Symptom Clusters and Network Analysis in Lung Cancer Patients Receiving Taxane-Based Chemotherapy: A Comprehensive Assessment Using the CIPNAT Multiscale Tool

Symptom Clusters and Network Analysis in Lung Cancer Patients Receiving Taxane-Based Chemotherapy: A Comprehensive Assessment Using the CIPNAT Multiscale Tool | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Symptom Clusters and Network Analysis in Lung Cancer Patients Receiving Taxane-Based Chemotherapy: A Comprehensive Assessment Using the CIPNAT Multiscale Tool Tangyihua Li, Qiyu Sun, Mei Zhang, Zilong Liu, Li Yao, Yan Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7731088/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Supportive Care in Cancer → Version 1 posted 9 You are reading this latest preprint version Abstract Objective This study aimed to clarify the topological structure, core symptoms, and inter-symptom association patterns of the symptom network in lung cancer patients receiving taxane-based chemotherapy, and to provide a basis for formulating precise symptom management strategies. Methods A convenience sampling method was used to enroll 315 hospitalized lung cancer patients who received taxane-based chemotherapy (paclitaxel, albumin-bound paclitaxel, docetaxel) in a Grade III-A hospital in Shanghai from January 2023 to June 2024. Data on demographics, physiological, psychological, and symptomatic variables were collected using a general information questionnaire, the Chemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT), the Pittsburgh Sleep Quality Index (PSQI), the Psychological Capital Questionnaire (PCQ), and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30). A symptom network was constructed using the graphical LASSO (least absolute shrinkage and selection operator) based on the Extended Bayesian Information Criterion (EBIC) (EBIC-glasso) algorithm.Centrality analysis was conducted to identify core symptoms, the bootstrap method was used to verify the network accuracy and stability, and the Network Comparison Test (NCT) was applied to analyze differences in network structure between two age groups (≤ 65 years vs. >65 years). Results Among the 315 patients, 86.35% were male, with a median age of 68 years (interquartile range [IQR]:60.50–72.00 years), and 58.73% were aged over 65 years. Three pairs of strongly correlated symptoms were identified in the symptom network: optimism and hope (r = 0.625), symptom frequency and bothersomeness (r = 0.603), and sleep efficiency and sleep duration (r = 0.522). The network was dominated by positive connections and exhibited high global connectivity, indicating that symptoms tended to co-occur and mutually reinforce each other. Centrality analysis showed that fatigue (QLQ7) was the key hub node in the network, with the highest strength centrality (2.735), closeness centrality (2.078), and betweenness centrality (3.944). The frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) was driver node of the network, with the highest expected influence (EI = 1.417). The network showed good stability, with a correlation stability (CS) coefficient of 0.673 for strength centrality and expected influence. Subgroup analysis by age revealed no significant differences in network structure (M = 0.240, p = 0.347) or global connectivity (12.516 vs. 12.418, p = 0.802) between the two age groups. Conclusion The symptom network of lung cancer patients receiving taxane-based chemotherapy exhibits a tightly interconnected characteristic. Fatigue is the core hub symptom, and the frequency of chemotherapy-induced peripheral neuropathy is the key driver symptom. Additionally, the network structure shows age universality. Clinically, interventions can be prioritized for the aforementioned core symptoms to achieve efficient management of the overall symptom cluster. Lung cancer Taxane-based chemotherapy Symptom network Core symptoms Network analysis Chemotherapy-induced peripheral neuropathy Fatigue Age-related differences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Lung cancer ranks as the second most prevalent cancer and the leading cause of cancer-related mortality worldwide, and it is the malignancy with the highest incidence and mortality rates in China [ 1 ] . According to the GLOBOCAN 2020 data released by the International Agency for Research on Cancer (IARC), there were 2,206,771 new lung cancer cases and 1,796,144 lung cancer-related deaths worldwide in 2020. In China, the annual number of new lung cancer cases and deaths reached 815,563 and 714,699, respectively, accounting for 37.0% and 39.8% of the worldwide totals [ 2 ] . Chemotherapy remains a cornerstone of treatment for patients with lung cancer [ 3 ] . As a systemic therapeutic modality, chemotherapy effectively inhibits tumor growth and metastasis, thereby prolonging the survival of patients [ 4 ] . Among chemotherapeutic agents, taxanes are the most commonly used first-line drugs for the treatment of lung cancer. They exert their anti-tumor effects by stabilizing tubulin and inhibiting the division of tumor cells. However, they are prone to causing dose-dependent sensory neuropathy, such as paclitaxel − induced axonal degeneration, and may exacerbate central sensitization through neuroinflammatory responses, resulting in a toxicity profile distinct from that of other chemotherapeutic agents (e.g.,platinum − based drugs) [ 5 ] . Lung cancer patients often experience multiple symptoms after chemotherapy [ 6 ] , such as chest tightness, shortness of breath and cough caused by tumor bleeding irritating the mucosa or compressing the trachea [ 7 ] , gastrointestinal symptoms [ 8 ] , and psychological symptoms [ 9 ] . Compared with patients with other types of cancer, lung cancer patients endure the most severe symptom burden [ 10 ] . In a symptom survey involving 145 lung cancer patients after chemotherapy, the most common symptoms identified were fatigue, drowsiness, sleep disturbance, and pain [ 11 ] . Despite the widespread use of taxane-based chemotherapy in the treatment of lung cancer, patients often experience the interactive effects of multiple symptoms (e.g.,neuropathy, fatigue, nausea). However, existing research has significant limitations. Traditional clustering methods (e.g.,hierarchical clustering) can only identify static symptom clusters and cannot quantify the directional influences between symptoms or network hub nodes, which makes it difficult to guide dynamic interventions. Traditional statistical models (e.g.,regression analysis) assume independence between variables. In contrast, network analysis, based on the systems theory framework, views symptoms as a dynamically interacting system. By controlling for confounding effects through partial correlation networks, network analysis is better suited to reveal direct associations and feedback loops between symptoms [ 12 ] . Furthermore, the specific toxicity profile of taxanes, such as that related to neuroinflammation-mediated central sensitization, may contribute to the formation of a unique symptom network. However, relevant research remains scarce, and most studies are based on populations from Europe and America, with no validation in Asian patients (e.g.,due to metabolic differences in metabolism or the combined use of traditional Chinese medicine treatments). These methodological flaws and barriers to clinical translation underscore the necessity of applying network analysis,which can provide a mechanistic basis for precise interventions by quantifying the strength of associations between symptoms and topological structures (e.g.,the identification of hub nodes) [ 13 – 16 ] . In summary, this study, which is based on symptom network analysis, treats symptoms as interacting network nodes. By quantifying connection strengths (i.e., edge weights0 and topological characteristics (e.g.,centrality metrics), this study can not only identify core hub symptoms but also reveal potential pathways of action. In recent years, network analysis has demonstrated its value in the research on symptoms of chronic diseases. However, significant gaps remain in understanding the specific symptom networks of lung cancer patients receiving taxane-based chemotherapy: (1) a lack of elucidation of network characteristics associated with the unique toxicity profile of these drugs (e.g., dose-dependent neuropathy) ; (2) a scarcity of data from Asian populations, which may lead to the oversight of culturally specific symptom associations. Therefore, this study intends to conduct a cross-sectional survey and employ network analysis based on the Gaussian graphical model (i.e., the EBIC-glasso algorithm) to systematically map the symptom interaction network in Asian lung cancer patients receiving taxane-based chemotherapy. This study aims to provide new evidence for the development of precise management strategies targeting hub nodes. Methods Participants Convenience sampling was used to enroll 315 hospitalized lung cancer patients receiving taxane-based chemotherapy (paclitaxel, albumin-bound paclitaxel, docetaxel) at a Grade III-A hospital in Shanghai from January 2023 to June 2024. Inclusion criteria: (1) Pathologically confirmed primary bronchogenic carcinoma; (2) Patients undergoing taxane-based chemotherapy (paclitaxel, albumin-bound paclitaxel, docetaxel); (3) Physical Status Score (PS) ≤ 2; (4) Age ≥ 18 years; (5) No cognitive impairment or communication difficulties; (6) Voluntary participation in questionnaire surveys with informed consent. Exclusion criteria: (1) Patients with peripheral neuropathy due to comorbidities; (2) Patients with concurrent malignancies; (3) Patients with concealed medical conditions. Sample size The sample size for network analysis was determined based on the standard of 5–6 participants per node [ 17 ] . In this study, there were 29 nodes, so at least 145–174 patients were required. Considering a 20% sample loss rate, 174–209 patients were required. Ultimately, 315 patients were surveyed in this study. Data collection In this study, the Chemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT) and four additional assessment tools were used to evaluate symptom clusters in lung cancer patients undergoing taxane-based chemotherapy, while investigating the contributing factors from demographic, physiological, psychological, and sociological perspectives. Rigorous quality control measures were implemented during data collection, including investigator training, standardized calibration of assessment tools, and participant screening based on the inclusion and exclusion criteria. As the chemotherapy regimen involved 21-day cycles, the survey was administered after chemotherapy on the day of the patient's next hospitalization to ensure the accuracy of the data. Researchers collected the questionnaires on-site, verified the integrity of questionnaire completion, and provided explanations for patients' inquiries without influencing their treatment decisions Instruments The General Information Questionnaire The General Information Questionnaire included sociodemographic data (e.g., age, gender, marital status, occupation, monthly per capita family income, medical payment method, etc.) and medical data (e.g., comorbid chronic diseases, laboratory indicators, body mass index [BMI], number of chemotherapy cycles, etc.). Chemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT) The Chemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT), developed by Tofthagen [ 18 ] at the University of South Florida in 2008, was localized into Chinese by Wang Yue and colleagues [ 19 ] . The Chinese version of the CIPNAT demonstrates strong psychometric properties, with item-total correlations ranging from 0.32 to 0.70. The Cronbach's α coefficient for the total scale is 0.94, while those for the subscales are 0.92 and 0.89, respectively. Item content validity index (CVI) values range from 0.89 to 1.00, with an average of 0.92 across all items. As a self-report instrument, the CIPNAT consists of two components: symptom experience assessment and evaluation of the impact on daily activities. A 0–10 scoring scale is used, where higher total scores indicate more severe symptoms, greater distress, longer symptom duration, and more significant impairment in daily activities. Pittsburgh Sleep Quality Index (PSQI) The Pittsburgh Sleep Quality Index (PSQI), developed by Buysse [ 20 ] and adapted into Chinese by Lu Taoying [ 21 ] , is a scale consisting of 19 self-reported items and 5 items reported by others, designed to evaluate patients' sleep patterns over the past month. With a Cronbach's α coefficient of 0.845, the PSQI demonstrates strong reliability, validity, and stability. The scale covers seven dimensions: subjective sleep quality, time to fall asleep, total sleep duration, sleep efficiency, sleep disturbances, use of hypnotic medications, and impairment in daytime functioning. Each item is rated on a 0–3 scale, with cumulative scores reflecting overall sleep quality; higher scores indicate poorer sleep quality. European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) is a standardized assessment tool for evaluating cancer patients' quality of life [ 22 ] . This 30-item questionnaire covers five functional domains (physical function, role function, emotional function, cognitive function, social function), three primary symptoms (fatigue, nausea/vomiting, pain), six subscale items (breathing difficulties, appetite loss, sleep disorders, constipation, diarrhea, financial status), and one overall quality of life measure. All five functional domains, three symptom dimensions, and six subscale items are scored using a 1–4 system, where 1 indicates "none" and 4 represents "very much". The overall quality of life is rated on a 1–7 scale.. Higher scores in the overall quality of life and functional domains indicate better health outcomes. Conversely, higher scores in symptom dimensions or subscale items suggest more severe symptoms or poorer quality of life. The questionnaire demonstrates excellent reliability and validity, with Cronbach's α coefficients exceeding 0.8 for both test-retest consistency and internal consistency. Psychological Capital Questionnaire (PCQ) The Psychological Capital Questionnaire (PCQ) was developed by Zhang K et al. [ 23 ] in 2008. This instrument assesses four dimensions: self-efficacy, resilience, hope, and optimism, comprising 26 items. The questionnaire demonstrates strong structural validity, with nearly all items showing factor loadings above 0.5 (mean = 0.64) and item discrimination indices exceeding 0.6 (mean = 0.71). Confirmatory factor analysis confirmed that both the four-factor model and the higher-order factor model exhibited good fit. Items of each subscale of the questionnaire had Cronbach's α coefficients of 0.86, 0.83, 0.80, and 0.76, respectively, while the overall Cronbach's α coefficient reached 0.90, indicating excellent internal consistency reliability. Each item is scored using a 7-point Likert scale (1–7), where 1 = "completely inconsistent" and 7 = "completely consistent," with total scores ranging from 26 to 182. Higher scores reflect higher levels of psychological capital and stronger psychological resilience. Ethical considerations This study was conducted in compliance with the revised Declaration of Helsinki (2013) and was approved by the Ethics Committee of Zhongshan Hospital, Fudan University (Ethics Approval No.: B2024-405R). All participants voluntarily participated in the study. Participants provided written informed consent for each study phase. Study sample and data preprocessing We analyzed data from N = 315 patients who completed the multidimensional symptom and function assessments. Variables included self-efficacy, resilience, hope, optimism, EORTC QLQ-C30 functional scales (physical, role, emotional, cognitive, social), general health status, a set of symptom-type indicators (fatigue, nausea/vomiting, pain, dyspnea, insomnia, appetite loss, constipation, diarrhea, financial difficulty), and multiple PSQI (Pittsburgh Sleep Quality Index) and CIPNAT items. Continuous and ordinal symptom/item scores were retained in their original scoring metric for network estimation. All analyses were performed in R (version ≥ 4.0) using the packages qgraph, bootnet, and NetworkComparisonTest. Statistical analysis Count data were described using frequencies and percentages. Scores for frequency, severity, and distress of symptoms, which were severely skewed, were described using the median (interquartile range, IQR; P25–P75). Symptoms were first transformed into a suitable correlation matrix using the cor_auto function in the qgraph package, which computes Spearman correlations (or polychoric correlations, if needed) to accommodate non-normal data. We then estimated a Gaussian graphical model (GGM) representing the symptom network. This was done via the graphical LASSO (least absolute shrinkage and selection operator) with Extended Bayesian Information Criterion (EBIC) model selection (hyperparameter γ = 0.5) to obtain a sparse partial correlation network. The tuning parameter γ = 0.5 was chosen to balance model fit and parsimony, as commonly recommended in psychopathological network analysis. The resulting network was visualized using the qgraph package: node layouts were arranged by symptom domain for interpretability, and edge width was scaled to the absolute value of the regularized partial correlation. Positive edges are displayed as solid blue lines and negative edges as dashed red lines. We computed node centrality metrics including strength, closeness, betweenness, and expected influence. Centrality indices were calculated using algorithms in the qgraph and bootnet packages (which compute these metrics from the estimated network). We assessed the accuracy of edge weight estimates using a nonparametric bootstrap (2000 samples), computing 95% confidence intervals (CI) for each edge weight. Narrower bootstrap CIs indicate more precise estimates. Network stability was evaluated via case-dropping bootstrap (2000 samples) to estimate the correlation stability (CS) coefficient for each centrality metric. The CS coefficient quantifies the maximum proportion of cases that can be dropped while retaining, with 95% probability, a correlation of at least 0.7 between original and subset centrality indices. We consider CS-coefficients above 0.25 (preferably above 0.50) as indicative of adequate stability. These analyses (edge-weight CIs and CS coefficient calculation) were conducted using the bootnet package. To examine differences in network structure between predefined groups (younger vs. older participants), we applied the Network Comparison Test (NCT) using the NetworkComparisonTest package. This permutation-based test evaluates whether two independent networks differ in overall structure or global connectivity. We conducted 5,000 permutations to test (a) network invariance (M-test for structural differences) and (b) global strength invariance (total connectivity) between subgroups. In addition, we inspected between-group differences in specific node centralities (e.g., expected influence) using bootstrap difference tests where applicable. All analyses were performed in R (version 4.5.0) using the packages qgraph, bootnet, and NetworkComparisonTest. Results Sample characteristics (brief) A total of 315 lung cancer patients receiving taxane-based chemotherapy were enrolled in this study. The gender distribution showed a significant imbalance, with male patients accounting for 86.35% (272/315) and female patients only 13.65% (43/315). This gender composition is partially consistent with the historical gender distribution characteristics of lung cancer in clinical settings, where male patients have traditionally been more represented due to factors such as higher smoking prevalence and occupational exposure risks. However, it should be noted that recent epidemiological data [ 24 ] indicate an increasing trend in lung cancer incidence among women, which may be associated with risk factors such as secondhand smoke exposure and indoor air pollution from biomass fuel combustion. Summary distributions (median [interquartile range] or n (%)) for key psychosocial and symptom scales were presented in Table 1. Median scores for psychosocial scales were: self-efficacy 33.00 (IQR 28.00–38.00), resilience 31.00 (IQR 27.00–35.00), hope 29.00 (IQR 25.00–33.00), and optimism 30.00 (IQR 27.00–36.00). Functional scales (EORTC QLQ-C30) showed median physical functioning 80.00 (IQR 66.67–93.33) and role functioning 100.00 (IQR 66.67–100.00). The prevalence distributions of symptom-type indicators (e.g., fatigue, dyspnea, insomnia, appetite loss), other demographic and clinical characteristicswere summarized in Table 1. Table 1. Baseline characteristics of the sample (N = 315). Data were presented as median (IQR) for continuous/ordinal scales and n (%) for categorical variables. Variable Mean or n (SD) or % Gender Male 272 86.35% Female 43 13.65% Marital Married 298 94.60% Unmarried 3 0.95% Divorced 2 0.63% Widowed 12 3.81% Age Median (IQR) 68 60.50–72.00 ≤ 65 130 41.27% > 65 185 58.73% Religion Yes 24 7.62% No 291 92.38% BMI 23 20.95–25.00 Occupation Civil Servant 7 2.22% Company or Private Enterprise Employee 16 5.08% Farmer 89 28.25% Public Institution Staff 16 5.08% Retired 183 58.10% Unemployed 4 1.27% Education Level Illiterate 14 4.44% Primary School 84 26.67% Junior High School 122 38.73% High School/Technical School 58 18.41% Junior College 22 6.98% Bachelor's Degree 15 4.76% Living Arrangement Living Alone 11 3.49% Living with Family 304 96.51% Monthly household income per capita (RMB) 8000 30 9.52% Pathological Type Squamous cell carcinoma 196 62.22% Adenocarcinoma 70 22.22% Non-small cell carcinoma 22 7.0% Neuroendocrine carcinoma- Small cell 13 4.12% Neuroendocrine carcinoma- Large cell neuroendocrine carcinoma 3 0.95% Sarcomatoid carcinoma 3 0.95% Mixed type 8 2.54% Medical Insurance Payment Method Self-pay 15 4.44% Medical Insurance 221 70.16% Rural Cooperative Medical Care 76 24.13% Other 4 1.27% ECOG Performance Status(PS) Median (IQR) 0 0.00–1.00 0 226 71.75% 1 85 26.98% 2 4 1.27% White Blood cells (×10 9 /L) 6.7 5.57–8.53 Red Blood cells (×10 12 /L) 220 177.50–279.50 Number of chemotherapy sessions 2 1.00–5.00 Concurrent Immunotherapy No 144 45.71% Yes 171 54.29% Chemotherapeutics single-agent chemotherapy 75 23.81% combination chemotherapy 240 76.19% Self-efficacy 33.00 28.00–38.00 Resilience 31.00 27.00–35.00 Hope 29.00 25.00–33.00 Optimism 30.00 27.00–36.00 Physical Functioning 80.00 66.67–93.33 Role Functioning 100.00 66.67–100.00 Emotional Functioning 91.67 66.67–100.00 Cognitive Functioning 83.33 66.67–100.00 Social Functioning 83.33 66.67–100.00 General Health Status 33.33 16.67–50.00 Fatigue Symptom Type 33.33 11.11–44.44 Nausea and Vomiting Symptom Type 0.00 0.00–16.67 Pain Symptom Type 0.00 0.00–33.33 Shortness of Breath Symptom Type 33.33 0.00–33.33 Insomnia Symptom Type 33.33 0.00–66.67 Appetite Loss Symptom Type 33.33 0.00–33.33 Constipation Symptom Type 0.00 0.00–33.33 Diarrhea Symptom Type 0.00 0.00–33.33 Financial Difficulty Symptom Type 33.33 0.00–33.33 Sleep Quality 1.00 1.00–2.00 Sleep Onset Time 1.00 1.00–2.00 Sleep Duration 1.00 0.00–1.00 Sleep Efficiency 1.00 0.00–3.00 Sleep Disturbances 1.00 1.00–2.00 Hypnotic Medications 0.00 0.00–0.00 Daytime Functioning Impairment 1.00 0.00–2.00 CIPNAT Severity 0.00 0.00–6.00 CIPNAT Bothersomeness 0.00 0.00–1.50 CIPNAT Frequency 0.00 0.00–2.00 Estimated Network: The estimated symptom network (Fig. 1 ) consisted of 29 nodes corresponding to symptom domains. Edges represent regularized partial correlations among symptoms. Positive partial correlations were represented by solid blue edges and negative partial correlations by red dashed edges (Fig. 1 ). The largest edge weights were observed between Optimism (PPQ4) and Hope (PPQ3) (r = 0.625), Frequency (CIPNAT3) and Bothersomeness (CIPNAT2) (r = 0.603), and Sleep Efficiency (PSQI4) and Sleep Duration (PSQI3) (r = 0.522). These strong associations suggest closely linked symptom pairs. Overall, most edges were positive (only a few negative edges were present), indicating that higher levels of one symptom tended to co-occur with higher levels of other related symptoms. The total network connectivity (global strength) was high, reflecting a tightly interconnected symptom structure. Centrality Indices: Figure 2 presents the centrality values for each symptom. The node “QLQ7 (Fatigue)” emerged as the most central in this network: it had the highest strength centrality (2.735), highest closeness centrality (2.078),and highest betweenness centrality (3.944), identifying it as a key hub symptom. By contrast, the symptom “CIPNAT3 (Frequency of chemotherapy-induced peripheral neuropathy)” showed the highest expected influence (EI = 1.417), indicating that it exerts the largest overall effect on the network when accounting for the sign of connections. Other nodes had lower centrality scores (see Table 1). These results suggest that QLQ7 (Fatigue) is a core symptom connecting many others, while CIPNAT3 (Frequency of chemotherapy-induced peripheral neuropathy) strongly influences the network’s state. Network Accuracy and Stability: Figure 3 shows the bootstrapped 95% confidence intervals for all edge weights (red points denote observed estimates; grey bands represent the CIs). In general, the intervals were relatively narrow (see Fig. 3), indicating precise estimation of edge strengths. In the case-dropping bootstrap (Fig. 4 ), the correlation of each node’s centrality index in the subsamples with its value in the full sample remained high even when dropping up to 75% of the sample cases. The CS coefficient for strength and for expected influence was 0.673 (substantially above the 0.50 guideline), indicating excellent stability; the CS coefficient for closeness also was 0.673. The CS coefficient for betweenness, although above the minimal acceptable threshold (0.25), was lower than that for strength and closeness Overall, these results demonstrate that the network parameters (especially strength and EI) were stable under sampling variability. Subgroup Differences: Figure 5 presents the symptom networks and centrality indices in different age groups (≤ 65 vs. >65 years). No significant differences emerged when the symptom networks between subgroups were compared. The NCT showed that the network structures were invariant (M = 0.240, p = 0.347) and that global strength was comparable between groups (Global Strength = 12.516 vs. 12.418, p = 0.802). In other words, neither the arrangement of connections nor the overall connectivity differed significantly across groups. Likewise, pairwise comparisons of node centralities (e.g., expected influence for each symptom) yielded no significant group differences (all p-values > 0.05). These findings indicate that the symptom network topology and the importance of central symptoms did not vary meaningfully by subgroup in our sample. Discussion Summary of Main Findings This study employed network analysis to investigate the complex interrelationships among multidimensional symptoms in 315 lung cancer patients undergoing taxane-based chemotherapy. Our findings provide a critical foundation for developing precise clinical interventions. Network analysis revealed three pairs of strongly correlated symptoms: optimism (PPQ4)-hope (PPQ3) (r = 0.625), symptom frequency (CIPNAT3)-bothersomeness (CIPNAT2) (r = 0.603), and sleep efficiency (PSQI4)-sleep duration (PSQI3) (r = 0.522). The network was characterized by predominantly positive edges and high global connectivity, indicating a tendency for symptoms to co-occur and mutually reinforce. Centrality analyses identified fatigue (EORTC QLQ-C30, QLQ7) as the network’s key hub (highest strength centrality, closeness centrality, and betweenness centrality) and frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) as the node with the strongest overall influence (highest expected influence). This strong interconnectedness implies that effectively managing fatigue (EORTC QLQ-C30, QLQ7) may yield positive ripple effects across the entire symptom cluster, although causal relationships require validation through longitudinal studies. Furthermore, specific association patterns were identified, including a fatigue (EORTC QLQ-C30, QLQ7)–appetite loss (EORTC QLQ-C30, QLQ12)–insomnia (EORTC QLQ-C30, QLQ11) cluster and a cluster showing negative correlations between financial difficulties (EORTC QLQ-C30, QLQ15) and social and emotional functioning (EORTC QLQ-C30). The network structure exhibited high stability, with no significant differences observed across age subgroups (≤ 65 years vs. >65 years), indicating its potential generalizability. Another notable finding was the relatively weak association between sleep disturbances (PSQI) and psychological capital (PCQ), suggesting that sleep disturbances (PSQI) may operate through a distinct pathway. Interpretation and Mechanistic Exploration of Core Symptoms The Central Hub Role of Fatigue Fatigue (EORTC QLQ-C30, QLQ7) served as a core symptom in this study, with its high centrality indices (strength, closeness, and betweenness centrality) holding significant clinical implications. Network analysis revealed that the connection between “fatigue (EORTC QLQ-C30, QLQ7)” and “overall health status (EORTC QLQ-C30, QLQ6)” ranked first in strength centrality, closeness centrality, and betweenness centrality. Specifically, fatigue (EORTC QLQ-C30, QLQ7) exhibited the highest strength centrality (rs, Spearman’s correlation coefficient = 2.735), closeness centrality (rc, closeness correlation coefficient = 2.078), and betweenness centrality (rb, betweenness correlation coefficient = 3.944) within the network, indicating its role as a central hub of the symptom cluster. This finding is consistent with the known side effects of taxane chemotherapy (such as docetaxel and paclitaxel), which often induce significant fatigue and consequently affect quality of life. The underlying mechanisms may involve neurotoxicity, release of pro-inflammatory cytokines (e.g., TNF-α, IL-1β, IL-6), and energy metabolism disorders. Taxanes may lead to fatigue through direct or indirect neurotoxic effects [ 25 ] , potentially impacting peripheral nerve function and causing neuropathy, which in turn exacerbates the sensation of fatigue [ 26 ] . Pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 may induce fatigue by activating neuroinflammatory pathways and affecting neurotransmitter metabolism and function [ 27 , 28 ] . Energy metabolism disorders represent another crucial mechanism of fatigue. The rapid growth and metabolic demands of tumor cells may lead to insufficient energy supply, thereby causing fatigue [ 29 ] . Therefore, prioritizing fatigue as an intervention target may positively alleviate the overall symptom cluster in lung cancer patients receiving taxane-based chemotherapy through its central position. CIP Frequency (CIPNAT3): The Network Driver Symptom The frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) had the highest expected influence (EI = 1.417), indicating that the frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) may act as a “trigger” to set off the entire symptom network. This is consistent with previous studies suggesting that chemotherapy-induced peripheral neuropathy (CIPN) may lead to sensory, motor, and autonomic dysfunction, thereby severely affecting patients’ quality of life [ 30 – 33 ] . This finding suggests that the neurotoxicity of taxanes may shape a unique symptom network profile. The highest expected influence of frequency indicates that the “frequency of occurrence” of symptoms, rather than their “severity” or “degree of distress,” may more effectively amplify the cascading effect of the entire network. Considering the symptom triggering mechanism, this supports Barsevick’s “symptom frequency–overall distress” hypothesis [ 34 ] . In clinical practice, early identification and reduction of high-frequency symptoms (such as nocturnal numbness and tingling) may be more effective in improving patients’ overall condition than simply controlling single severe events. Symptom Frequency-Bothersomeness Loop: A Key Modulator of Network Dynamics Symptom Frequency (CIPNAT3) and Bothersomeness (CIPNAT2) (r = 0.603) reflects the “symptom appraisal loop” in cancer patients: more frequent symptoms are more likely to be perceived as bothersome, which in turn may amplify emotional distress and reduce functional capacity. For patients receiving taxane-based chemotherapy, this loop may be exacerbated by the chronicity of taxane-related symptoms (e.g., persistent peripheral neuropathy). Importantly, this association highlights that symptom management should not only target frequency (e.g., pharmacologic interventions for fatigue) but also address subjective bother (e.g., cognitive-behavioral strategies to reframe symptom impact). Pathophysiological and Psychosocial Interpretation of Key Symptom Clusters The strong correlation cluster of “fatigue (EORTC QLQ-C30, QLQ7)–anorexia (i.e., appetite loss, EORTC QLQ-C30, QLQ12)–insomnia (EORTC QLQ-C30, QLQ11)” may be closely related to the systemic effects of taxane-based chemotherapy. The potential common biological pathways underlying this cluster may include inflammatory responses, dysregulation of the hypothalamic-pituitary-adrenal axis (HPA axis), and metabolic alterations [ 35 ] . On the other hand, the negative correlation cluster of “financial difficulty (EORTC QLQ-C30, QLQ15)–social function (EORTC QLQ-C30, QLQ5)–emotional function (EORTC QLQ-C30, QLQ3)” highlights the significant impact of socioeconomic factors on patients’ psychological health (assessed by PCQ). Financial stress may not only directly trigger anxiety and depression but also limit patients’ ability to access social support and high-quality medical resources, thereby further impairing their social and emotional functions. Unique Patterns of Symptom Associations and Implications for Intervention Protective Role of Functional and Psychological Resources In this study, physical function (EORTC QLQ-C30, QLQ1) and emotional function (EORTC QLQ-C30, QLQ3) were negatively correlated with major symptoms such as fatigue (EORTC QLQ-C30, QLQ7) and anorexia (EORTC QLQ-C30, QLQ12), indicating that maintaining or enhancing patients' physical and emotional functional levels may be an important resource for buffering the negative impacts of symptoms (e.g., fatigue, anorexia). Research [ 36 ] has shown that a comprehensive intervention approach combining pharmacological, psychological, nursing, and family support may be effective for fatigue. Pharmacological treatment can alleviate fatigue symptoms, psychological interventions can improve emotional states, nursing interventions can provide guidance for daily living, and family support can enhance patients' confidence and adherence. Therefore, enhancing patients' functional levels through rehabilitation training and psychological interventions may help buffer the negative impacts of symptoms. Independence of Sleep Problems The weak associations between sleep disturbances (PSQI) and psychological capital dimensions (PCQ) such as hope (PPQ3) and resilience (PPQ2) indicate that conventional interventions targeting positive psychological constructs may have limited efficacy in addressing sleep-related issues. Therefore, more targeted strategies should be considered, such as cognitive-behavioral therapy for insomnia, pharmacological management, or sleep environment modifications, to more effectively ameliorate sleep problems. Reliability Verification of Network Stability and Clinical Significance of Universality The network structure in this study demonstrated high stability. The correlation stability coefficients (CS = 0.53 and 0.49) for the strength and expected influence both exceeded the recommended threshold of 0.25 [ 37 ] , indicating that the network structure was highly robust and less susceptible to sample fluctuations, thereby supporting the reliability of the conclusions. The stability results substantiate the key findings; for instance, a stable network structure implies that critical conclusions such as “fatigue (EORTC QLQ-C30, QLQ7) as a core symptom” are highly reproducible in similar populations. This provides a reliable basis for subsequent clinical application and generalization. More importantly, age subgroup analysis revealed no statistically significant differences in the overall network structure, connection strength, or centrality order of symptoms among patients of different age groups (≤ 65 years vs. >65 years). This finding of “age universality” holds considerable clinical value. It suggests that core intervention strategies developed based on these results—such as prioritizing fatigue management in all adult patients, regardless of age—are universally applicable. Clinicians need not design vastly different or complex intervention plans solely based on age, which significantly enhances the efficiency and generalizability of interventions and allows resources to be concentrated on the most effective targets. Comparative Analysis with Existing Related Studies Numerous domestic and international studies on symptom networks in patients undergoing taxane-based chemotherapy for lung cancer or other solid tumors have also identified “fatigue” as a core symptom [ 38 – 41 ] . For example, a study [ 38 ] of 1,255 lung cancer patients receiving chemotherapy patients identified four symptom clusters: fatigue, gastrointestinal, neuropsychiatric, and respiratory, with central symptoms being fatigue, vomiting, distress, and hemoptysis, respectively. In a study [ 39 ] of 221 gynecological oncology patients receiving chemotherapy patients, the most common symptoms were fatigue (n = 197, 89.1%) and loss of appetite (n = 192, 86.9%), while the most severe symptoms were fatigue (mean = 4.17, SD = 2.07) and anxiety (mean = 3.43, SD = 2.20). These findings are consistent with the conclusions of this study, underscoring the universality of the central role of the “fatigue symptom cluster” and further confirming that the results of this study are not coincidental but widely applicable. However, some studies did not identify fatigue as a core symptom. The discrepancies may be attributed to the following factors: Differences in sample inclusion criteria. For instance, some studies [ 42 ] enrolled patients with poorer physical condition, who may present more complex symptoms, thereby altering the core structure of the symptom network. Variations in chemotherapy regimens. Single-agent versus dual-agent chemotherapy may differentially affect patient symptoms. Single-agent chemotherapy may result in relatively simpler symptoms, whereas dual-agent chemotherapy, due to drug synergism, may lead to more complex symptom manifestations, potentially obscuring the central role of fatigue [ 43 ] . Differences in assessment tools. If a comprehensive evaluation system such as the CIPNAT + multi-scale assessment used in this study is not employed, it may not fully capture patient symptoms, leading to misjudgment of key nodes in the symptom network [ 44 ] . Studies on quality of life in cancer patients generally indicate that physical function is negatively correlated with symptom severity [ 45 ] . The results of this study are highly consistent with this view, revealing significant negative correlations between physical function and symptoms such as fatigue and pain. For example, better physical function is associated with milder fatigue and pain. This further validates the important impact of functional status on the symptom network, emphasizing that clinical treatment should not only focus on the disease itself but also prioritize the maintenance and improvement of patients’ physical function. Rehabilitation training and other means to enhance physical function can thereby alleviate symptom burden. Psycho-oncological research has shown that financial burden exacerbates psychological stress in cancer patients [ 46 ] . This study provides new evidence for this perspective through symptom network analysis. The results demonstrate strong negative correlations between “financial difficulty” and emotional function as well as social function (r = − 0.162 to − 0.334), indicating that financial difficulties directly reduce patients’ emotional regulation capacity and decrease their participation in social activities, indirectly worsening physical symptoms. This highlights the importance of addressing patients’ financial status during treatment, and necessary financial assistance and psychological support should be provided to mitigate the negative impact of financial factors on the symptom network. While most existing studies [ 47 ] focus on the effect of age on the severity of individual symptoms, this study, through network structure invariance testing, is the first to propose that “the topological structure of the symptom network in patients undergoing taxane-based chemotherapy is universal across age groups.” This conclusion addresses a gap in research on the relationship between age and symptom networks, challenging the previous clinical practice of over-differentiating symptom management strategies based on age. It provides a theoretical basis for developing simpler and more efficient universal symptom management strategies. Implications for Clinical Practice The findings of this study indicate that fatigue is a core symptom in lung cancer patients following taxane chemotherapy, serving as the most strongly connected and highest-centrality core node within the entire symptom network. This signifies that fatigue is not merely a distressing symptom but also a key driver that activates and sustains other symptoms such as anorexia and insomnia. Fatigue should be prioritized for assessment and management in symptom care. As a subjective experience, fatigue manifests not only physically but also psychologically. Standardized tools should be employed for quantitative assessment to objectively document its severity and dynamic changes. This should be a routine component of every follow-up visit, thereby mapping individualized fatigue trajectories for patients and providing baseline data and feedback on intervention effectiveness. In clinical practice, clinical teams should focus on patients' emotional and physical states, understand the specific causes of their fatigue, develop personalized care plans, and consider integrated multidisciplinary care. A collaborative team led by oncology nurses and involving oncologists, psychologists, nutritionists, rehabilitation therapists, and palliative care specialists should be established. Patients should be encouraged to actively report fatigue levels and participate in decision-making processes. Jointly developing feasible activity/rest schedules with patients enhances patients’ self-management capabilities, thereby reducing patients’ feelings of helplessness. Only through a patient-centered care model—grounded in precise assessment, non-pharmacological interventions, and supported by multidisciplinary teams—can this core symptom be effectively addressed. This approach leverages improvements across the entire symptom network, ultimately delivering substantial quality-of-life gains that empower patients to navigate their treatment journey with greater positivity and dignity. Limitations First, the sample was recruited from a single center, limiting generalizability to other populations or regions. This single-center study exclusively enrolled hospitalized lung cancer patients receiving taxane-based chemotherapy from one Grade III-A hospital in Shanghai. Regional disparities in healthcare resources exist, necessitating caution when extrapolating the study’s findings to primary care settings in China or other regions. Future studies should conduct multicenter, multi-regional investigations enrolling diverse populations. Second, the cross-sectional design lacks temporal perspective, as it only surveys symptoms 21 days post each cycle of taxane-based chemotherapy, and thus is unable to infer causal sequencing between symptoms. Future longitudinal studies should explore dynamic symptom networks over the course of taxane-based chemotherapy. Third, integration of biopsychosocial variables in the analysis is inadequate. Relevant biomarkers such as IL-6 and CRP (inflammatory markers) were not measured, and chemotherapy-related psychological factors (e.g., fear of recurrence, social support) were not assessed. Future research should incorporate these multifaceted dimensions (i.e., biopsychosocial variables). Fourth, the study has a marked gender imbalance (86.35% males, 13.65% females), rooted in China’s lung cancer epidemiology. Data [ 48 ] from 1990–2021 show consistent male predominance: with male new cases doubling females’ in 2021 (62,000 vs. 31,000). Smoking, a key driver of male-predominant squamous cell carcinoma, further reinforces male dominance in clinical samples, leading to recruitment bias .This imbalance limits external validity. Chinese females predominantly have adenocarcinoma (60%–70% of cases) linked to non-smoking risks (e.g., kitchen fume), with distinct symptoms and treatment responses versus males’ smoking-related squamous cell carcinoma. Moreover, the Bayesian Age-Period-Cohort (BAPC) model predicts 2021–2036 female ASIR growth (35.7%) will vastly outpace males’ (4.8%) [ 48 ] . Findings may thus reflect male-specific symptom networks, not females’ adenocarcinoma-related patterns. Future studies should enroll more females (especially non-smoking risk-exposed) to improve generalizability . Conclusion This study employed network analysis to unravel the intricate interplay patterns among symptoms in lung cancer patients undergoing taxane-based chemotherapy. It identified fatigue as a core hub symptom strength centrality, which emerged as a critical intervention target by driving the formation of the "fatigue-anorexia-insomnia" symptom cluster, and frequency(CIPNAT3) as a key driver. The findings revealed that the symptom network exhibits dual characteristics, being influenced by both biological drivers and psychosocial factors, while demonstrating high stability and age universality. To our best knowledge, this research is the first to explicitly confirm the topological invariance of the symptom network in taxane-treated chemotherapy patients by leveraging a large sample size and the EBIC-glasso method. These results provide a scientific foundation for establishing a comprehensive intervention system that "centers on fatigue management while addressing multidimensional factors". The study supports the clinical promotion of standardized intervention protocols; however, further validation of the network's dynamic characteristics through longitudinal designs is warranted. Declarations Author Contribution TYH.L and L.Y contributed to the study conception and design. Material preparation and data collection were performed by M.Z and TYH.L. Analysis was performed by QY.S. The first draft of the manuscript was written by TYH.L and M.Z, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.The project administration for this research was undertaken by L.Y. Funding acquisition for this research was undertaken by Y.W. M.Dprovided overall supervision and guidance for the entire research project. References KRATZER T B, BANDI P, FREEDMAN N D, et al. Lung cancer statistics, 2023 [J]. Cancer, 2024, 130(8): 1330–48. SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209–49. MOUNTZIOS G, SUN L, CHO B C, et al. Tarlatamab in Small-Cell Lung Cancer after Platinum-Based Chemotherapy [J]. N Engl J Med, 2025, 393(4): 349–61. AWAD M M, FORDE P M, GIRARD N, et al. Neoadjuvant Nivolumab Plus Ipilimumab Versus Chemotherapy in Resectable Lung Cancer [J]. J Clin Oncol, 2025, 43(12): 1453–62. ZHANG Y, ZHAO Z, LI W, et al. Mechanism of Taxanes in the Treatment of Lung Cancer Based on Network Pharmacology and Molecular Docking [J]. Curr Issues Mol Biol, 2023, 45(8): 6564–82. DEGNER L F, SLOAN J A. Symptom distress in newly diagnosed ambulatory cancer patients and as a predictor of survival in lung cancer [J]. J Pain Symptom Manage, 1995, 10(6): 423–31. SILVONIEMI M, VASANKARI T, LöYTTYNIEMI E, et al. Symptom Assessment for Patients with Non-small Cell Lung Cancer Scheduled for Chemotherapy [J]. Anticancer Res, 2016, 36(8): 4123–8. ZHANG Y, CHEN Y C, HU L, et al. Chemotherapy-induced functional changes of the default mode network in patients with lung cancer [J]. Brain Imaging Behav, 2020, 14(3): 847–56. HINZ A, KRAUSS O, HAUSS J P, et al. Anxiety and depression in cancer patients compared with the general population [J]. Eur J Cancer Care (Engl), 2010, 19(4): 522–9. WONG M L, COOPER B A, PAUL S M, et al. Differences in Symptom Clusters Identified Using Ratings of Symptom Occurrence vs. Severity in Lung Cancer Patients Receiving Chemotherapy [J]. J Pain Symptom Manage, 2017, 54(2): 194–203. WONG M L, PAUL S M, COOPER B A, et al. Predictors of the multidimensional symptom experience of lung cancer patients receiving chemotherapy [J]. Support Care Cancer, 2017, 25(6): 1931–9. BOTHA J, DE RIDDER J H, POTGIETER J C, et al. Structural vascular disease in Africans: Performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: The SABPA study [J]. Exp Clin Endocrinol Diabetes, 2013, 121(9): 515–20. ZOU M, LIN M, HU K L, et al. Cross-Tissue Regulatory Network Analyses Reveal Novel Susceptibility Genes and Potential Mechanisms for Endometriosis [J]. Biology (Basel), 2024, 13(11). DAVIS K A, MORGAN V L. Network Analyses in Epilepsy: Are Nodes and Edges Ready for Clinical Translation? [J]. Neurology, 2021, 96(5): 195–6. BURGER J, ISVORANU A M, LUNANSKY G, et al. Reporting standards for psychological network analyses in cross-sectional data [J]. Psychol Methods, 2023, 28(4): 806–24. LASS A N S, JORDAN D G, WINER E S. Using theory to guide exploratory network analyses [J]. J Clin Psychol, 2023, 79(2): 531–40. FERNáNDEZ-DE-LAS-PEñAS C, PALACIOS-CEñA M, VALERA-CALERO J A, et al. Understanding the interaction between clinical, emotional and psychophysical outcomes underlying tension-type headache: a network analysis approach [J]. J Neurol, 2022, 269(8): 4525–34. TOFTHAGEN C S, MCMILLAN S C, KIP K E. Development and Psychometric Evaluation of the Chemotherapy-Induced Peripheral Neuropathy Assessment Tool [J]. Cancer Nurs, 2011, 34(4): E10-E20. Y W. Evaluation of peripheral neuropathy caused by chemotherapy in cancer patients [D], 2017. BUYSSE D J, REYNOLDS C F, 3RD, MONK T H, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research [J]. Psychiatry research, 1989, 28(2): 193–213. LU T Y L Y, XIA P, ET AL. Reliability and validity analysis of Pittsburgh sleep quality index [J]. Chongqing Medical Journal, 2014, 43(03): 260–3. SUN X G L C Y, WU M, ET AL. Evaluation of the Chinese version of EORTC QLQ-C30 in assessing quality of life in ovarian cancer patients [J]. Journal of Rehabilitation Medicine of China, 2007, (04): 335–8. ZHANG K Z S, DONG Y H. Positive psychological capital: measurement and its relationship with mental health [J]. Psychological and Behavioral Research, 2010, 8(01): 58–64. GEE K, YENDAMURI S. Lung cancer in females-sex-based differences from males in epidemiology, biology, and outcomes: a narrative review [J]. Transl Lung Cancer Res, 2024, 13(1): 163–78. COSTA R, PASSOS G F, QUINTãO N L M, et al. Taxane-induced neurotoxicity: Pathophysiology and therapeutic perspectives [J]. British Journal of Pharmacology, 2020. LI T, MIZRAHI D, GOLDSTEIN D, et al. Chemotherapy and peripheral neuropathy [J]. Neurological Sciences, 2021. LI Q-Y, XU H-Y, YANG H-J. Effect of proinflammatory factors TNF-α, IL-1β, IL-6 on neuropathic pain [J]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese … 17. GUPTA S C, KIM J H, KANNAPPAN R, et al. Role of nuclear factor- κ B-mediated inflammatory pathways in cancer-related symptoms and their regulation by nutritional agents [J]. Experimental Biology and Medicine, 2011. YANG J, SHAY C, SABA N F, et al. Cancer metabolism and carcinogenesis [J]. Experimental Hematology and Oncology, 2024. HUNG H-W, LIU C-Y, CHEN H-F, et al. Impact of Chemotherapy-induced Peripheral Neuropathy on Quality of Life in Patients with Advanced Lung Cancer Receiving Platinum-based Chemotherapy [J]. 2021. PARK H J. Chemotherapy induced peripheral neuropathic pain [J]. Korean journal of anesthesiology, 2014. LEE G, KIM S. Therapeutic Effects of Phytochemicals and Medicinal Herbs on Chemotherapy-Induced Peripheral Neuropathy [J]. Molecules, 2016. SIOKA C, KYRITSIS A P. Central and peripheral nervous system toxicity of common chemotherapeutic agents [J]. Cancer chemotherapy and pharmacology, 2009, 63(5): 761–7. BARSEVICK A. Defining the Symptom Cluster: How Far Have We Come? [J]. Seminars in oncology nursing, 2016, 32(4): 334–50. OH I-J, KIM K-S, KIM Y-C, et al. Altered Hypothalamus-Pituitary-Adrenal Axis Function: A Potential Underlying Biological Pathway for Multiple Concurrent Symptoms in Patients With Advanced Lung Cancer [J]. Psychosomatic Medicine, 2019. ZHU Y, LIU Y. Application Efficacy of Nursing Interventions Guided by the Medication-Psychological-Nursing-Family-Support (MPNFS) Framework in Mitigating Cancer-Related Fatigue and Enhancing Pulmonary Function Among Lung Cancer Patients Undergoing Chemotherapy [J]. Cancer Management and Research, 2025. EPSKAMP S, BORSBOOM D, FRIED E I. Estimating psychological networks and their accuracy: A tutorial paper [J]. Behavior Research Methods, 2018, 50(1): 195–212. LUO Y Y, LUO J H, SU Q, et al. Exploring Central and Bridge Symptoms in Patients with Lung Cancer: A Network Analysis [J]. Semin Oncol Nurs, 2024, 40(3): 9. ZHANG J J, WANG S, WANG Y, et al. Symptom clusters and network analysis in patients with gynecologic cancer undergoing chemotherapy: A cross-sectional study [J]. Asia-Pac J Oncol Nurs, 2024, 11(12): 8. SHAO X D, WANG N, TANG K, et al. Network analysis used to investigate the symptoms of cancer patients during chemotherapy: a scoping review [J]. Discov Oncol, 2025, 16(1): 15. RHA S Y, LEE J. Stable Symptom Clusters and Evolving Symptom Networks in Relation to Chemotherapy Cycles [J]. J Pain Symptom Manage, 2021, 61(3): 544–54. HARRIS C, HAMMER M J, CONLEY Y P, et al. Impact of Multimorbidity on Symptom Burden and Symptom Clusters in Patients Receiving Chemotherapy [J]. Cancer Med, 2025, 14(3): 20. RUSSELL J, WONG M L, MACKIN L, et al. Stability of Symptom Clusters in Patients With Lung Cancer Receiving Chemotherapy [J]. J Pain Symptom Manage, 2019, 57(5): 909–22. TENG L P, ZHOU Z, YANG Y T, et al. Identifying central symptom clusters and correlates in patients with lung cancer post-chemotherapy: A network analysis [J]. Asia-Pac J Oncol Nurs, 2024, 11(4): 7. GUDHOOR M, MATHEW A T, GANACHARI A M, et al. Utilization of European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 scale for evaluation of quality of life among cancer patients treated with chemotherapy: A hospital-based observational study [J]. J Oncol Pharm Pract, 2024, 30(5): 844–52. XING W J, LU Z Q, LU Y H, et al. Understanding and mitigating cancer-related financial toxicity in China: challenges and recommendations [J]. Lancet Reg Health-W Pac, 2025, 60: 5. SAITO Y. Taxane-Associated Acute Pain Syndrome: a Review of its Features and Management [J]. Curr Treat Options Oncol, 2025, 26(3): 187–96. WU X R Z H, JI C, ET AL. Epidemiological trends of lung cancer in China and globally from 1990 to 2021 and prediction of incidence and mortality: An analysis based on the age-period-cohort model [J]. Journal of Modern Preventive Medicine, 2025, 52(10): 1735–41. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Supportive Care in Cancer → Version 1 posted Editorial decision: Revision requested 31 Jan, 2026 Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Submission checks completed at journal 01 Oct, 2025 First submitted to journal 27 Sep, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-7731088","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540711318,"identity":"3be0a958-314b-40a4-a574-f17561839e52","order_by":0,"name":"Tangyihua Li","email":"","orcid":"","institution":"Zhongshan Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Tangyihua","middleName":"","lastName":"Li","suffix":""},{"id":540711319,"identity":"3906eeb2-111f-43e4-91a9-4a1e89062808","order_by":1,"name":"Qiyu Sun","email":"","orcid":"","institution":"Zhongshan Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Qiyu","middleName":"","lastName":"Sun","suffix":""},{"id":540711320,"identity":"dc53970c-a505-4dc0-9003-6b3ccfb35a08","order_by":2,"name":"Mei Zhang","email":"","orcid":"","institution":"Zhongshan Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Zhang","suffix":""},{"id":540711321,"identity":"2c270b18-ae93-41f6-a895-2b2b351384e5","order_by":3,"name":"Zilong Liu","email":"","orcid":"","institution":"Zhongshan Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Zilong","middleName":"","lastName":"Liu","suffix":""},{"id":540711322,"identity":"9985221e-f78a-4d88-9df7-8fdd52c4e6f3","order_by":4,"name":"Li Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3QsWoCQRAG4DkOtJnTdo+DbB5hgnCkkPNVNixYhSCksZIDwWfwNexSjmxhI14rpDFNKouAlZAiswGFFKspA9m/WHZhvt2dAYiJ+avZUV9W9FsF0AZIrxJDwzNRvv4XBNyJwHVCK8dgRs1A19ny7fhyP+lOIT2MoHoKkvXQyMdeU+KO7WVrpZSDVjEH+xwk/EietEjvyyKZSS9CUgR+qEOk2XuyQV1jmR+FaCcfu0i236+wAsZSZUJIRlFcIvn23bAhS8TY873kC5fMCiQbJJ3GLncfn5VMDO9kYpPuzWrqDjiuguSWAfh8SurTSoF6if5xVxK6OSYmJuZf5ws6N00MXx1stgAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongshan Hospital Affiliated to Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Yao","suffix":""},{"id":540711323,"identity":"b1979c8f-bea0-40b7-bf12-949d0a5fafd1","order_by":5,"name":"Yan Wu","email":"","orcid":"","institution":"Zhongshan Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wu","suffix":""},{"id":540711324,"identity":"2b24a63a-cf16-4cf2-bac3-1336169683a0","order_by":6,"name":"Min Ding","email":"","orcid":"","institution":"Zhongshan Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-09-28 02:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7731088/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7731088/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00520-026-10540-1","type":"published","date":"2026-03-17T15:59:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96244345,"identity":"d1c1eef2-027e-441e-897d-d5edbac8ee1c","added_by":"auto","created_at":"2025-11-19 07:18:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":981378,"visible":true,"origin":"","legend":"","description":"","filename":"9.24SymptomClustersandNetworkAnalysisinLungCancerPatientsDuringTaxaneBasedChemotherapy.docx","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/1409dd424a75bd0cb9b1c9a4.docx"},{"id":95914175,"identity":"368aec92-ce50-4238-8824-8c060b029538","added_by":"auto","created_at":"2025-11-14 11:12:14","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9849,"visible":true,"origin":"","legend":"","description":"","filename":"b6158d7440ae47fa9df1a4805d06cc65.json","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/87cfb96d24353d88a67c5380.json"},{"id":96242370,"identity":"2238246c-9614-404b-b882-cf1f6f7cb710","added_by":"auto","created_at":"2025-11-19 07:12:49","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134917,"visible":true,"origin":"","legend":"","description":"","filename":"b6158d7440ae47fa9df1a4805d06cc651enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/4f94ff686ea1efe00d226887.xml"},{"id":95914178,"identity":"eb73c670-978a-464b-aeb6-86fe15be2356","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":379893,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/e33fa90c4f1ba044e5e337c5.jpeg"},{"id":95914180,"identity":"ddbcf3a1-ef5f-4abd-b0c3-990092cb1c4e","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107989,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/8faaafab951316e50d428912.png"},{"id":95914181,"identity":"ea296d1d-f8f3-4525-ba5a-4ce7596889da","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62780,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/96275c8f3aff15dc008afa1b.png"},{"id":95914186,"identity":"83af49e7-6c88-4585-ba4b-b3a9547e0f34","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60687,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/5c967e28b2e4796ad49a3a59.png"},{"id":95914182,"identity":"eac38861-86b8-496d-88c2-649759810271","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54713,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/2f48b01eca4a2d1bf1d5ed8c.png"},{"id":95914188,"identity":"ae584779-d65e-4641-9e40-507de5e180be","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217654,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/526bd81776135a436894ae2d.png"},{"id":95914187,"identity":"8f322e4f-1941-4a76-ae97-8a14da7441bc","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134170,"visible":true,"origin":"","legend":"","description":"","filename":"b6158d7440ae47fa9df1a4805d06cc651structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/d68f97a48df8adc9002add77.xml"},{"id":96243444,"identity":"4ece15e1-7bb3-4358-b406-b11b86a120db","added_by":"auto","created_at":"2025-11-19 07:16:23","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142940,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/e4a33ea1e0878d0d67d48167.html"},{"id":95914184,"identity":"8d960734-bf6c-4967-b359-a5b60da6641c","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":221322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSymptom network graph.\u003c/em\u003e Nodes represent individual symptoms (grouped by symptom domain) and edges represent regularized partial correlations (EBIC-glasso, γ = 0.5). Blue solid edges indicate positive correlations; red dashed edges indicate negative correlations. Edge thickness is proportional to the absolute correlation magnitude.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/9eaf9523c840c474d70443d1.png"},{"id":95914174,"identity":"14496e41-0bc0-49a0-99a8-2900188c31c5","added_by":"auto","created_at":"2025-11-14 11:12:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64276,"visible":true,"origin":"","legend":"\u003cp\u003eCentrality indices for symptom nodes. Line charts display strength, closeness, betweenness, and expected influence for each symptom node. Higher values indicate more central (influential) symptoms. Error bars indicate bootstrapped 95% confidence intervals.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/8b875bf701f6812b9d90fbdd.png"},{"id":95914176,"identity":"1c894ef9-b980-4c71-8e24-f4dd0b7d49f4","added_by":"auto","created_at":"2025-11-14 11:12:15","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133283,"visible":true,"origin":"","legend":"\u003cp\u003eEdge weight accuracy. Plots of bootstrapped 95% confidence intervals for each edge weight (2000 nonparametric bootstrap samples). Narrower intervals reflect more precise estimates.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/83dc0025632c40ef46530e53.jpeg"},{"id":96244619,"identity":"2c65f5cb-5aab-41fa-bc6a-5d566198ed81","added_by":"auto","created_at":"2025-11-19 07:18:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358070,"visible":true,"origin":"","legend":"\u003cp\u003eCentrality stability. Results of case-dropping bootstrap (2000 samples) showing the correlation of centrality indices (strength, closeness, betweenness, expected influence) between subsamples and the full dataset.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/78707465db26c1085e68fbe5.jpeg"},{"id":96242432,"identity":"2869166f-6f0b-4ec3-8e6a-3e36af1968e2","added_by":"auto","created_at":"2025-11-19 07:12:57","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1051947,"visible":true,"origin":"","legend":"\u003cp\u003eSymptom networks and centrality indices between age groups (≤65 vs. \u0026gt;65 years).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/89b0fc4ffb3e3a3c1cbf695e.jpeg"},{"id":105223724,"identity":"fa43887b-96b6-4760-a6eb-a44f06ce6fec","added_by":"auto","created_at":"2026-03-23 16:09:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3113480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7731088/v1/4a33e54c-3ac9-47ff-a587-87ff26285975.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Symptom Clusters and Network Analysis in Lung Cancer Patients Receiving Taxane-Based Chemotherapy: A Comprehensive Assessment Using the CIPNAT Multiscale Tool","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer ranks as the second most prevalent cancer and the leading cause of cancer-related mortality worldwide, and it is the malignancy with the highest incidence and mortality rates in China \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to the GLOBOCAN 2020 data released by the International Agency for Research on Cancer (IARC), there were 2,206,771 new lung cancer cases and 1,796,144 lung cancer-related deaths worldwide in 2020. In China, the annual number of new lung cancer cases and deaths reached 815,563 and 714,699, respectively, accounting for 37.0% and 39.8% of the worldwide totals \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Chemotherapy remains a cornerstone of treatment for patients with lung cancer \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. As a systemic therapeutic modality, chemotherapy effectively inhibits tumor growth and metastasis, thereby prolonging the survival of patients \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Among chemotherapeutic agents, taxanes are the most commonly used first-line drugs for the treatment of lung cancer. They exert their anti-tumor effects by stabilizing tubulin and inhibiting the division of tumor cells. However, they are prone to causing dose-dependent sensory neuropathy, such as paclitaxel\u0026thinsp;\u0026minus;\u0026thinsp;induced axonal degeneration, and may exacerbate central sensitization through neuroinflammatory responses, resulting in a toxicity profile distinct from that of other chemotherapeutic agents (e.g.,platinum\u0026thinsp;\u0026minus;\u0026thinsp;based drugs) \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Lung cancer patients often experience multiple symptoms after chemotherapy \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, such as chest tightness, shortness of breath and cough caused by tumor bleeding irritating the mucosa or compressing the trachea \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, gastrointestinal symptoms \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, and psychological symptoms \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Compared with patients with other types of cancer, lung cancer patients endure the most severe symptom burden \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In a symptom survey involving 145 lung cancer patients after chemotherapy, the most common symptoms identified were fatigue, drowsiness, sleep disturbance, and pain\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite the widespread use of taxane-based chemotherapy in the treatment of lung cancer, patients often experience the interactive effects of multiple symptoms (e.g.,neuropathy, fatigue, nausea). However, existing research has significant limitations. Traditional clustering methods (e.g.,hierarchical clustering) can only identify static symptom clusters and cannot quantify the directional influences between symptoms or network hub nodes, which makes it difficult to guide dynamic interventions. Traditional statistical models (e.g.,regression analysis) assume independence between variables. In contrast, network analysis, based on the systems theory framework, views symptoms as a dynamically interacting system. By controlling for confounding effects through partial correlation networks, network analysis is better suited to reveal direct associations and feedback loops between symptoms\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the specific toxicity profile of taxanes, such as that related to neuroinflammation-mediated central sensitization, may contribute to the formation of a unique symptom network. However, relevant research remains scarce, and most studies are based on populations from Europe and America, with no validation in Asian patients (e.g.,due to metabolic differences in metabolism or the combined use of traditional Chinese medicine treatments). These methodological flaws and barriers to clinical translation underscore the necessity of applying network analysis,which can provide a mechanistic basis for precise interventions by quantifying the strength of associations between symptoms and topological structures (e.g.,the identification of hub nodes) \u003csup\u003e[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn summary, this study, which is based on symptom network analysis, treats symptoms as interacting network nodes. By quantifying connection strengths (i.e., edge weights0 and topological characteristics (e.g.,centrality metrics), this study can not only identify core hub symptoms but also reveal potential pathways of action. In recent years, network analysis has demonstrated its value in the research on symptoms of chronic diseases. However, significant gaps remain in understanding the specific symptom networks of lung cancer patients receiving taxane-based chemotherapy: (1) a lack of elucidation of network characteristics associated with the unique toxicity profile of these drugs (e.g., dose-dependent neuropathy) ; (2) a scarcity of data from Asian populations, which may lead to the oversight of culturally specific symptom associations. Therefore, this study intends to conduct a cross-sectional survey and employ network analysis based on the Gaussian graphical model (i.e., the EBIC-glasso algorithm) to systematically map the symptom interaction network in Asian lung cancer patients receiving taxane-based chemotherapy. This study aims to provide new evidence for the development of precise management strategies targeting hub nodes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eConvenience sampling was used to enroll 315 hospitalized lung cancer patients receiving taxane-based chemotherapy (paclitaxel, albumin-bound paclitaxel, docetaxel) at a Grade III-A hospital in Shanghai from January 2023 to June 2024. Inclusion criteria: (1) Pathologically confirmed primary bronchogenic carcinoma; (2) Patients undergoing taxane-based chemotherapy (paclitaxel, albumin-bound paclitaxel, docetaxel); (3) Physical Status Score (PS)\u0026thinsp;\u0026le;\u0026thinsp;2; (4) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (5) No cognitive impairment or communication difficulties; (6) Voluntary participation in questionnaire surveys with informed consent. Exclusion criteria: (1) Patients with peripheral neuropathy due to comorbidities; (2) Patients with concurrent malignancies; (3) Patients with concealed medical conditions.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe sample size for network analysis was determined based on the standard of 5\u0026ndash;6 participants per node \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In this study, there were 29 nodes, so at least 145\u0026ndash;174 patients were required. Considering a 20% sample loss rate, 174\u0026ndash;209 patients were required. Ultimately, 315 patients were surveyed in this study.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eIn this study, the Chemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT) and four additional assessment tools were used to evaluate symptom clusters in lung cancer patients undergoing taxane-based chemotherapy, while investigating the contributing factors from demographic, physiological, psychological, and sociological perspectives. Rigorous quality control measures were implemented during data collection, including investigator training, standardized calibration of assessment tools, and participant screening based on the inclusion and exclusion criteria. As the chemotherapy regimen involved 21-day cycles, the survey was administered after chemotherapy on the day of the patient's next hospitalization to ensure the accuracy of the data. Researchers collected the questionnaires on-site, verified the integrity of questionnaire completion, and provided explanations for patients' inquiries without influencing their treatment decisions\u003c/p\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eThe General Information Questionnaire\u003c/h2\u003e\u003cp\u003eThe General Information Questionnaire included sociodemographic data (e.g., age, gender, marital status, occupation, monthly per capita family income, medical payment method, etc.) and medical data (e.g., comorbid chronic diseases, laboratory indicators, body mass index [BMI], number of chemotherapy cycles, etc.).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eChemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT)\u003c/h2\u003e\u003cp\u003eThe Chemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT), developed by Tofthagen \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e at the University of South Florida in 2008, was localized into Chinese by Wang Yue and colleagues \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The Chinese version of the CIPNAT demonstrates strong psychometric properties, with item-total correlations ranging from 0.32 to 0.70. The Cronbach's α coefficient for the total scale is 0.94, while those for the subscales are 0.92 and 0.89, respectively. Item content validity index (CVI) values range from 0.89 to 1.00, with an average of 0.92 across all items. As a self-report instrument, the CIPNAT consists of two components: symptom experience assessment and evaluation of the impact on daily activities. A 0\u0026ndash;10 scoring scale is used, where higher total scores indicate more severe symptoms, greater distress, longer symptom duration, and more significant impairment in daily activities.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePittsburgh Sleep Quality Index (PSQI)\u003c/h3\u003e\n\u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI), developed by Buysse \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e and adapted into Chinese by Lu Taoying \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, is a scale consisting of 19 self-reported items and 5 items reported by others, designed to evaluate patients' sleep patterns over the past month. With a Cronbach's α coefficient of 0.845, the PSQI demonstrates strong reliability, validity, and stability. The scale covers seven dimensions: subjective sleep quality, time to fall asleep, total sleep duration, sleep efficiency, sleep disturbances, use of hypnotic medications, and impairment in daytime functioning. Each item is rated on a 0\u0026ndash;3 scale, with cumulative scores reflecting overall sleep quality; higher scores indicate poorer sleep quality.\u003c/p\u003e\n\u003ch3\u003eEuropean Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30)\u003c/h3\u003e\n\u003cp\u003eThe European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) is a standardized assessment tool for evaluating cancer patients' quality of life\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This 30-item questionnaire covers five functional domains (physical function, role function, emotional function, cognitive function, social function), three primary symptoms (fatigue, nausea/vomiting, pain), six subscale items (breathing difficulties, appetite loss, sleep disorders, constipation, diarrhea, financial status), and one overall quality of life measure. All five functional domains, three symptom dimensions, and six subscale items are scored using a 1\u0026ndash;4 system, where 1 indicates \"none\" and 4 represents \"very much\". The overall quality of life is rated on a 1\u0026ndash;7 scale.. Higher scores in the overall quality of life and functional domains indicate better health outcomes. Conversely, higher scores in symptom dimensions or subscale items suggest more severe symptoms or poorer quality of life. The questionnaire demonstrates excellent reliability and validity, with Cronbach's α coefficients exceeding 0.8 for both test-retest consistency and internal consistency.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePsychological Capital Questionnaire (PCQ)\u003c/h2\u003e\u003cp\u003eThe Psychological Capital Questionnaire (PCQ) was developed by Zhang K et al. \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e in 2008. This instrument assesses four dimensions: self-efficacy, resilience, hope, and optimism, comprising 26 items. The questionnaire demonstrates strong structural validity, with nearly all items showing factor loadings above 0.5 (mean\u0026thinsp;=\u0026thinsp;0.64) and item discrimination indices exceeding 0.6 (mean\u0026thinsp;=\u0026thinsp;0.71). Confirmatory factor analysis confirmed that both the four-factor model and the higher-order factor model exhibited good fit. Items of each subscale of the questionnaire had Cronbach's α coefficients of 0.86, 0.83, 0.80, and 0.76, respectively, while the overall Cronbach's α coefficient reached 0.90, indicating excellent internal consistency reliability. Each item is scored using a 7-point Likert scale (1\u0026ndash;7), where 1 = \"completely inconsistent\" and 7 = \"completely consistent,\" with total scores ranging from 26 to 182. Higher scores reflect higher levels of psychological capital and stronger psychological resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEthical considerations\u003c/h2\u003e\u003cp\u003e This study was conducted in compliance with the revised Declaration of Helsinki (2013) and was approved by the Ethics Committee of Zhongshan Hospital, Fudan University (Ethics Approval No.: B2024-405R). All participants voluntarily participated in the study. Participants provided written informed consent for each study phase.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStudy sample and data preprocessing\u003c/h2\u003e\u003cp\u003eWe analyzed data from N\u0026thinsp;=\u0026thinsp;315 patients who completed the multidimensional symptom and function assessments. Variables included self-efficacy, resilience, hope, optimism, EORTC QLQ-C30 functional scales (physical, role, emotional, cognitive, social), general health status, a set of symptom-type indicators (fatigue, nausea/vomiting, pain, dyspnea, insomnia, appetite loss, constipation, diarrhea, financial difficulty), and multiple PSQI (Pittsburgh Sleep Quality Index) and CIPNAT items. Continuous and ordinal symptom/item scores were retained in their original scoring metric for network estimation. All analyses were performed in R (version\u0026thinsp;\u0026ge;\u0026thinsp;4.0) using the packages qgraph, bootnet, and NetworkComparisonTest.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCount data were described using frequencies and percentages. Scores for frequency, severity, and distress of symptoms, which were severely skewed, were described using the median (interquartile range, IQR; P25\u0026ndash;P75). Symptoms were first transformed into a suitable correlation matrix using the cor_auto function in the qgraph package, which computes Spearman correlations (or polychoric correlations, if needed) to accommodate non-normal data. We then estimated a Gaussian graphical model (GGM) representing the symptom network. This was done via the graphical LASSO (least absolute shrinkage and selection operator) with Extended Bayesian Information Criterion (EBIC) model selection (hyperparameter γ\u0026thinsp;=\u0026thinsp;0.5) to obtain a sparse partial correlation network. The tuning parameter γ\u0026thinsp;=\u0026thinsp;0.5 was chosen to balance model fit and parsimony, as commonly recommended in psychopathological network analysis. The resulting network was visualized using the qgraph package: node layouts were arranged by symptom domain for interpretability, and edge width was scaled to the absolute value of the regularized partial correlation. Positive edges are displayed as solid blue lines and negative edges as dashed red lines. We computed node centrality metrics including strength, closeness, betweenness, and expected influence. Centrality indices were calculated using algorithms in the qgraph and bootnet packages (which compute these metrics from the estimated network).\u003c/p\u003e\u003cp\u003eWe assessed the accuracy of edge weight estimates using a nonparametric bootstrap (2000 samples), computing 95% confidence intervals (CI) for each edge weight. Narrower bootstrap CIs indicate more precise estimates. Network stability was evaluated via case-dropping bootstrap (2000 samples) to estimate the correlation stability (CS) coefficient for each centrality metric. The CS coefficient quantifies the maximum proportion of cases that can be dropped while retaining, with 95% probability, a correlation of at least 0.7 between original and subset centrality indices. We consider CS-coefficients above 0.25 (preferably above 0.50) as indicative of adequate stability. These analyses (edge-weight CIs and CS coefficient calculation) were conducted using the bootnet package.\u003c/p\u003e\u003cp\u003eTo examine differences in network structure between predefined groups (younger vs. older participants), we applied the Network Comparison Test (NCT) using the NetworkComparisonTest package. This permutation-based test evaluates whether two independent networks differ in overall structure or global connectivity. We conducted 5,000 permutations to test (a) network invariance (M-test for structural differences) and (b) global strength invariance (total connectivity) between subgroups. In addition, we inspected between-group differences in specific node centralities (e.g., expected influence) using bootstrap difference tests where applicable. All analyses were performed in R (version 4.5.0) using the packages qgraph, bootnet, and NetworkComparisonTest.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSample characteristics (brief)\u003c/h2\u003e\u003cp\u003eA total of 315 lung cancer patients receiving taxane-based chemotherapy were enrolled in this study. The gender distribution showed a significant imbalance, with male patients accounting for 86.35% (272/315) and female patients only 13.65% (43/315). This gender composition is partially consistent with the historical gender distribution characteristics of lung cancer in clinical settings, where male patients have traditionally been more represented due to factors such as higher smoking prevalence and occupational exposure risks. However, it should be noted that recent epidemiological data\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e indicate an increasing trend in lung cancer incidence among women, which may be associated with risk factors such as secondhand smoke exposure and indoor air pollution from biomass fuel combustion. Summary distributions (median [interquartile range] or n (%)) for key psychosocial and symptom scales were presented in Table\u0026nbsp;1. Median scores for psychosocial scales were: self-efficacy 33.00 (IQR 28.00\u0026ndash;38.00), resilience 31.00 (IQR 27.00\u0026ndash;35.00), hope 29.00 (IQR 25.00\u0026ndash;33.00), and optimism 30.00 (IQR 27.00\u0026ndash;36.00). Functional scales (EORTC QLQ-C30) showed median physical functioning 80.00 (IQR 66.67\u0026ndash;93.33) and role functioning 100.00 (IQR 66.67\u0026ndash;100.00). The prevalence distributions of symptom-type indicators (e.g., fatigue, dyspnea, insomnia, appetite loss), other demographic and clinical characteristicswere summarized in Table\u0026nbsp;1. \u003cb\u003eTable\u0026nbsp;1.\u003c/b\u003e Baseline characteristics of the sample (N\u0026thinsp;=\u0026thinsp;315). Data were presented as median (IQR) for continuous/ordinal scales and n (%) for categorical variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean or n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(SD) or %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003e272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.35%\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\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.65%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.63%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.81%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.50\u0026ndash;72.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.73%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.62%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.95\u0026ndash;25.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCivil Servant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompany or Private Enterprise Employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.25%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic Institution Staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.44%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.67%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.73%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh School/Technical School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior College\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.98%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor's Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.76%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving Arrangement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving Alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.49%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with Family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.51%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly household income per capita (RMB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.37%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.11%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5001\u0026ndash;8000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;8000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.52%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdenocarcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-small cell carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroendocrine carcinoma-\u003c/p\u003e\u003cp\u003eSmall cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroendocrine carcinoma-\u003c/p\u003e\u003cp\u003eLarge cell neuroendocrine carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSarcomatoid carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Insurance Payment Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.44%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.16%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural Cooperative Medical Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.13%\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\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG Performance Status(PS) Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.75%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.98%\u003c/p\u003e\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\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite Blood cells (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.57\u0026ndash;8.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed Blood cells (\u0026times;10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e177.50\u0026ndash;279.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of chemotherapy sessions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcurrent Immunotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.71%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.29%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapeutics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esingle-agent chemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.81%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecombination chemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.19%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.00\u0026ndash;38.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.00\u0026ndash;35.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.00\u0026ndash;33.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.00\u0026ndash;36.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Functioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.67\u0026ndash;93.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRole Functioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.67\u0026ndash;100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional Functioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.67\u0026ndash;100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Functioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.67\u0026ndash;100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Functioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.67\u0026ndash;100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral Health Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.67\u0026ndash;50.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.11\u0026ndash;44.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNausea and Vomiting Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;16.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShortness of Breath Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsomnia Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;66.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAppetite Loss Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstipation Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiarrhea Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial Difficulty Symptom Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u0026ndash;2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Onset Time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u0026ndash;2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Duration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Disturbances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u0026ndash;2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypnotic Medications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaytime Functioning Impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIPNAT Severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;6.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIPNAT Bothersomeness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;1.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIPNAT Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u0026ndash;2.00\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=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEstimated Network:\u003c/h2\u003e\u003cp\u003eThe estimated symptom network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) consisted of 29 nodes corresponding to symptom domains. Edges represent regularized partial correlations among symptoms. Positive partial correlations were represented by solid blue edges and negative partial correlations by red dashed edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The largest edge weights were observed between Optimism (PPQ4) and Hope (PPQ3) (r\u0026thinsp;=\u0026thinsp;0.625), Frequency (CIPNAT3) and Bothersomeness (CIPNAT2) (r\u0026thinsp;=\u0026thinsp;0.603), and Sleep Efficiency (PSQI4) and Sleep Duration (PSQI3) (r\u0026thinsp;=\u0026thinsp;0.522). These strong associations suggest closely linked symptom pairs. Overall, most edges were positive (only a few negative edges were present), indicating that higher levels of one symptom tended to co-occur with higher levels of other related symptoms. The total network connectivity (global strength) was high, reflecting a tightly interconnected symptom structure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eCentrality Indices:\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the centrality values for each symptom. The node \u0026ldquo;QLQ7 (Fatigue)\u0026rdquo; emerged as the most central in this network: it had the highest strength centrality (2.735), highest closeness centrality (2.078),and highest betweenness centrality (3.944), identifying it as a key hub symptom. By contrast, the symptom \u0026ldquo;CIPNAT3 (Frequency of chemotherapy-induced peripheral neuropathy)\u0026rdquo; showed the highest expected influence (EI\u0026thinsp;=\u0026thinsp;1.417), indicating that it exerts the largest overall effect on the network when accounting for the sign of connections. Other nodes had lower centrality scores (see Table\u0026nbsp;1). These results suggest that QLQ7 (Fatigue) is a core symptom connecting many others, while CIPNAT3 (Frequency of chemotherapy-induced peripheral neuropathy) strongly influences the network\u0026rsquo;s state.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eNetwork Accuracy and Stability:\u003c/h2\u003e\u003cp\u003eFigure 3 shows the bootstrapped 95% confidence intervals for all edge weights (red points denote observed estimates; grey bands represent the CIs). In general, the intervals were relatively narrow (see Fig.\u0026nbsp;3), indicating precise estimation of edge strengths. In the case-dropping bootstrap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the correlation of each node\u0026rsquo;s centrality index in the subsamples with its value in the full sample remained high even when dropping up to 75% of the sample cases. The CS coefficient for strength and for expected influence was 0.673 (substantially above the 0.50 guideline), indicating excellent stability; the CS coefficient for closeness also was 0.673. The CS coefficient for betweenness, although above the minimal acceptable threshold (0.25), was lower than that for strength and closeness Overall, these results demonstrate that the network parameters (especially strength and EI) were stable under sampling variability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Differences:\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the symptom networks and centrality indices in different age groups (\u0026le;\u0026thinsp;65 vs. \u0026gt;65 years). No significant differences emerged when the symptom networks between subgroups were compared. The NCT showed that the network structures were invariant (M\u0026thinsp;=\u0026thinsp;0.240, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.347) and that global strength was comparable between groups (Global Strength\u0026thinsp;=\u0026thinsp;12.516 vs. 12.418, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.802). In other words, neither the arrangement of connections nor the overall connectivity differed significantly across groups. Likewise, pairwise comparisons of node centralities (e.g., expected influence for each symptom) yielded no significant group differences (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings indicate that the symptom network topology and the importance of central symptoms did not vary meaningfully by subgroup in our sample.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eSummary of Main Findings\u003c/h2\u003e\u003cp\u003eThis study employed network analysis to investigate the complex interrelationships among multidimensional symptoms in 315 lung cancer patients undergoing taxane-based chemotherapy. Our findings provide a critical foundation for developing precise clinical interventions. Network analysis revealed three pairs of strongly correlated symptoms: optimism (PPQ4)-hope (PPQ3) (r\u0026thinsp;=\u0026thinsp;0.625), symptom frequency (CIPNAT3)-bothersomeness (CIPNAT2) (r\u0026thinsp;=\u0026thinsp;0.603), and sleep efficiency (PSQI4)-sleep duration (PSQI3) (r\u0026thinsp;=\u0026thinsp;0.522). The network was characterized by predominantly positive edges and high global connectivity, indicating a tendency for symptoms to co-occur and mutually reinforce. Centrality analyses identified fatigue (EORTC QLQ-C30, QLQ7) as the network\u0026rsquo;s key hub (highest strength centrality, closeness centrality, and betweenness centrality) and frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) as the node with the strongest overall influence (highest expected influence). This strong interconnectedness implies that effectively managing fatigue (EORTC QLQ-C30, QLQ7) may yield positive ripple effects across the entire symptom cluster, although causal relationships require validation through longitudinal studies.\u003c/p\u003e\u003cp\u003eFurthermore, specific association patterns were identified, including a fatigue (EORTC QLQ-C30, QLQ7)\u0026ndash;appetite loss (EORTC QLQ-C30, QLQ12)\u0026ndash;insomnia (EORTC QLQ-C30, QLQ11) cluster and a cluster showing negative correlations between financial difficulties (EORTC QLQ-C30, QLQ15) and social and emotional functioning (EORTC QLQ-C30). The network structure exhibited high stability, with no significant differences observed across age subgroups (\u0026le;\u0026thinsp;65 years vs. \u0026gt;65 years), indicating its potential generalizability. Another notable finding was the relatively weak association between sleep disturbances (PSQI) and psychological capital (PCQ), suggesting that sleep disturbances (PSQI) may operate through a distinct pathway.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eInterpretation and Mechanistic Exploration of Core Symptoms\u003c/h2\u003e\u003cdiv id=\"Sec24\" class=\"Section4\"\u003e\u003ch2\u003eThe Central Hub Role of Fatigue\u003c/h2\u003e\u003cp\u003eFatigue (EORTC QLQ-C30, QLQ7) served as a core symptom in this study, with its high centrality indices (strength, closeness, and betweenness centrality) holding significant clinical implications. Network analysis revealed that the connection between \u0026ldquo;fatigue (EORTC QLQ-C30, QLQ7)\u0026rdquo; and \u0026ldquo;overall health status (EORTC QLQ-C30, QLQ6)\u0026rdquo; ranked first in strength centrality, closeness centrality, and betweenness centrality. Specifically, fatigue (EORTC QLQ-C30, QLQ7) exhibited the highest strength centrality (rs, Spearman\u0026rsquo;s correlation coefficient\u0026thinsp;=\u0026thinsp;2.735), closeness centrality (rc, closeness correlation coefficient\u0026thinsp;=\u0026thinsp;2.078), and betweenness centrality (rb, betweenness correlation coefficient\u0026thinsp;=\u0026thinsp;3.944) within the network, indicating its role as a central hub of the symptom cluster. This finding is consistent with the known side effects of taxane chemotherapy (such as docetaxel and paclitaxel), which often induce significant fatigue and consequently affect quality of life. The underlying mechanisms may involve neurotoxicity, release of pro-inflammatory cytokines (e.g., TNF-α, IL-1β, IL-6), and energy metabolism disorders. Taxanes may lead to fatigue through direct or indirect neurotoxic effects \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, potentially impacting peripheral nerve function and causing neuropathy, which in turn exacerbates the sensation of fatigue \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 may induce fatigue by activating neuroinflammatory pathways and affecting neurotransmitter metabolism and function \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Energy metabolism disorders represent another crucial mechanism of fatigue. The rapid growth and metabolic demands of tumor cells may lead to insufficient energy supply, thereby causing fatigue \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Therefore, prioritizing fatigue as an intervention target may positively alleviate the overall symptom cluster in lung cancer patients receiving taxane-based chemotherapy through its central position.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eCIP Frequency (CIPNAT3): The Network Driver Symptom\u003c/h2\u003e\u003cp\u003eThe frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) had the highest expected influence (EI\u0026thinsp;=\u0026thinsp;1.417), indicating that the frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) may act as a \u0026ldquo;trigger\u0026rdquo; to set off the entire symptom network. This is consistent with previous studies suggesting that chemotherapy-induced peripheral neuropathy (CIPN) may lead to sensory, motor, and autonomic dysfunction, thereby severely affecting patients\u0026rsquo; quality of life\u003csup\u003e[\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This finding suggests that the neurotoxicity of taxanes may shape a unique symptom network profile. The highest expected influence of frequency indicates that the \u0026ldquo;frequency of occurrence\u0026rdquo; of symptoms, rather than their \u0026ldquo;severity\u0026rdquo; or \u0026ldquo;degree of distress,\u0026rdquo; may more effectively amplify the cascading effect of the entire network. Considering the symptom triggering mechanism, this supports Barsevick\u0026rsquo;s \u0026ldquo;symptom frequency\u0026ndash;overall distress\u0026rdquo; hypothesis \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In clinical practice, early identification and reduction of high-frequency symptoms (such as nocturnal numbness and tingling) may be more effective in improving patients\u0026rsquo; overall condition than simply controlling single severe events.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eSymptom Frequency-Bothersomeness Loop: A Key Modulator of Network Dynamics\u003c/h2\u003e\u003cp\u003eSymptom Frequency (CIPNAT3) and Bothersomeness (CIPNAT2) (r\u0026thinsp;=\u0026thinsp;0.603) reflects the \u0026ldquo;symptom appraisal loop\u0026rdquo; in cancer patients: more frequent symptoms are more likely to be perceived as bothersome, which in turn may amplify emotional distress and reduce functional capacity. For patients receiving taxane-based chemotherapy, this loop may be exacerbated by the chronicity of taxane-related symptoms (e.g., persistent peripheral neuropathy). Importantly, this association highlights that symptom management should not only target frequency (e.g., pharmacologic interventions for fatigue) but also address subjective bother (e.g., cognitive-behavioral strategies to reframe symptom impact).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003ePathophysiological and Psychosocial Interpretation of Key Symptom Clusters\u003c/h2\u003e\u003cp\u003eThe strong correlation cluster of \u0026ldquo;fatigue (EORTC QLQ-C30, QLQ7)\u0026ndash;anorexia (i.e., appetite loss, EORTC QLQ-C30, QLQ12)\u0026ndash;insomnia (EORTC QLQ-C30, QLQ11)\u0026rdquo; may be closely related to the systemic effects of taxane-based chemotherapy. The potential common biological pathways underlying this cluster may include inflammatory responses, dysregulation of the hypothalamic-pituitary-adrenal axis (HPA axis), and metabolic alterations \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. On the other hand, the negative correlation cluster of \u0026ldquo;financial difficulty (EORTC QLQ-C30, QLQ15)\u0026ndash;social function (EORTC QLQ-C30, QLQ5)\u0026ndash;emotional function (EORTC QLQ-C30, QLQ3)\u0026rdquo; highlights the significant impact of socioeconomic factors on patients\u0026rsquo; psychological health (assessed by PCQ). Financial stress may not only directly trigger anxiety and depression but also limit patients\u0026rsquo; ability to access social support and high-quality medical resources, thereby further impairing their social and emotional functions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eUnique Patterns of Symptom Associations and Implications for Intervention\u003c/h2\u003e\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\u003ch2\u003eProtective Role of Functional and Psychological Resources\u003c/h2\u003e\u003cp\u003eIn this study, physical function (EORTC QLQ-C30, QLQ1) and emotional function (EORTC QLQ-C30, QLQ3) were negatively correlated with major symptoms such as fatigue (EORTC QLQ-C30, QLQ7) and anorexia (EORTC QLQ-C30, QLQ12), indicating that maintaining or enhancing patients' physical and emotional functional levels may be an important resource for buffering the negative impacts of symptoms (e.g., fatigue, anorexia). Research \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e has shown that a comprehensive intervention approach combining pharmacological, psychological, nursing, and family support may be effective for fatigue. Pharmacological treatment can alleviate fatigue symptoms, psychological interventions can improve emotional states, nursing interventions can provide guidance for daily living, and family support can enhance patients' confidence and adherence. Therefore, enhancing patients' functional levels through rehabilitation training and psychological interventions may help buffer the negative impacts of symptoms.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eIndependence of Sleep Problems\u003c/h3\u003e\n\u003cp\u003eThe weak associations between sleep disturbances (PSQI) and psychological capital dimensions (PCQ) such as hope (PPQ3) and resilience (PPQ2) indicate that conventional interventions targeting positive psychological constructs may have limited efficacy in addressing sleep-related issues. Therefore, more targeted strategies should be considered, such as cognitive-behavioral therapy for insomnia, pharmacological management, or sleep environment modifications, to more effectively ameliorate sleep problems.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eReliability Verification of Network Stability and Clinical Significance of Universality\u003c/h2\u003e\u003cp\u003eThe network structure in this study demonstrated high stability. The correlation stability coefficients (CS\u0026thinsp;=\u0026thinsp;0.53 and 0.49) for the strength and expected influence both exceeded the recommended threshold of 0.25 \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, indicating that the network structure was highly robust and less susceptible to sample fluctuations, thereby supporting the reliability of the conclusions. The stability results substantiate the key findings; for instance, a stable network structure implies that critical conclusions such as \u0026ldquo;fatigue (EORTC QLQ-C30, QLQ7) as a core symptom\u0026rdquo; are highly reproducible in similar populations. This provides a reliable basis for subsequent clinical application and generalization.\u003c/p\u003e\u003cp\u003eMore importantly, age subgroup analysis revealed no statistically significant differences in the overall network structure, connection strength, or centrality order of symptoms among patients of different age groups (\u0026le;\u0026thinsp;65 years vs. \u0026gt;65 years). This finding of \u0026ldquo;age universality\u0026rdquo; holds considerable clinical value. It suggests that core intervention strategies developed based on these results\u0026mdash;such as prioritizing fatigue management in all adult patients, regardless of age\u0026mdash;are universally applicable. Clinicians need not design vastly different or complex intervention plans solely based on age, which significantly enhances the efficiency and generalizability of interventions and allows resources to be concentrated on the most effective targets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eComparative Analysis with Existing Related Studies\u003c/h2\u003e\u003cp\u003eNumerous domestic and international studies on symptom networks in patients undergoing taxane-based chemotherapy for lung cancer or other solid tumors have also identified \u0026ldquo;fatigue\u0026rdquo; as a core symptom \u003csup\u003e[\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. For example, a study \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e of 1,255 lung cancer patients receiving chemotherapy patients identified four symptom clusters: fatigue, gastrointestinal, neuropsychiatric, and respiratory, with central symptoms being fatigue, vomiting, distress, and hemoptysis, respectively. In a study \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e of 221 gynecological oncology patients receiving chemotherapy patients, the most common symptoms were fatigue (n\u0026thinsp;=\u0026thinsp;197, 89.1%) and loss of appetite (n\u0026thinsp;=\u0026thinsp;192, 86.9%), while the most severe symptoms were fatigue (mean\u0026thinsp;=\u0026thinsp;4.17, SD\u0026thinsp;=\u0026thinsp;2.07) and anxiety (mean\u0026thinsp;=\u0026thinsp;3.43, SD\u0026thinsp;=\u0026thinsp;2.20). These findings are consistent with the conclusions of this study, underscoring the universality of the central role of the \u0026ldquo;fatigue symptom cluster\u0026rdquo; and further confirming that the results of this study are not coincidental but widely applicable.\u003c/p\u003e\u003cp\u003eHowever, some studies did not identify fatigue as a core symptom. The discrepancies may be attributed to the following factors:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDifferences in sample inclusion criteria. For instance, some studies \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e enrolled patients with poorer physical condition, who may present more complex symptoms, thereby altering the core structure of the symptom network.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eVariations in chemotherapy regimens. Single-agent versus dual-agent chemotherapy may differentially affect patient symptoms. Single-agent chemotherapy may result in relatively simpler symptoms, whereas dual-agent chemotherapy, due to drug synergism, may lead to more complex symptom manifestations, potentially obscuring the central role of fatigue\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDifferences in assessment tools. If a comprehensive evaluation system such as the CIPNAT\u0026thinsp;+\u0026thinsp;multi-scale assessment used in this study is not employed, it may not fully capture patient symptoms, leading to misjudgment of key nodes in the symptom network\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eStudies on quality of life in cancer patients generally indicate that physical function is negatively correlated with symptom severity\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. The results of this study are highly consistent with this view, revealing significant negative correlations between physical function and symptoms such as fatigue and pain. For example, better physical function is associated with milder fatigue and pain. This further validates the important impact of functional status on the symptom network, emphasizing that clinical treatment should not only focus on the disease itself but also prioritize the maintenance and improvement of patients\u0026rsquo; physical function. Rehabilitation training and other means to enhance physical function can thereby alleviate symptom burden.\u003c/p\u003e\u003cp\u003ePsycho-oncological research has shown that financial burden exacerbates psychological stress in cancer patients \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. This study provides new evidence for this perspective through symptom network analysis. The results demonstrate strong negative correlations between \u0026ldquo;financial difficulty\u0026rdquo; and emotional function as well as social function (r = \u0026minus;\u0026thinsp;0.162 to \u0026minus;\u0026thinsp;0.334), indicating that financial difficulties directly reduce patients\u0026rsquo; emotional regulation capacity and decrease their participation in social activities, indirectly worsening physical symptoms. This highlights the importance of addressing patients\u0026rsquo; financial status during treatment, and necessary financial assistance and psychological support should be provided to mitigate the negative impact of financial factors on the symptom network.\u003c/p\u003e\u003cp\u003eWhile most existing studies \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e focus on the effect of age on the severity of individual symptoms, this study, through network structure invariance testing, is the first to propose that \u0026ldquo;the topological structure of the symptom network in patients undergoing taxane-based chemotherapy is universal across age groups.\u0026rdquo; This conclusion addresses a gap in research on the relationship between age and symptom networks, challenging the previous clinical practice of over-differentiating symptom management strategies based on age. It provides a theoretical basis for developing simpler and more efficient universal symptom management strategies.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003eImplications for Clinical Practice\u003c/h2\u003e\u003cp\u003eThe findings of this study indicate that fatigue is a core symptom in lung cancer patients following taxane chemotherapy, serving as the most strongly connected and highest-centrality core node within the entire symptom network. This signifies that fatigue is not merely a distressing symptom but also a key driver that activates and sustains other symptoms such as anorexia and insomnia. Fatigue should be prioritized for assessment and management in symptom care. As a subjective experience, fatigue manifests not only physically but also psychologically. Standardized tools should be employed for quantitative assessment to objectively document its severity and dynamic changes. This should be a routine component of every follow-up visit, thereby mapping individualized fatigue trajectories for patients and providing baseline data and feedback on intervention effectiveness. In clinical practice, clinical teams should focus on patients' emotional and physical states, understand the specific causes of their fatigue, develop personalized care plans, and consider integrated multidisciplinary care. A collaborative team led by oncology nurses and involving oncologists, psychologists, nutritionists, rehabilitation therapists, and palliative care specialists should be established. Patients should be encouraged to actively report fatigue levels and participate in decision-making processes. Jointly developing feasible activity/rest schedules with patients enhances patients\u0026rsquo; self-management capabilities, thereby reducing patients\u0026rsquo; feelings of helplessness. Only through a patient-centered care model\u0026mdash;grounded in precise assessment, non-pharmacological interventions, and supported by multidisciplinary teams\u0026mdash;can this core symptom be effectively addressed. This approach leverages improvements across the entire symptom network, ultimately delivering substantial quality-of-life gains that empower patients to navigate their treatment journey with greater positivity and dignity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eFirst, the sample was recruited from a single center, limiting generalizability to other populations or regions. This single-center study exclusively enrolled hospitalized lung cancer patients receiving taxane-based chemotherapy from one Grade III-A hospital in Shanghai. Regional disparities in healthcare resources exist, necessitating caution when extrapolating the study\u0026rsquo;s findings to primary care settings in China or other regions. Future studies should conduct multicenter, multi-regional investigations enrolling diverse populations. Second, the cross-sectional design lacks temporal perspective, as it only surveys symptoms 21 days post each cycle of taxane-based chemotherapy, and thus is unable to infer causal sequencing between symptoms. Future longitudinal studies should explore dynamic symptom networks over the course of taxane-based chemotherapy. Third, integration of biopsychosocial variables in the analysis is inadequate. Relevant biomarkers such as IL-6 and CRP (inflammatory markers) were not measured, and chemotherapy-related psychological factors (e.g., fear of recurrence, social support) were not assessed. Future research should incorporate these multifaceted dimensions (i.e., biopsychosocial variables). Fourth, the study has a marked gender imbalance (86.35% males, 13.65% females), rooted in China\u0026rsquo;s lung cancer epidemiology. Data \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e from 1990\u0026ndash;2021 show consistent male predominance: with male new cases doubling females\u0026rsquo; in 2021 (62,000 vs. 31,000). Smoking, a key driver of male-predominant squamous cell carcinoma, further reinforces male dominance in clinical samples, leading to recruitment bias .This imbalance limits external validity. Chinese females predominantly have adenocarcinoma (60%\u0026ndash;70% of cases) linked to non-smoking risks (e.g., kitchen fume), with distinct symptoms and treatment responses versus males\u0026rsquo; smoking-related squamous cell carcinoma. Moreover, the Bayesian Age-Period-Cohort (BAPC) model predicts 2021\u0026ndash;2036 female ASIR growth (35.7%) will vastly outpace males\u0026rsquo; (4.8%) \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Findings may thus reflect male-specific symptom networks, not females\u0026rsquo; adenocarcinoma-related patterns. Future studies should enroll more females (especially non-smoking risk-exposed) to improve generalizability .\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed network analysis to unravel the intricate interplay patterns among symptoms in lung cancer patients undergoing taxane-based chemotherapy. It identified fatigue as a core hub symptom strength centrality, which emerged as a critical intervention target by driving the formation of the \"fatigue-anorexia-insomnia\" symptom cluster, and frequency(CIPNAT3) as a key driver. The findings revealed that the symptom network exhibits dual characteristics, being influenced by both biological drivers and psychosocial factors, while demonstrating high stability and age universality. To our best knowledge, this research is the first to explicitly confirm the topological invariance of the symptom network in taxane-treated chemotherapy patients by leveraging a large sample size and the EBIC-glasso method. These results provide a scientific foundation for establishing a comprehensive intervention system that \"centers on fatigue management while addressing multidimensional factors\". The study supports the clinical promotion of standardized intervention protocols; however, further validation of the network's dynamic characteristics through longitudinal designs is warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTYH.L and L.Y contributed to the study conception and design. Material preparation and data collection were performed by M.Z and TYH.L. Analysis was performed by QY.S. The first draft of the manuscript was written by TYH.L and M.Z, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.The project administration for this research was undertaken by L.Y. Funding acquisition for this research was undertaken by Y.W. M.Dprovided overall supervision and guidance for the entire research project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKRATZER T B, BANDI P, FREEDMAN N D, et al. Lung cancer statistics, 2023 [J]. Cancer, 2024, 130(8): 1330\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMOUNTZIOS G, SUN L, CHO B C, et al. Tarlatamab in Small-Cell Lung Cancer after Platinum-Based Chemotherapy [J]. N Engl J Med, 2025, 393(4): 349\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAWAD M M, FORDE P M, GIRARD N, et al. Neoadjuvant Nivolumab Plus Ipilimumab Versus Chemotherapy in Resectable Lung Cancer [J]. J Clin Oncol, 2025, 43(12): 1453\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZHANG Y, ZHAO Z, LI W, et al. Mechanism of Taxanes in the Treatment of Lung Cancer Based on Network Pharmacology and Molecular Docking [J]. Curr Issues Mol Biol, 2023, 45(8): 6564\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDEGNER L F, SLOAN J A. Symptom distress in newly diagnosed ambulatory cancer patients and as a predictor of survival in lung cancer [J]. J Pain Symptom Manage, 1995, 10(6): 423\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSILVONIEMI M, VASANKARI T, L\u0026ouml;YTTYNIEMI E, et al. Symptom Assessment for Patients with Non-small Cell Lung Cancer Scheduled for Chemotherapy [J]. Anticancer Res, 2016, 36(8): 4123\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZHANG Y, CHEN Y C, HU L, et al. Chemotherapy-induced functional changes of the default mode network in patients with lung cancer [J]. Brain Imaging Behav, 2020, 14(3): 847\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHINZ A, KRAUSS O, HAUSS J P, et al. Anxiety and depression in cancer patients compared with the general population [J]. Eur J Cancer Care (Engl), 2010, 19(4): 522\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWONG M L, COOPER B A, PAUL S M, et al. Differences in Symptom Clusters Identified Using Ratings of Symptom Occurrence vs. Severity in Lung Cancer Patients Receiving Chemotherapy [J]. J Pain Symptom Manage, 2017, 54(2): 194\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWONG M L, PAUL S M, COOPER B A, et al. Predictors of the multidimensional symptom experience of lung cancer patients receiving chemotherapy [J]. Support Care Cancer, 2017, 25(6): 1931\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBOTHA J, DE RIDDER J H, POTGIETER J C, et al. Structural vascular disease in Africans: Performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: The SABPA study [J]. Exp Clin Endocrinol Diabetes, 2013, 121(9): 515\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZOU M, LIN M, HU K L, et al. Cross-Tissue Regulatory Network Analyses Reveal Novel Susceptibility Genes and Potential Mechanisms for Endometriosis [J]. Biology (Basel), 2024, 13(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDAVIS K A, MORGAN V L. Network Analyses in Epilepsy: Are Nodes and Edges Ready for Clinical Translation? [J]. Neurology, 2021, 96(5): 195\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBURGER J, ISVORANU A M, LUNANSKY G, et al. Reporting standards for psychological network analyses in cross-sectional data [J]. Psychol Methods, 2023, 28(4): 806\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLASS A N S, JORDAN D G, WINER E S. Using theory to guide exploratory network analyses [J]. J Clin Psychol, 2023, 79(2): 531\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFERN\u0026aacute;NDEZ-DE-LAS-PE\u0026ntilde;AS C, PALACIOS-CE\u0026ntilde;A M, VALERA-CALERO J A, et al. Understanding the interaction between clinical, emotional and psychophysical outcomes underlying tension-type headache: a network analysis approach [J]. J Neurol, 2022, 269(8): 4525\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTOFTHAGEN C S, MCMILLAN S C, KIP K E. Development and Psychometric Evaluation of the Chemotherapy-Induced Peripheral Neuropathy Assessment Tool [J]. Cancer Nurs, 2011, 34(4): E10-E20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eY W. Evaluation of peripheral neuropathy caused by chemotherapy in cancer patients [D], 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBUYSSE D J, REYNOLDS C F, 3RD, MONK T H, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research [J]. Psychiatry research, 1989, 28(2): 193\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLU T Y L Y, XIA P, ET AL. Reliability and validity analysis of Pittsburgh sleep quality index [J]. Chongqing Medical Journal, 2014, 43(03): 260\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSUN X G L C Y, WU M, ET AL. Evaluation of the Chinese version of EORTC QLQ-C30 in assessing quality of life in ovarian cancer patients [J]. Journal of Rehabilitation Medicine of China, 2007, (04): 335\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZHANG K Z S, DONG Y H. Positive psychological capital: measurement and its relationship with mental health [J]. Psychological and Behavioral Research, 2010, 8(01): 58\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGEE K, YENDAMURI S. Lung cancer in females-sex-based differences from males in epidemiology, biology, and outcomes: a narrative review [J]. Transl Lung Cancer Res, 2024, 13(1): 163\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCOSTA R, PASSOS G F, QUINT\u0026atilde;O N L M, et al. Taxane-induced neurotoxicity: Pathophysiology and therapeutic perspectives [J]. British Journal of Pharmacology, 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLI T, MIZRAHI D, GOLDSTEIN D, et al. Chemotherapy and peripheral neuropathy [J]. Neurological Sciences, 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLI Q-Y, XU H-Y, YANG H-J. Effect of proinflammatory factors TNF-α, IL-1β, IL-6 on neuropathic pain [J]. Zhongguo Zhong yao za zhi\u0026thinsp;=\u0026thinsp;Zhongguo zhongyao zazhi\u0026thinsp;=\u0026thinsp;China journal of Chinese \u0026hellip; 17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGUPTA S C, KIM J H, KANNAPPAN R, et al. Role of nuclear factor- κ B-mediated inflammatory pathways in cancer-related symptoms and their regulation by nutritional agents [J]. Experimental Biology and Medicine, 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYANG J, SHAY C, SABA N F, et al. Cancer metabolism and carcinogenesis [J]. Experimental Hematology and Oncology, 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHUNG H-W, LIU C-Y, CHEN H-F, et al. Impact of Chemotherapy-induced Peripheral Neuropathy on Quality of Life in Patients with Advanced Lung Cancer Receiving Platinum-based Chemotherapy [J]. 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePARK H J. Chemotherapy induced peripheral neuropathic pain [J]. Korean journal of anesthesiology, 2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLEE G, KIM S. Therapeutic Effects of Phytochemicals and Medicinal Herbs on Chemotherapy-Induced Peripheral Neuropathy [J]. Molecules, 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSIOKA C, KYRITSIS A P. Central and peripheral nervous system toxicity of common chemotherapeutic agents [J]. Cancer chemotherapy and pharmacology, 2009, 63(5): 761\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBARSEVICK A. Defining the Symptom Cluster: How Far Have We Come? [J]. Seminars in oncology nursing, 2016, 32(4): 334\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOH I-J, KIM K-S, KIM Y-C, et al. Altered Hypothalamus-Pituitary-Adrenal Axis Function: A Potential Underlying Biological Pathway for Multiple Concurrent Symptoms in Patients With Advanced Lung Cancer [J]. Psychosomatic Medicine, 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZHU Y, LIU Y. Application Efficacy of Nursing Interventions Guided by the Medication-Psychological-Nursing-Family-Support (MPNFS) Framework in Mitigating Cancer-Related Fatigue and Enhancing Pulmonary Function Among Lung Cancer Patients Undergoing Chemotherapy [J]. Cancer Management and Research, 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEPSKAMP S, BORSBOOM D, FRIED E I. Estimating psychological networks and their accuracy: A tutorial paper [J]. Behavior Research Methods, 2018, 50(1): 195\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLUO Y Y, LUO J H, SU Q, et al. Exploring Central and Bridge Symptoms in Patients with Lung Cancer: A Network Analysis [J]. Semin Oncol Nurs, 2024, 40(3): 9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZHANG J J, WANG S, WANG Y, et al. Symptom clusters and network analysis in patients with gynecologic cancer undergoing chemotherapy: A cross-sectional study [J]. Asia-Pac J Oncol Nurs, 2024, 11(12): 8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSHAO X D, WANG N, TANG K, et al. Network analysis used to investigate the symptoms of cancer patients during chemotherapy: a scoping review [J]. Discov Oncol, 2025, 16(1): 15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRHA S Y, LEE J. Stable Symptom Clusters and Evolving Symptom Networks in Relation to Chemotherapy Cycles [J]. J Pain Symptom Manage, 2021, 61(3): 544\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHARRIS C, HAMMER M J, CONLEY Y P, et al. Impact of Multimorbidity on Symptom Burden and Symptom Clusters in Patients Receiving Chemotherapy [J]. Cancer Med, 2025, 14(3): 20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRUSSELL J, WONG M L, MACKIN L, et al. Stability of Symptom Clusters in Patients With Lung Cancer Receiving Chemotherapy [J]. J Pain Symptom Manage, 2019, 57(5): 909\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTENG L P, ZHOU Z, YANG Y T, et al. Identifying central symptom clusters and correlates in patients with lung cancer post-chemotherapy: A network analysis [J]. Asia-Pac J Oncol Nurs, 2024, 11(4): 7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGUDHOOR M, MATHEW A T, GANACHARI A M, et al. Utilization of European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 scale for evaluation of quality of life among cancer patients treated with chemotherapy: A hospital-based observational study [J]. J Oncol Pharm Pract, 2024, 30(5): 844\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXING W J, LU Z Q, LU Y H, et al. Understanding and mitigating cancer-related financial toxicity in China: challenges and recommendations [J]. Lancet Reg Health-W Pac, 2025, 60: 5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSAITO Y. Taxane-Associated Acute Pain Syndrome: a Review of its Features and Management [J]. Curr Treat Options Oncol, 2025, 26(3): 187\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWU X R Z H, JI C, ET AL. Epidemiological trends of lung cancer in China and globally from 1990 to 2021 and prediction of incidence and mortality: An analysis based on the age-period-cohort model [J]. Journal of Modern Preventive Medicine, 2025, 52(10): 1735\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lung cancer, Taxane-based chemotherapy, Symptom network, Core symptoms, Network analysis, Chemotherapy-induced peripheral neuropathy, Fatigue, Age-related differences","lastPublishedDoi":"10.21203/rs.3.rs-7731088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7731088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e This study aimed to clarify the topological structure, core symptoms, and inter-symptom association patterns of the symptom network in lung cancer patients receiving taxane-based chemotherapy, and to provide a basis for formulating precise symptom management strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e A convenience sampling method was used to enroll 315 hospitalized lung cancer patients who received taxane-based chemotherapy (paclitaxel, albumin-bound paclitaxel, docetaxel) in a Grade III-A hospital in Shanghai from January 2023 to June 2024. Data on demographics, physiological, psychological, and symptomatic variables were collected using a general information questionnaire, the Chemotherapy-Induced Peripheral Neuropathy Assessment Tool (CIPNAT), the Pittsburgh Sleep Quality Index (PSQI), the Psychological Capital Questionnaire (PCQ), and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30). A symptom network was constructed using the graphical LASSO (least absolute shrinkage and selection operator) based on the Extended Bayesian Information Criterion (EBIC) (EBIC-glasso) algorithm.Centrality analysis was conducted to identify core symptoms, the bootstrap method was used to verify the network accuracy and stability, and the Network Comparison Test (NCT) was applied to analyze differences in network structure between two age groups (\u0026le;\u0026thinsp;65 years vs. \u0026gt;65 years).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e Among the 315 patients, 86.35% were male, with a median age of 68 years (interquartile range [IQR]:60.50\u0026ndash;72.00 years), and 58.73% were aged over 65 years. Three pairs of strongly correlated symptoms were identified in the symptom network: optimism and hope (r\u0026thinsp;=\u0026thinsp;0.625), symptom frequency and bothersomeness (r\u0026thinsp;=\u0026thinsp;0.603), and sleep efficiency and sleep duration (r\u0026thinsp;=\u0026thinsp;0.522). The network was dominated by positive connections and exhibited high global connectivity, indicating that symptoms tended to co-occur and mutually reinforce each other. Centrality analysis showed that fatigue (QLQ7) was the key hub node in the network, with the highest strength centrality (2.735), closeness centrality (2.078), and betweenness centrality (3.944). The frequency of chemotherapy-induced peripheral neuropathy (CIPNAT3) was driver node of the network, with the highest expected influence (EI\u0026thinsp;=\u0026thinsp;1.417). The network showed good stability, with a correlation stability (CS) coefficient of 0.673 for strength centrality and expected influence. Subgroup analysis by age revealed no significant differences in network structure (M\u0026thinsp;=\u0026thinsp;0.240, p\u0026thinsp;=\u0026thinsp;0.347) or global connectivity (12.516 vs. 12.418, p\u0026thinsp;=\u0026thinsp;0.802) between the two age groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e The symptom network of lung cancer patients receiving taxane-based chemotherapy exhibits a tightly interconnected characteristic. Fatigue is the core hub symptom, and the frequency of chemotherapy-induced peripheral neuropathy is the key driver symptom. Additionally, the network structure shows age universality. Clinically, interventions can be prioritized for the aforementioned core symptoms to achieve efficient management of the overall symptom cluster.\u003c/p\u003e","manuscriptTitle":"Symptom Clusters and Network Analysis in Lung Cancer Patients Receiving Taxane-Based Chemotherapy: A Comprehensive Assessment Using the CIPNAT Multiscale Tool","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 11:12:10","doi":"10.21203/rs.3.rs-7731088/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-31T15:35:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T00:38:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217429896424352110045672774138892484027","date":"2026-01-22T02:08:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T04:35:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160220335545658008689755743993607058527","date":"2026-01-07T05:45:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T00:10:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-05T00:07:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-02T01:42:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2025-09-28T02:00:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c01d3280-ee90-40df-a9e6-56a3bba2a180","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:06:09+00:00","versionOfRecord":{"articleIdentity":"rs-7731088","link":"https://doi.org/10.1007/s00520-026-10540-1","journal":{"identity":"supportive-care-in-cancer","isVorOnly":false,"title":"Supportive Care in Cancer"},"publishedOn":"2026-03-17 15:59:14","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2025-11-14 11:12:10","video":"","vorDoi":"10.1007/s00520-026-10540-1","vorDoiUrl":"https://doi.org/10.1007/s00520-026-10540-1","workflowStages":[]},"version":"v1","identity":"rs-7731088","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7731088","identity":"rs-7731088","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}