar to social networks where influential individuals can affect many others, symptom networks contain core symptoms that influence other connected symptoms. By prioritizing interventions targeting these core symptoms, related symptoms may lose their “anchor points”, thereby alleviating the overall symptom burden. Our study found that the core symptoms in breast cancer patients change as chemotherapy progresses. This means that the focus of symptom management should also shift according to the treatment stage.
Keywords: breast cancer, chemotherapy, symptom network, core symptoms
School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Chaoming Hou; Jing Gao, Email 983729484@qq.com; 19942021@cdutcm.edu.cn
Background: Network analysis can identify the core symptoms reported by patients at each time point, providing a basis to develop precise symptom strategies for treatment. However, few studies have established symptom networks for breast cancer patients at various treatment stages.
Objective: The purpose of this study was to identify core symptoms that may be targeted for intervention in different chemotherapy phases for breast cancer patients.
Methods: A multicenter prospective longitudinal survey design was employed in this study. 467 breast cancer patients were recruited in the Breast Surgery Ward from November 2022 and October 2023. The Symptoms Assessment Scale for Breast Cancer Patients Receiving Chemotherapy was used to assess breast cancer patients’ symptoms before chemotherapy (T0), after the first chemotherapy (T1), after three chemotherapy treatments (T2), and after the sixth chemotherapy treatment (T3). Symptom networks and node properties were explored using network analysis.
Results: At T0, difficulty sleeping was a symptom with the highest strength (rs=2.43) and closeness (rc=1.51). Depression was a symptom with the highest betweenness (rb=2.47). At T1, decreased appetite was the symptom with the highest strength centrality (rs=2.22). Pain was the symptom with the highest closeness centrality (rc=1.48). Headache was a symptom with the highest betweenness centrality (rb=1.68). Fatigue was the symptom with the highest strength, closeness, and betweenness at T2 and T3 (T2: rs=1.67; rc=2.11; rb=2.30. T3: rs=1.96; rc=2.68; rb=2.15). Except for betweenness centrality at T1, all other centrality measures showed CS coefficients above 0.25, indicating that the symptom networks were substantially stable.
Conclusion: The core symptoms that need to be targeted for intervention in various chemotherapy stages of breast cancer patients differ. Difficulty sleeping and depression should be addressed at T0; decreased appetite and pain were the core symptoms at T1; and fatigue was the most central symptom at T2 and T3 and should be controlled as a priority.
Plain Language Summary: Breast cancer patients usually have many symptoms at the same time throughout the chemotherapy phase, resulting in a significant symptom burden. To better identify and manage these symptoms, we applied a technique called “network analysis” to construct “symptom networks” for breast cancer patients at different chemotherapy stages. Similar to social networks where influential individuals can affect many others, symptom networks contain core symptoms that influence other connected symptoms. By prioritizing interventions targeting these core symptoms, related symptoms may lose their “anchor points”, thereby alleviating the overall symptom burden. Our study found that the core symptoms in breast cancer patients change as chemotherapy progresses. This means that the focus of symptom management should also shift according to the treatment stage.
Keywords: breast cancer, chemotherapy, symptom network, core symptoms