Network analysis of concurrent symptoms in Patients with lung Cancer during the Intermission of chemotherapy

Network analysis of concurrent symptoms in Patients with lung Cancer during the Intermission of chemotherapy | 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 Network analysis of concurrent symptoms in Patients with lung Cancer during the Intermission of chemotherapy Zhu Sumei, Liu Jun, Chen Xiaohong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7571800/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the incidence and severity of symptoms in patients with lung cancer during the intermission of chemotherapy, and to identify the core symptoms and core symptom clusters of patients with lung cancer during the intermission of chemotherapy by using the concurrent symptom network analysis method. Methods : From January 2024 to June 2025, 239 patients with lung cancer during the chemotherapy interval who were treated at Suzhou Municipal Hospital were selected by convenience sampling. The patients were investigated using the Chinese version of the Anderson Symptom Scale. Based on R software, a concurrent symptom association network was established. The characteristic indicators of the network structure were evaluated, and their stability and accuracy were tested. The centrality characteristics of the nodes were analyzed, and the predictability indicators of each node were calculated. Finally, the core symptoms were identified. Results : A total of 249 cases were finally included in this study. The most common symptom was restless sleep (95.18%), and the most serious symptom was forgetfulness (92.37%). Exploratory factor analysis showed that the cumulative variance contribution rate was 61.603%, and three symptom clusters were identified: emotion-functional symptom cluster, multiple somatic symptom cluster, and digestive tract - neurological symptom cluster. The results of symptom network analysis show that the strongest correlations within the symptom clusters are vomiting and numbness (r=0.360), restlessness and distress during sleep (r=0.341), and relationships with others and walking (r=0.317). The strongest connections among symptom clusters were drowsiness and poor appetite (r=0.262). Central index analysis: Drowsiness (EI=1.140) was the core symptom. The symptoms most affected by bridge expectations are sadness, drowsiness and poor appetite (BEI=0.807, 0.805, 0.718). Conclusions : Drowsiness is the core symptom of patients with lung cancer during the intermission of chemotherapy. Nursing staff can identify the symptoms and changes of patients with lung cancer during the intermission of chemotherapy early based on the concurrent network, accurately determine the key intervention targets, and reduce the burden of symptom management for patients with lung cancer during the intermission of chemotherapy. Lung Cancer The chemotherapy interval Symptom cluster Symptom management Network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Intruduction Lung cancer, ranked as the second most common malignancy globally and the leading cause of cancer-related deaths, imposes a substantial disease burden [ 1 ] . According to the 2022 China Cancer Statistics, lung cancer ranks first in both incidence and mortality among all malignancies in China [ 2 ] , with 61% of patients diagnosed at stage Ⅲ/Ⅳ, losing the opportunity for radical surgery [ 3 ] . Chemotherapy has become a key treatment for such patients to control tumor progression and prolong survival [ 2 – 4 ] . However, the adverse reactions during chemotherapy and the various symptoms experienced by patients during chemotherapy intervals severely compromise their quality of life [ 5 – 6 ] . Research has confirmed that lung cancer patients, upon transitioning from the relatively professional medical care environment of hospitals to other settings, frequently experience a series of complex and interrelated symptoms, including fatigue, pain, nausea, vomiting, sleep disorders, and emotional distress [ 6 – 7 ] . Notably, These symptoms not only bring physical and psychological pain to patients alone, but also may interact with each other to form a complex network of symptoms, further aggravating the physical and mental burden of patients.For instance, chemotherapy drugs can cause the Fatigue-Pain-Sleep Disturbance symptom cluster [ 8 ] and damage to the gastrointestinal mucosa can trigger the nausea-vomiting-loss of appetite symptom cluster [ 9 – 10 ] . The synergy of symptom clusters not only significantly reduces the quality of life of lung cancer patients, but also may shorten their survival time [ 11 ] . In addition, improper symptom management can also lead to a reduction in treatment dosage or treatment interruption, increase the interval between chemotherapy sessions, and thereby affect the efficacy of anti-tumor treatment. The chemotherapy interval refers to the buffer period between two chemotherapy cycles (from the first day after the end of this cycle to the first day before the start of the next cycle), which is typically 21 to 28 days [ 12 ] . Traditionally, symptoms were believed to gradually resolve as drugs are metabolized; however, recent clinical observations reveal that lung cancer patients still experience moderate-to-severe symptom burdens during CFI, with persistence rates of fatigue, shortness of breath, and cough reaching 63.9%, 62.5%, and 78.5%, respectively [ 13 ] . Notably, some symptoms (e.g., chemotherapy-induced peripheral neuropathy-related numbness) may even worsen during this interval [ 14 ] . Relevant studies have also shown that the symptom state during the interphase directly affects the tolerance of the next chemotherapy cycle [ 15 ] . For instance, nausea/vomiting leading to a reduction in food intake may cause an energy imbalance, which in turn may result in a higher degree of cancer-related fatigue [ 15 ] . This also indicates that symptom management during the chemotherapy intermission is extremely necessary in clinical practice. Current research on chemotherapy symptoms in lung cancer exhibits three main limitations. First, studies predominantly focus on the chemotherapy administration period (e.g., the first 7 days of the first cycle) [ 16 ] , neglecting CFI as a “critical window for symptom persistence and recovery.”. Second, prior studies use “symptom clusters” as the primary unit of analysis, evaluating overall changes after grouping symptoms by similarity [ 17 ] . This grouping method based on symptom clusters is difficult to reveal the direct interaction relationship among symptoms. For instance, whether fatigue exacerbates sleep disorders or whether sleep disorders reverse affect appetite, the immediate association of such "symptom-symptom", that is, the "symptom network", is masked in cluster analysis [ 18 – 19 ] .Third, there is a lack of exploration of the structural characteristics of the symptom network during CFI. Such as the existence of the "central symptom" (the node that has the greatest impact on other symptoms) or the variation of the association intensity within the interval [ 20 ] . This information is crucial for formulating "targeted intervention strategies" : identifying a central symptom can improve the overall burden through a single intervention, which is more effective than managing multiple isolated symptoms [ 21 ] . Symptom network analysis, as an emerging method, builds a "symptom-symptom" correlation matrix, quantifies the correlation strength among symptoms, identifies core nodes and individual symptom indicators, and provides a basis for precise symptom management [ 22 ] . A study on the analysis of lung cancer symptom networks in immunotherapy shows that intervention measures focused on addressing sadness can effectively reduce the severity of the entire symptom network, while early intervention for coughing and nausea can alleviate the burden of symptom management for patients [ 23 ] . However, such analysis has not been applied to CFI in lung cancer, leading to insufficient understanding of symptom interaction patterns during this phase and hindering the development of effective symptom management strategies. Therefore, this study focuses on the chemotherapy intermission period of lung cancer patients, aiming to reveal the correlation patterns among concurrent symptoms during this stage, identify core symptoms and network characteristics through symptom network analysis methods, and provide evidence-based basis for the formulation of precise and efficient symptom intervention strategies in clinical practice. 2. Materials and Methods 2.1 Study Design and Participants A cross-sectional study was conducted on lung cancer patients who were treated at Suzhou Municipal Hospital from January 2024 to July 2025 and were in the chemotherapy interval. The inclusion criteria are: (1) In line with the diagnostic criteria of the Guidelines for the Diagnosis and Treatment of Primary Lung Cancer neoadjuvant chemotherapy or adjuvant chemotherapy was administered, with a chemotherapy regimen of 4 to 8 cycles [ 24 ] ; (2) In the intermission period of chemotherapy; (3) Age ≥ 18 years old; (4) Be capable of effective communication and independently complete questionnaire surveys; (5) Voluntary participation and provision of informed consent. Exclusion criteria include: (1) Concurrent severe mental illness (such as schizophrenia) or cognitive impairment; (2) Severe comorbidities (such as decompensated heart failure and end-stage renal disease) can independently cause severe symptoms; (3) The questionnaire data is incomplete (10% of items are missing). According to the factor analysis sample size estimation method [ 25 ] , the sample size is 10 times the number of items. A total of 19 symptom items were analyzed in the study, taking into account a 20% inefficiency sample The minimum quantity is 228. This study has been approved by the Ethics Committee of Suzhou Municipal Hospital (No.k-2023-036-k01). Written informed consent was obtained. The study complied with the Declaration of Helsinki. A total of 249 lung cancer patients were ultimately included in this study to ensure the robustness of the data. 2.2 Data Collection Instruments 2.2.1 General Information Questionnaire The researchers designed it themselves based on the literature, including age, gender, educational level, occupational status, cancer type, time of cancer diagnosis (in years), treatment plan. 2.2.2 MD Anderson Symptom Inventory-Lung Cancer (MDASI-LC) A validated, disease-specific version of the MDASI for lung cancer patients [ 26 ] . The scale consists of two parts, the first part mainly assesses the incidence and severity of 13 symptoms (pain, fatigue, nausea, etc.) in the past 24 hours. The second part assesses the disruption that symptoms cause to daily life. These symptoms are manifested in six aspects of daily life, such as general activities, work, emotions, walking, relationships with others, and the joy of life.Each symptom was scored on a 0–10 subscale (0 = asymptomatic, 10 = most severe symptom). MDASI has good internal consistency, with Cronbach's α at being 0.87 [ 27 ] . 2.3 Data Collection Procedures Trained research nurses distributed questionnaires to eligible patients during their regular follow-up visits between chemotherapy cycles. Patients completed the questionnaires independently; for those with reading difficulties, the nurses read the items aloud and recorded responses. Data were double-checked for completeness, and missing values (< 5% of total data) were imputed using the median score of the corresponding symptom item. 2.4 Statistical Analysis Descriptive analysis and undirected network construction were conducted using R software (4.2.2). Use frequency, percentage, mean and standard deviation to describe the incidence and severity of demographic characteristics and symptoms. The symptom severity network graph was constructed using the qgraph package and based on the EBICglasso function and Spearman correlation analysis [ 28 ] . Symptoms are the nodes of the network. The weights of the edges connected between the nodes represent the partial correlation between the nodes. The thicker the edge, the stronger the correlation between the two symptoms. Use the Fruchterman-Reingold force to guide the layout and place the nodes with the strongest correlation at the center of the network. mgm packages are used to determine the predictability of nodes. Symptoms with high predictability indicate that the symptom can be controlled through its adjacent nodes [ 29 ] . Centrality analysis was conducted using strength, closeness and betweenness. Intensity is the sum of the lines connecting a symptom with other symptoms, indicating the influence of the symptom in the network. Close centrality is the reciprocal of the sum of the distances between a symptom and other symptoms. The larger the value, the more likely the symptom is to be at the center of the network. Mediating centrality refers to the number of times the symptom passes through the shortest path, that is, the bridging role of the symptom in the network. Using the bootnet package, the 95% confidence interval is estimated based on the Bootstrap algorithm to detect the stability of the centrality index after reducing the sample size in the network, and the correlation stability coefficient is calculated. It is generally believed that the correlation stability coefficient is preferably greater than 0.5 [ 30 ] . When P < 0.05 was considered statistically significant. 3. Results 3.1 Demographic and Clinical Characteristics of Participants A total of 249 lung cancer patients were ultimately included in this study. Among them, there were 177 male cases (71.08%) and 72 female cases (28.92%). The age ranged from 40 to 90 years old, with an average of (68.46 ± 9.33) years old. All ethnic groups are Han. Cancer classification: 36 cases (14.5%) of small cell carcinoma, 156 cases (62.6%) of adenocarcinoma, 41 cases (16.5%) of squamous cell carcinoma, and 16 cases (6.4%) of others. General information is shown in Table 1 . Table 1 Demographic and clinical characteristics of the participants (n = 249) Variable n constituent ratio(%) Gender Male 177 71.1 Female 72 28.9 Age (years) < 60 46 18.5 60–74 131 52.6 ≥ 75 72 28.9 Educational Level Illiterate 33 13.3 Primary school 120 48.2 Junior high school 64 25.7 Senior high school/Technical secondary school 23 9.2 College or above 9 3.6 Occupational Status Employed 30 12.1 Retired 136 54.6 Unemployed 17 6.8 Others 66 26.5 Cancer Type Small-cell carcinoma 36 14.5 Adenocarcinoma 156 62.6 Squamous cell carcinoma 41 16.5 Others 16 6.4 Time Since Diagnosis (years) ≤ 1 26 10.4 > 1–3 129 51.8 > 3–5 55 22.1 > 5 39 15.7 Treatment Modality Chemotherapy-dominant regimen 88 35.3 Immunotherapy-combined regimen 71 28.5 Targeted therapy regimen 49 19.7 Anti-angiogenic regimen 22 8.8 Bone metastasis supportive therapy 10 4.0 Others 9 3.6 Primary Caregiver Spouse 131 52.6 Children 60 24.1 Professional caregiver 26 10.4 Others 32 12.9 3.2 The incidence and severity of symptoms in patients with lung cancer The top five symptoms in lung cancer patients in terms of incidence rate are restlessness of sleep (95.18%), impact on work (94.38%), poor appetite (93.57%), drowsiness (93.57%), and impact on general activities (93.57%). The symptoms with higher severity scores were forgetfulness, drowsiness, sadness and poor appetite. See Table 2 . Table 2 Prevalence and Severity of Symptoms in Patients with Lung Cancer (n = 249) Symptom Number of Cases with Symptom (n) Prevalence (%) Severity Score [ M ( P 25 , P 75 );Score] Common Symptoms Pain 194 77.91 2.0 (1.0, 4.0) Fatigue 228 91.57 2.0 (1.0, 4.0) Nausea 224 89.96 2.0 (1.0, 3.0) Disturbed sleep 237 95.18 2.0 (1.0, 4.5) Distress 230 92.37 2.0 (1.0, 4.0) Shortness of breath 224 89.96 2.0 (1.0, 4.0) Difficulty remembering 230 92.37 3.0 (1.0, 5.0) Poor appetite 233 93.57 3.0 (1.0, 4.0) Drowsiness 233 93.57 3.0 (1.0, 5.0) Dry mouth 223 89.56 2.0 (1.0, 4.0) Sadness 232 93.17 3.0 (1.0, 5.0) Vomiting 232 93.17 2.0 (1.0, 4.0) Numbness 227 91.16 2.0 (1.0, 4.0) Interference with Daily Life General activities 233 93.57 2.0 (1.0, 4.0) Mood 231 92.77 2.0 (1.0, 4.0) Work 235 94.38 2.0 (1.0, 4.0) Relationships with others 232 93.17 2.0 (1.0, 4.0) Walking 223 89.56 2.0 (1.0, 4.0) Enjoyment of life 231 92.77 2.0 (1.0, 5.0) 3.3 Exploratory Factor Analysis of Symptoms in Lung Cancer Patients Nineteen symptoms were included in the exploratory factor analysis. The results showed that KMO = 0.940, Bartlett's sphericity test χ2 = 2702.999, P < 0.001, indicating that the data from this study are suitable for factor analysis. Exploratory factor analysis extracted a total of 3 factors with eigenvalues greater than 1, and the cumulative variance contribution rate was 61.603%, as shown in Table 3 . Based on the symptom factor loading, three symptom clusters were finally determined. In combination with the symptom characteristics, factor 1, factor 2, and factor 3 were respectively named the emotion-functional symptom cluster, the multiple somatic symptom cluster, and the digestive tract - neurological symptom cluster, as shown in Table 4 . Table 3 EFA Results of Symptoms in Patients with Lung Cancer—Total Variance Explained Component Initial Eigenvalues Extracted Sums of Squared Loadings Rotated Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 9.144 48.125 48.125 9.144 48.125 48.125 4.886 25.713 25.713 2 1.486 7.819 55.943 1.486 7.819 55.943 4.033 21.226 46.940 3 1.075 5.659 61.603 1.075 5.659 61.603 2.786 14.663 61.603 4 0.845 4.449 66.051 - - - - - - 5 0.719 3.785 69.837 - - - - - - 6 0.682 3.590 73.427 - - - - - - 7 0.611 3.215 76.642 - - - - - - 8 0.586 3.083 79.725 - - - - - - 9 0.523 2.755 82.480 - - - - - - 10 0.442 2.328 84.808 - - - - - - 11 0.426 2.243 87.051 - - - - - - 12 0.401 2.111 89.163 - - - - - - 13 0.387 2.035 91.197 - - - - - - 14 0.375 1.976 93.173 - - - - - - 15 0.328 1.724 94.897 - - - - - - 16 0.291 1.534 96.431 - - - - - - 17 0.268 1.410 97.841 - - - - - - 18 0.216 1.138 98.979 - - - - - - 19 0.194 1.021 100.000 - - - - - - Note: Extraction method: Principal component analysis. Table 4 Factor Loadings of Symptoms in Patients with Lung Cancer Symptom Cluster Symptom Factor Loading Factor 1 Factor 2 Factor 3 A. Emotional-functional symptom cluster A1. Sadness 0.453 - - A2. General activities 0.689 - - A3. Mood 0.764 - - A4. Work 0.791 - - A5. Relationships with others 0.796 - - A6. Walking 0.818 - - A7. Enjoyment of life 0.725 - - B. Multiple physical symptom cluster B1. Pain - 0.593 - B2. Fatigue - 0.522 - B3. Nausea - 0.655 - B4. Disturbed sleep - 0.685 - B5. Distress - 0.748 - B6. Shortness of breath 0.511 - - B7. Difficulty remembering 0.611 - - B8. Drowsiness - 0.566 - C. Gastrointestinal-neurological symptom cluster C1. Poor appetite - - 0.500 C2. Dry mouth - - 0.682 C3. Vomiting - - 0.806 C4. Numbness - - 0.671 3.4 Symptom Network Analysis of Lung Cancer Patients The symptom network of 249 lung cancer patients is shown in Fig. 1 . The blue lines represent positive correlations, each point represents a symptom, and the lines connecting the symptoms represent the relationship between the two. The thicker the edge lines, the stronger the relationship between the two. According to the thickness of the edge lines of the symptom network and the regularized partial correlation coefficient, it can be known that: ① the strongest correlation within the symptom cluster is: Among the digestive tract - neurological symptom clusters, the most strongly correlated ones were "vomiting" and "numbness" (C3-C4, regularized partial correlation coefficient r = 0.360), and among the multiple somatic symptom clusters, the most strongly correlated ones were "restlessness of sleep" and "distress" (B4-B5, regularized partial correlation coefficient r = 0.341). Among the emotion-functional symptom clusters, the strongest correlations are "relationships with others" and "walking" (A5-A6, regularized partial correlation coefficient r = 0.317). ② The strongest connections among symptom clusters are "drowsiness" in the multiple somatic symptom cluster and "poor appetite" in the digestive tract - nerve symptom cluster (B8-C1, regularized partial correlation coefficient r = 0.262). 3.5 Analysis of symptom network centrality index and bridge centrality index Expected influence (EI) takes into account the correlation between nodes when calculating the total sum of edge line weights. Therefore, it can reflect the practical significance of the symptom network. Thus, EI is selected as the centrality index in this study. As shown in Fig. 2 , "sleepiness (B8)" has the highest expected impact (EI = 1.140) and is the core symptom in the symptom network. Figure 3 shows the bridge expected influence (BEI) of each symptom: Among the emotion-functional symptom cluster, multiple somatic symptom cluster, and digestive tract - neurological symptom cluster, the symptoms with the highest bridge expectation influence are "sadness (A1)", "drowsiness (B8)", and "poor appetite (C1)" (BEI = 0.807, 0.805, 0.718), respectively. It is indicated that feelings of sadness, drowsiness and poor appetite are the bridge symptoms that connect the other two symptom clusters within their respective symptom clusters. The coefficients of the centrality index and the bridge centrality index are shown in Table 5 . The number r = 0.262. Table 5 Centrality and Bridge Centrality Indices of the Symptom Network Symptom EI BEI Symptom 0.996 0.807 A1. Sadness 0.966 0.402 A2. General activities 1.057 0.197 A3. Mood 1.036 0.295 A4. Work 0.894 0.126 A5. Relationships with others 1.054 0.131 A6. Walking 0.797 0.261 A7. Enjoyment of life 0.616 0.218 B1. Pain 0.931 0.418 B2. Fatigue 0.702 0.279 B3. Nausea 1.002 0.237 B4. Disturbed sleep 0.947 0.138 B5. Distress 0.858 0.313 B6. Shortness of breath 1.056 0.426 B7. Difficulty remembering 1.140 0.805 B8. Drowsiness 0.849 0.718 C1. Poor appetite 0.732 0.545 C2. Dry mouth 0.699 0.118 C3. Vomiting 0.944 0.552 C4 Numbness 0.944 0.552 3.6 Accuracy and stability of the symptom network 3.6.1 Accuracy The accuracy test of the symptom network shows that the 95% confidence interval of the edge weights obtained by the bootstrap method is relatively narrow, indicating that the edge weight assessment is relatively accurate. See Fig. 4 . 3.6.2 Stability The stability test shows that the Correlation-Stability coefficient (CS) of the expected effect of symptoms is 0.518, and the CS of the expected effect of Bridges is 0.518, indicating that the expected effect of symptoms and the expected effect of Bridges have sufficient stability, and the stability of the network structure is good. See Fig. 5 . 4. Discussion 4.1 Three symptom clusters were identified during the interval of chemotherapy for lung cancer The cumulative variance contribution rate of the three symptom clusters (emotion-functional symptom cluster, multiple somatic symptom clusters, and digestive tract - neurological symptom cluster) extracted by exploratory factor analysis reached 61.603% (KMO=0.940, Bartlett's sphericity test P <0.001), confirming that its classification has good structural validity. 4.1.1The characteristics of emotional functional symptom clusters Emotion-functional symptom cluster (including sadness, joy of life, walking, etc.) : Within this cluster, the factor loadings of "relationship with others" and "walking" are the highest (0.818, 0.796), suggesting that the social function of lung cancer patients is highly correlated with physical activity ability. This result is consistent with the research of Ma et al. Lung cancer patients reduce social interaction due to concerns about disease transmission and poor prognosis [31] . At the same time, symptoms such as fatigue and shortness of breath restrict activities, further intensifying social isolation and forming a closed loop of "low mood - reduced activity - social withdrawal" [9] . Clinically, psychological intervention and progressive rehabilitation training are needed to break this cycle. 4.1.2 The characteristics of multiple somatic symptom clusters Multiple somatic symptom clusters (including pain, fatigue, restlessness of sleep, etc.) : Within the cluster, the correlation between "restlessness of sleep" and "distress" is the strongest (r=0.341), confirming the bidirectional effect of "physical discomfort - psychological stress" : Tumor-related pain and shortness of breath can directly interfere with sleep, and sleep deprivation can exacerbate emotional distress, thereby amplifying the perception of physical symptoms [8] . This result is highly consistent with similar studies at home and abroad: When Ju et al. evaluated the symptoms of lung cancer patients undergoing chemotherapy using MDASI-LC, they found that the incidence of sleep disorders (92.3%) and fatigue (90.1%) was at the top, and the severity of psychological related symptoms (such as sadness) was often underestimated [7] . This might be closely related to the anxiety and depression of lung cancer patients due to the uncertainty of disease prognosis [9] . From a pathological perspective, the high incidence of sleep restlessness may stem from physiological discomforts such as tumor-related pain and shortness of breath, as well as pre-sleep anxiety caused by concerns about treatment outcomes [16] .The coexistence of drowsiness and forgetfulness may be related to the "fatigue-sleep-cognition" vicious cycle formed by neurotoxicity, anemia and sleep structure disorders (such as fragmented sleep) caused by chemotherapy drugs (such as platinum-based and paclitaxel drugs) [10] , suggesting that clinical attention should be paid to the correlation between symptoms simultaneously rather than treating the symptoms alone [15] . In addition, this group covers the most common physical adverse reactions of chemotherapy, such as pain caused by platinum-based drugs and fatigue caused by paclitaxel drugs, suggesting that symptom management should be combined with adjustments to chemotherapy regimens (such as prophylactic use of antiemetic drugs and nutritional support) [11] . 4.1.3 The characteristics of the digestive tract - neurological symptom cluster The digestive tract - neurological symptom cluster (including vomiting, numbness, dry mouth, etc.) : The intra-cluster correlation between "vomiting" and "numbness" is the strongest (r=0.360), and both are directly related to the toxicity of chemotherapy drugs. Relevant studies have also found that the digestive tract reactions of chemotherapy drugs such as cisplatin can cause vomiting [32] , while the peripheral neurotoxicity of paclitaxel and oxaliplatin can lead to numbness [32] . This is consistent with previous research results, that is, nausea is a stable sentinel symptom in the digestive tract symptom cluster (such as nausea, vomiting, loss of appetite) [33- 34] . In addition, as the chemotherapy cycle prolongs, the severity of digestive tract symptoms aggregation in postoperative chemotherapy patients with lung cancer keeps increasing, causing serious distress to the patients [35] . Moreover, dry mouth (with an incidence rate of 89.56%) is often overlooked in clinical practice, but it may aggravate poor appetite and affect the health of the oral mucosa. It needs to be improved through intervention measures such as oral care and artificial [36- 37] . 4.2 Drowsiness is the core symptom, while sadness and poor appetite are the expected bridging points Symptom network analysis revealed that drowsiness (EI=1.140) was the sole core symptom and a bridge node of multiple somatic symptom clusters (BEI=0.805). The bridge nodes of the mood-functional symptom cluster and the digestive tract - neural symptom cluster were sadness (BEI=0.807) and poor appetite (BEI=0.718), respectively. Moreover, the strongest connection between the clusters was drowsiness - poor appetite (r=0.262). Drowsiness, as the center of the network, is associated with symptoms covering physical (fatigue, restless sleep), digestive tract (poor appetite), and emotional (distress) domains, suggesting that intervention in drowsiness may have a domino effect. From a mechanistic perspective, drowsiness in lung cancer patients may be related to chemotherapy-induced anemia (decreased blood oxygen-carrying capacity), disorders of the hypothalamic-pituitary-adrenal axis (abnormal cortisol rhythm), and sleep apnea (tumor compression of the airway) [38- 39] . For the core symptom of drowsiness, it is recommended that clinical routine assessment of the patient's sleep quality (such as using Pittsburgh sleep quality index) [38] , hemoglobin level and dosage of chemotherapy drugs be conducted. Targeted sleep hygiene education (such as maintaining a regular schedule), correcting anemia (such as the use of erythropoietin), and adjusting the infusion time of chemotherapy drugs (to avoid drowsiness caused by infusion in the afternoon) are provided, thereby improving the quality of life of patients with lung cancer during the chemotherapy interval [40] . Furthermore, as a bridge node of the emotion-functional group, a high BEI value of sadness indicates that emotional problems may spread to physical symptoms through this node, such as reduced activity due to sadness, which in turn aggravates fatigue [41] . Poor appetite, which is connected with multiple body groups and digestive tract and nerve groups, may lead to decreased willingness to eat due to drowsiness, further causing malnutrition and aggravating neurotoxicity related numbness [42] .This discovery suggests that clinical intervention at bridge nodes should be prioritized. For instance, cognitive behavioral therapy for sadness and nutritional counseling and gastrointestinal motility drugs for poor appetite can effectively prevent the spread of symptoms among different groups and reduce the overall symptom burden. 4.3 High accuracy and stability of symptom network This study verified through the bootleg method that the 95% confidence interval of the edge weights of the symptom network is narrow, and the correlation stability coefficient (CS=0.518) between the expected effect and the bridge expected effect is higher than the critical value of 0.5 [28] , indicating that the network structure has good accuracy and stability. This result provides a reliable basis for clinical application. Compared with the traditional symptom cluster analysis that only focuses on grouping, the network analysis in this study can better reveal the dynamic association strength between symptoms. For example, the strong correlation between sleep restlessness and distress (r=0.341) suggests that these two symptoms need to be intervened simultaneously in clinical practice rather than dealing with sleep problems separately [17, 42] . Limitations This research has many advantages, but it also has limitations. For instance, this study is a single-center research, and the samples may have biases in terms of region and diagnosis and treatment patterns. The cross-sectional design also fails to capture the dynamic changes in the symptom network. It is suggested that in the future, large-sample and multi-center longitudinal studies should be carried out to dynamically monitor the differences in node associations among different cycles of chemotherapy. Declarations Funds ：No. Human Ethics and Consent ： This study has been approved by the Ethics Committee of Suzhou Municipal Hospital (No.k-2023-036-k01). Written informed consent was obtained. The study complied with the Declaration of Helsinki Author Contribution Z. SM. : Conceptualization;Methodology; Supervision; Writing – Original Draft: Drafted the initial manuscript and revised based on co-authors’ feedback.L. J. :Data Curation; Formal Analysis; Visualization; Writing – Review & Editing. L. XH.: Literature Review; Investigation; Writing – Review & Editing References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Could the use of saliva substitutes improve food oral processing in individuals with xerostomia? A systematic review. J Texture Stud. 2021;52(3):278–293. doi: 10.1111/jtxs.12591 Lee H, Kim HH, Kim KY, et al. Associations among sleep-disordered breathing, sleep quality, and lung cancer in Korean patients. Sleep Breath. 2023;27(4):1619–1628. doi: 10.1007/s11325-022-02750-8 Liu W, Luo M, Fang YY, Wei S, Zhou L, Liu K. Relationship between Occurrence and Progression of Lung Cancer and Nocturnal Intermittent Hypoxia, Apnea and Daytime Sleepiness. Curr Med Sci. 2019;39(4):568–575. doi: 10.1007/s11596-019-2075-6 Papadopoulos D, Papadoudis A, Kiagia M, Syrigos K. Nonpharmacologic Interventions for Improving Sleep Disturbances in Patients With Lung Cancer: A Systematic Review and Meta-analysis. J Pain Symptom Manage. 2018;55(5):1364–1381.e5. doi: 10.1016/j.jpainsymman.2017.12.491 Shin JW, Lee BJ, Chung S, Lee KS, Kim KL, Hwang JI. Understanding experiences of cancer-related fatigue in patients with lung cancer after their cancer treatment: a qualitative content analysis. Qual Life Res. 2024;33(4):975–987. doi: 10.1007/s11136-023-03578-9 Bovio G, Fonte ML, Baiardi P. Prevalence of upper gastrointestinal symptoms and their influence on nutritional state and performance status in patients with different primary tumors receiving palliative care. Am J Hosp Palliat Care. 2014;31(1):20–26. doi: 10.1177/1049909112474713 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7571800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533691341,"identity":"33f4ad7d-5a00-4292-83bd-0dd00ea3ddd0","order_by":0,"name":"Zhu Sumei","email":"","orcid":"","institution":"Department of Radiotherapy，SuZhou Municipal Hospital, Jiangsu, China","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Sumei","suffix":""},{"id":533691342,"identity":"d456423f-0616-4321-ab35-7dd92f2b4286","order_by":1,"name":"Liu 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1","display":"","copyAsset":false,"role":"figure","size":34043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConcurrent Symptom Network of Patients with Lung Cancer\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7571800/v1/3649e7aad38fd8abaaf7b862.png"},{"id":94916335,"identity":"38d5a4df-1001-4585-975b-1a02518b66f4","added_by":"auto","created_at":"2025-11-01 11:46:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentrality Index (Expected Influence)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7571800/v1/b4e478e1a598fb3bb86190a2.png"},{"id":94916341,"identity":"cdae7b4f-963b-4ed8-8358-731329995a7c","added_by":"auto","created_at":"2025-11-01 11:46:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBridge Centrality Index (Bridge Expected Influence)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7571800/v1/87a163341ed471e307ac7953.png"},{"id":94916346,"identity":"6fcc6d55-81d1-4776-b579-c08ca056d24b","added_by":"auto","created_at":"2025-11-01 11:46:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAccuracy of the Symptom Network\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7571800/v1/160e69eb43ba6c29ac61ccc2.png"},{"id":94916339,"identity":"5c1ac9cd-72a5-42d6-a775-a63fa3d204ca","added_by":"auto","created_at":"2025-11-01 11:46:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability of Expected Influence\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7571800/v1/17ab10fbfd285a6ecd9bbd40.png"},{"id":94916350,"identity":"36caa77c-7af6-43a4-956f-72047258395b","added_by":"auto","created_at":"2025-11-01 11:46:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability of Bridge Expected Influence\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7571800/v1/7edca7dc4b1075754894466a.png"},{"id":101205211,"identity":"a8dee952-36fe-40a0-b137-72290cdaf78d","added_by":"auto","created_at":"2026-01-27 09:48:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1883640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7571800/v1/8a376101-516a-4d3b-8744-95a1918773b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network analysis of concurrent symptoms in Patients with lung Cancer during the Intermission of chemotherapy","fulltext":[{"header":"1 Intruduction","content":"\u003cp\u003eLung cancer, ranked as the second most common malignancy globally and the leading cause of cancer-related deaths, imposes a substantial disease burden\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to the 2022 China Cancer Statistics, lung cancer ranks first in both incidence and mortality among all malignancies in China\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, with 61% of patients diagnosed at stage Ⅲ/Ⅳ, losing the opportunity for radical surgery\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Chemotherapy has become a key treatment for such patients to control tumor progression and prolong survival\u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, the adverse reactions during chemotherapy and the various symptoms experienced by patients during chemotherapy intervals severely compromise their quality of life\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Research has confirmed that lung cancer patients, upon transitioning from the relatively professional medical care environment of hospitals to other settings, frequently experience a series of complex and interrelated symptoms, including fatigue, pain, nausea, vomiting, sleep disorders, and emotional distress\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Notably, These symptoms not only bring physical and psychological pain to patients alone, but also may interact with each other to form a complex network of symptoms, further aggravating the physical and mental burden of patients.For instance, chemotherapy drugs can cause the Fatigue-Pain-Sleep Disturbance symptom cluster\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e and damage to the gastrointestinal mucosa can trigger the nausea-vomiting-loss of appetite symptom cluster\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The synergy of symptom clusters not only significantly reduces the quality of life of lung cancer patients, but also may shorten their survival time\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In addition, improper symptom management can also lead to a reduction in treatment dosage or treatment interruption, increase the interval between chemotherapy sessions, and thereby affect the efficacy of anti-tumor treatment.\u003c/p\u003e\u003cp\u003eThe chemotherapy interval refers to the buffer period between two chemotherapy cycles (from the first day after the end of this cycle to the first day before the start of the next cycle), which is typically 21 to 28 days\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Traditionally, symptoms were believed to gradually resolve as drugs are metabolized; however, recent clinical observations reveal that lung cancer patients still experience moderate-to-severe symptom burdens during CFI, with persistence rates of fatigue, shortness of breath, and cough reaching 63.9%, 62.5%, and 78.5%, respectively\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Notably, some symptoms (e.g., chemotherapy-induced peripheral neuropathy-related numbness) may even worsen during this interval\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Relevant studies have also shown that the symptom state during the interphase directly affects the tolerance of the next chemotherapy cycle\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. For instance, nausea/vomiting leading to a reduction in food intake may cause an energy imbalance, which in turn may result in a higher degree of cancer-related fatigue\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This also indicates that symptom management during the chemotherapy intermission is extremely necessary in clinical practice.\u003c/p\u003e\u003cp\u003eCurrent research on chemotherapy symptoms in lung cancer exhibits three main limitations. First, studies predominantly focus on the chemotherapy administration period (e.g., the first 7 days of the first cycle)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, neglecting CFI as a \u0026ldquo;critical window for symptom persistence and recovery.\u0026rdquo;. Second, prior studies use \u0026ldquo;symptom clusters\u0026rdquo; as the primary unit of analysis, evaluating overall changes after grouping symptoms by similarity\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This grouping method based on symptom clusters is difficult to reveal the direct interaction relationship among symptoms. For instance, whether fatigue exacerbates sleep disorders or whether sleep disorders reverse affect appetite, the immediate association of such \"symptom-symptom\", that is, the \"symptom network\", is masked in cluster analysis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.Third, there is a lack of exploration of the structural characteristics of the symptom network during CFI. Such as the existence of the \"central symptom\" (the node that has the greatest impact on other symptoms) or the variation of the association intensity within the interval\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This information is crucial for formulating \"targeted intervention strategies\" : identifying a central symptom can improve the overall burden through a single intervention, which is more effective than managing multiple isolated symptoms\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSymptom network analysis, as an emerging method, builds a \"symptom-symptom\" correlation matrix, quantifies the correlation strength among symptoms, identifies core nodes and individual symptom indicators, and provides a basis for precise symptom management\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. A study on the analysis of lung cancer symptom networks in immunotherapy shows that intervention measures focused on addressing sadness can effectively reduce the severity of the entire symptom network, while early intervention for coughing and nausea can alleviate the burden of symptom management for patients\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. However, such analysis has not been applied to CFI in lung cancer, leading to insufficient understanding of symptom interaction patterns during this phase and hindering the development of effective symptom management strategies. Therefore, this study focuses on the chemotherapy intermission period of lung cancer patients, aiming to reveal the correlation patterns among concurrent symptoms during this stage, identify core symptoms and network characteristics through symptom network analysis methods, and provide evidence-based basis for the formulation of precise and efficient symptom intervention strategies in clinical practice.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted on lung cancer patients who were treated at Suzhou Municipal Hospital from January 2024 to July 2025 and were in the chemotherapy interval. The inclusion criteria are: (1) In line with the diagnostic criteria of the Guidelines for the Diagnosis and Treatment of Primary Lung Cancer neoadjuvant chemotherapy or adjuvant chemotherapy was administered, with a chemotherapy regimen of 4 to 8 cycles\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e; (2) In the intermission period of chemotherapy; (3) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old; (4) Be capable of effective communication and independently complete questionnaire surveys; (5) Voluntary participation and provision of informed consent. Exclusion criteria include: (1) Concurrent severe mental illness (such as schizophrenia) or cognitive impairment; (2) Severe comorbidities (such as decompensated heart failure and end-stage renal disease) can independently cause severe symptoms; (3) The questionnaire data is incomplete (10% of items are missing).\u003c/p\u003e\u003cp\u003eAccording to the factor analysis sample size estimation method\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, the sample size is 10 times the number of items. A total of 19 symptom items were analyzed in the study, taking into account a 20% inefficiency sample The minimum quantity is 228. This study has been approved by the Ethics Committee of Suzhou Municipal Hospital (No.k-2023-036-k01). Written informed consent was obtained. The study complied with the Declaration of Helsinki. A total of 249 lung cancer patients were ultimately included in this study to ensure the robustness of the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection Instruments\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 General Information Questionnaire\u003c/h2\u003e\u003cp\u003eThe researchers designed it themselves based on the literature, including age, gender, educational level, occupational status, cancer type, time of cancer diagnosis (in years), treatment plan.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 MD Anderson Symptom Inventory-Lung Cancer (MDASI-LC)\u003c/h2\u003e\u003cp\u003eA validated, disease-specific version of the MDASI for lung cancer patients\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The scale consists of two parts, the first part mainly assesses the incidence and severity of 13 symptoms (pain, fatigue, nausea, etc.) in the past 24 hours. The second part assesses the disruption that symptoms cause to daily life. These symptoms are manifested in six aspects of daily life, such as general activities, work, emotions, walking, relationships with others, and the joy of life.Each symptom was scored on a 0\u0026ndash;10 subscale (0\u0026thinsp;=\u0026thinsp;asymptomatic, 10\u0026thinsp;=\u0026thinsp;most severe symptom). MDASI has good internal consistency, with Cronbach's α at being 0.87\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Collection Procedures\u003c/h2\u003e\u003cp\u003eTrained research nurses distributed questionnaires to eligible patients during their regular follow-up visits between chemotherapy cycles. Patients completed the questionnaires independently; for those with reading difficulties, the nurses read the items aloud and recorded responses. Data were double-checked for completeness, and missing values (\u0026lt;\u0026thinsp;5% of total data) were imputed using the median score of the corresponding symptom item.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive analysis and undirected network construction were conducted using R software (4.2.2). Use frequency, percentage, mean and standard deviation to describe the incidence and severity of demographic characteristics and symptoms. The symptom severity network graph was constructed using the qgraph package and based on the EBICglasso function and Spearman correlation analysis\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Symptoms are the nodes of the network. The weights of the edges connected between the nodes represent the partial correlation between the nodes. The thicker the edge, the stronger the correlation between the two symptoms. Use the Fruchterman-Reingold force to guide the layout and place the nodes with the strongest correlation at the center of the network. mgm packages are used to determine the predictability of nodes. Symptoms with high predictability indicate that the symptom can be controlled through its adjacent nodes\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Centrality analysis was conducted using strength, closeness and betweenness. Intensity is the sum of the lines connecting a symptom with other symptoms, indicating the influence of the symptom in the network. Close centrality is the reciprocal of the sum of the distances between a symptom and other symptoms. The larger the value, the more likely the symptom is to be at the center of the network. Mediating centrality refers to the number of times the symptom passes through the shortest path, that is, the bridging role of the symptom in the network. Using the bootnet package, the 95% confidence interval is estimated based on the Bootstrap algorithm to detect the stability of the centrality index after reducing the sample size in the network, and the correlation stability coefficient is calculated. It is generally believed that the correlation stability coefficient is preferably greater than 0.5\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. When \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic and Clinical Characteristics of Participants\u003c/h2\u003e\u003cp\u003eA total of 249 lung cancer patients were ultimately included in this study. Among them, there were 177 male cases (71.08%) and 72 female cases (28.92%). The age ranged from 40 to 90 years old, with an average of (68.46\u0026thinsp;\u0026plusmn;\u0026thinsp;9.33) years old. All ethnic groups are Han. Cancer classification: 36 cases (14.5%) of small cell carcinoma, 156 cases (62.6%) of adenocarcinoma, 41 cases (16.5%) of squamous cell carcinoma, and 16 cases (6.4%) of others. General information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and clinical characteristics of the participants (n\u0026thinsp;=\u0026thinsp;249)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003econstituent ratio(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.1\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\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;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational Level\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.3\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.2\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior high school/Technical secondary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupational Status\u003c/b\u003e\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\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.1\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.6\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer Type\u003c/b\u003e\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\u003eSmall-cell carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.5\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.6\u003c/p\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime Since Diagnosis (years)\u003c/b\u003e\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\u0026le;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment Modality\u003c/b\u003e\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\u003eChemotherapy-dominant regimen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunotherapy-combined regimen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTargeted therapy regimen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-angiogenic regimen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone metastasis supportive therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrimary Caregiver\u003c/b\u003e\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\u003eSpouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional caregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.9\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=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 The incidence and severity of symptoms in patients with lung cancer\u003c/h2\u003e\u003cp\u003eThe top five symptoms in lung cancer patients in terms of incidence rate are restlessness of sleep (95.18%), impact on work (94.38%), poor appetite (93.57%), drowsiness (93.57%), and impact on general activities (93.57%). The symptoms with higher severity scores were forgetfulness, drowsiness, sadness and poor appetite. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrevalence and Severity of Symptoms in Patients with Lung Cancer (n\u0026thinsp;=\u0026thinsp;249)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Cases with Symptom (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSeverity Score\u003c/p\u003e\u003cp\u003e[\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e75\u003c/sub\u003e);Score]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommon Symptoms\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNausea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisturbed sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShortness of breath\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifficulty remembering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (1.0, 5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor appetite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrowsiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (1.0, 5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry mouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (1.0, 5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVomiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumbness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInterference with Daily Life\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationships with others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWalking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnjoyment of life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Exploratory Factor Analysis of Symptoms in Lung Cancer Patients\u003c/h2\u003e\u003cp\u003eNineteen symptoms were included in the exploratory factor analysis. The results showed that KMO\u0026thinsp;=\u0026thinsp;0.940, Bartlett's sphericity test χ2\u0026thinsp;=\u0026thinsp;2702.999, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating that the data from this study are suitable for factor analysis. Exploratory factor analysis extracted a total of 3 factors with eigenvalues greater than 1, and the cumulative variance contribution rate was 61.603%, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on the symptom factor loading, three symptom clusters were finally determined. In combination with the symptom characteristics, factor 1, factor 2, and factor 3 were respectively named the emotion-functional symptom cluster, the multiple somatic symptom cluster, and the digestive tract - neurological symptom cluster, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEFA Results of Symptoms in Patients with Lung Cancer\u0026mdash;Total Variance Explained\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInitial Eigenvalues\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eExtracted Sums of Squared Loadings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eRotated Sums of Squared Loadings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of Variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCumulative %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e% of Variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCumulative %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e% of Variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCumulative %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25.713\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e46.940\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e61.603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote: Extraction method: Principal component analysis.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor Loadings of Symptoms in Patients with Lung Cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom Cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSymptom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor Loading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFactor 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFactor 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA. Emotional-functional symptom cluster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA1. Sadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA2. General activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA3. Mood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA4. Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA5. Relationships with others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA6. Walking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA7. Enjoyment of life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB. Multiple physical symptom cluster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB1. Pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB2. Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB3. Nausea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB4. Disturbed sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB5. Distress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB6. Shortness of breath\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB7. Difficulty remembering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB8. Drowsiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC. Gastrointestinal-neurological symptom cluster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1. Poor appetite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC2. Dry mouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC3. Vomiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC4. Numbness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.671\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Symptom Network Analysis of Lung Cancer Patients\u003c/h2\u003e\u003cp\u003eThe symptom network of 249 lung cancer patients is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The blue lines represent positive correlations, each point represents a symptom, and the lines connecting the symptoms represent the relationship between the two. The thicker the edge lines, the stronger the relationship between the two. According to the thickness of the edge lines of the symptom network and the regularized partial correlation coefficient, it can be known that: ① the strongest correlation within the symptom cluster is: Among the digestive tract - neurological symptom clusters, the most strongly correlated ones were \"vomiting\" and \"numbness\" (C3-C4, regularized partial correlation coefficient r\u0026thinsp;=\u0026thinsp;0.360), and among the multiple somatic symptom clusters, the most strongly correlated ones were \"restlessness of sleep\" and \"distress\" (B4-B5, regularized partial correlation coefficient r\u0026thinsp;=\u0026thinsp;0.341). Among the emotion-functional symptom clusters, the strongest correlations are \"relationships with others\" and \"walking\" (A5-A6, regularized partial correlation coefficient r\u0026thinsp;=\u0026thinsp;0.317). ② The strongest connections among symptom clusters are \"drowsiness\" in the multiple somatic symptom cluster and \"poor appetite\" in the digestive tract - nerve symptom cluster (B8-C1, regularized partial correlation coefficient r\u0026thinsp;=\u0026thinsp;0.262).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Analysis of symptom network centrality index and bridge centrality index\u003c/h2\u003e\u003cp\u003eExpected influence (EI) takes into account the correlation between nodes when calculating the total sum of edge line weights. Therefore, it can reflect the practical significance of the symptom network. Thus, EI is selected as the centrality index in this study. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \"sleepiness (B8)\" has the highest expected impact (EI\u0026thinsp;=\u0026thinsp;1.140) and is the core symptom in the symptom network.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the bridge expected influence (BEI) of each symptom: Among the emotion-functional symptom cluster, multiple somatic symptom cluster, and digestive tract - neurological symptom cluster, the symptoms with the highest bridge expectation influence are \"sadness (A1)\", \"drowsiness (B8)\", and \"poor appetite (C1)\" (BEI\u0026thinsp;=\u0026thinsp;0.807, 0.805, 0.718), respectively. It is indicated that feelings of sadness, drowsiness and poor appetite are the bridge symptoms that connect the other two symptom clusters within their respective symptom clusters. The coefficients of the centrality index and the bridge centrality index are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The number r\u0026thinsp;=\u0026thinsp;0.262.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCentrality and Bridge Centrality Indices of the Symptom Network\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003eSymptom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBEI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA1. Sadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA2. General activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA3. Mood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA4. Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA5. Relationships with others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA6. Walking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA7. Enjoyment of life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB1. Pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB2. Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB3. Nausea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB4. Disturbed sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB5. Distress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB6. Shortness of breath\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB7. Difficulty remembering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB8. Drowsiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC1. Poor appetite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC2. Dry mouth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC3. Vomiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC4 Numbness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.552\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=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Accuracy and stability of the symptom network\u003c/h2\u003e\u003cp\u003e\u003cb\u003e3.6.1 Accuracy\u003c/b\u003e The accuracy test of the symptom network shows that the 95% confidence interval of the edge weights obtained by the bootstrap method is relatively narrow, indicating that the edge weight assessment is relatively accurate. See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.6.2 Stability\u003c/b\u003e The stability test shows that the Correlation-Stability coefficient (CS) of the expected effect of symptoms is 0.518, and the CS of the expected effect of Bridges is 0.518, indicating that the expected effect of symptoms and the expected effect of Bridges have sufficient stability, and the stability of the network structure is good. See Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Three symptom clusters were identified during the interval of chemotherapy for lung cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cumulative variance contribution rate of the three symptom clusters (emotion-functional symptom cluster, multiple somatic symptom clusters, and digestive tract - neurological symptom cluster) extracted by exploratory factor analysis reached 61.603% (KMO=0.940, Bartlett\u0026apos;s sphericity test \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), confirming that its classification has good structural validity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.1The characteristics of emotional functional symptom clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmotion-functional symptom cluster (including sadness, joy of life, walking, etc.) : Within this cluster, the factor loadings of \u0026quot;relationship with others\u0026quot; and \u0026quot;walking\u0026quot; are the highest (0.818, 0.796), suggesting that the social function of lung cancer patients is highly correlated with physical activity ability. This result is consistent with the research of Ma et al. Lung cancer patients reduce social interaction due to concerns about disease transmission and poor prognosis\u003csup\u003e[31]\u003c/sup\u003e. At the same time, symptoms such as fatigue and shortness of breath restrict activities, further intensifying social isolation and forming a closed loop of \u0026quot;low mood - reduced activity - social withdrawal\u0026quot;\u003csup\u003e[9]\u003c/sup\u003e. Clinically, psychological intervention and progressive rehabilitation training are needed to break this cycle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.2 The characteristics of multiple somatic symptom clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple somatic symptom clusters (including pain, fatigue, restlessness of sleep, etc.) : Within the cluster, the correlation between \u0026quot;restlessness of sleep\u0026quot; and \u0026quot;distress\u0026quot; is the strongest (r=0.341), confirming the bidirectional effect of \u0026quot;physical discomfort - psychological stress\u0026quot; : Tumor-related pain and shortness of breath can directly interfere with sleep, and sleep deprivation can exacerbate emotional distress, thereby amplifying the perception of physical symptoms\u003csup\u003e[8]\u003c/sup\u003e. This result is highly consistent with similar studies at home and abroad: When Ju et al. evaluated the symptoms of lung cancer patients undergoing chemotherapy using MDASI-LC, they found that the incidence of sleep disorders (92.3%) and fatigue (90.1%) was at the top, and the severity of psychological related symptoms (such as sadness) was often underestimated\u003csup\u003e[7]\u0026nbsp;\u003c/sup\u003e. This might be closely related to the anxiety and depression of lung cancer patients due to the uncertainty of disease prognosis\u003csup\u003e[9]\u003c/sup\u003e. From a pathological perspective, the high incidence of sleep restlessness may stem from physiological discomforts such as tumor-related pain and shortness of breath, as well as pre-sleep anxiety caused by concerns about treatment outcomes\u003csup\u003e[16]\u003c/sup\u003e.The coexistence of drowsiness and forgetfulness may be related to the \u0026quot;fatigue-sleep-cognition\u0026quot; vicious cycle formed by neurotoxicity, anemia and sleep structure disorders (such as fragmented sleep) caused by chemotherapy drugs (such as platinum-based and paclitaxel drugs)\u003csup\u003e[10]\u003c/sup\u003e, suggesting that clinical attention should be paid to the correlation between symptoms simultaneously rather than treating the symptoms alone\u003csup\u003e[15]\u003c/sup\u003e. In addition, this group covers the most common physical adverse reactions of chemotherapy, such as pain caused by platinum-based drugs and fatigue caused by paclitaxel drugs, suggesting that symptom management should be combined with adjustments to chemotherapy regimens (such as prophylactic use of antiemetic drugs and nutritional support)\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.3 The characteristics of the digestive tract - neurological symptom cluster\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe digestive tract - neurological symptom cluster (including vomiting, numbness, dry mouth, etc.) : The intra-cluster correlation between \u0026quot;vomiting\u0026quot; and \u0026quot;numbness\u0026quot; is the strongest (r=0.360), and both are directly related to the toxicity of chemotherapy drugs. Relevant studies have also found that the digestive tract reactions of chemotherapy drugs such as cisplatin can cause vomiting\u003csup\u003e[32]\u003c/sup\u003e, while the peripheral neurotoxicity of paclitaxel and oxaliplatin can lead to numbness\u003csup\u003e[32]\u003c/sup\u003e. This is consistent with previous research results, that is, nausea is a stable sentinel symptom in the digestive tract symptom cluster (such as nausea, vomiting, loss of appetite)\u003csup\u003e[33-\u003c/sup\u003e\u003csup\u003e34]\u003c/sup\u003e. In addition, as the chemotherapy cycle prolongs, the severity of digestive tract symptoms aggregation in postoperative chemotherapy patients with lung cancer keeps increasing, causing serious distress to the patients\u003csup\u003e[35]\u003c/sup\u003e. Moreover, dry mouth (with an incidence rate of 89.56%) is often overlooked in clinical practice, but it may aggravate poor appetite and affect the health of the oral mucosa. It needs to be improved through intervention measures such as oral care and artificial\u003csup\u003e[36-\u003c/sup\u003e\u003csup\u003e37]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Drowsiness is the core symptom, while sadness and poor appetite are the expected bridging points\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSymptom network analysis revealed that drowsiness (EI=1.140) was the sole core symptom and a bridge node of multiple somatic symptom clusters (BEI=0.805). The bridge nodes of the mood-functional symptom cluster and the digestive tract - neural symptom cluster were sadness (BEI=0.807) and poor appetite (BEI=0.718), respectively. Moreover, the strongest connection between the clusters was drowsiness - poor appetite (r=0.262). Drowsiness, as the center of the network, is associated with symptoms covering physical (fatigue, restless sleep), digestive tract (poor appetite), and emotional (distress) domains, suggesting that intervention in drowsiness may have a domino effect. From a mechanistic perspective, drowsiness in lung cancer patients may be related to chemotherapy-induced anemia (decreased blood oxygen-carrying capacity), disorders of the hypothalamic-pituitary-adrenal axis (abnormal cortisol rhythm), and sleep apnea (tumor compression of the airway)\u003csup\u003e[38-\u003c/sup\u003e\u003csup\u003e39]\u003c/sup\u003e. For the core symptom of drowsiness, it is recommended that clinical routine assessment of the patient\u0026apos;s sleep quality (such as using Pittsburgh sleep quality index)\u003csup\u003e[38]\u003c/sup\u003e, hemoglobin level and dosage of chemotherapy drugs be conducted. Targeted sleep hygiene education (such as maintaining a regular schedule), correcting anemia (such as the use of erythropoietin), and adjusting the infusion time of chemotherapy drugs (to avoid drowsiness caused by infusion in the afternoon) are provided, thereby improving the quality of life of patients with lung cancer during the chemotherapy interval\u003csup\u003e[40]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, as a bridge node of the emotion-functional group, a high BEI value of sadness indicates that emotional problems may spread to physical symptoms through this node, such as reduced activity due to sadness, which in turn aggravates fatigue\u003csup\u003e[41]\u003c/sup\u003e. Poor appetite, which is connected with multiple body groups and digestive tract and nerve groups, may lead to decreased willingness to eat due to drowsiness, further causing malnutrition and aggravating neurotoxicity related numbness\u003csup\u003e[42]\u003c/sup\u003e.This discovery suggests that clinical intervention at bridge nodes should be prioritized. For instance, cognitive behavioral therapy for sadness and nutritional counseling and gastrointestinal motility drugs for poor appetite can effectively prevent the spread of symptoms among different groups and reduce the overall symptom burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 High accuracy and stability of symptom network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study verified through the bootleg method that the 95% confidence interval of the edge weights of the symptom network is narrow, and the correlation stability coefficient (CS=0.518) between the expected effect and the bridge expected effect is higher than the critical value of 0.5\u003csup\u003e[28]\u003c/sup\u003e, indicating that the network structure has good accuracy and stability. This result provides a reliable basis for clinical application. Compared with the traditional symptom cluster analysis that only focuses on grouping, the network analysis in this study can better reveal the dynamic association strength between symptoms. For example, the strong correlation between sleep restlessness and distress (r=0.341) suggests that these two symptoms need to be intervened simultaneously in clinical practice rather than dealing with sleep problems separately\u0026nbsp;\u003csup\u003e[17,\u003c/sup\u003e\u003csup\u003e\u0026nbsp;42]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has many advantages, but it also has limitations. For instance, this study is a single-center research, and the samples may have biases in terms of region and diagnosis and treatment patterns. The cross-sectional design also fails to capture the dynamic changes in the symptom network. It is suggested that in the future, large-sample and multi-center longitudinal studies should be carried out to dynamically monitor the differences in node associations among different cycles of chemotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunds\u003c/strong\u003e：No.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis study has been approved by the Ethics Committee of Suzhou Municipal Hospital (No.k-2023-036-k01). Written informed consent was obtained. The study complied with the Declaration of Helsinki\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ. SM. : Conceptualization;Methodology; Supervision; Writing \u0026ndash; Original Draft: Drafted the initial manuscript and revised based on co-authors\u0026rsquo; feedback.L. J. :Data Curation; Formal Analysis; Visualization; Writing \u0026ndash; Review \u0026amp; Editing. L. XH.: Literature Review; Investigation; Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;263. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21834\u003c/span\u003e\u003cspan address=\"10.3322/caac.21834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia, C., Dong, X., Li, H., et al., 2022. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin. Med. 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Am J Hosp Palliat Care. 2014;31(1):20\u0026ndash;26. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1049909112474713\u003c/span\u003e\u003cspan address=\"10.1177/1049909112474713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung Cancer, The chemotherapy interval, Symptom cluster, Symptom management, Network analysis","lastPublishedDoi":"10.21203/rs.3.rs-7571800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7571800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003eTo investigate the incidence and severity of symptoms in patients with lung cancer during the intermission of chemotherapy, and to identify the core symptoms and core symptom clusters of patients with lung cancer during the intermission of chemotherapy by using the concurrent symptom network analysis method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: From January 2024 to June 2025, 239 patients with lung cancer during the chemotherapy interval who were treated at Suzhou Municipal Hospital were selected by convenience sampling. The patients were investigated using the Chinese version of the Anderson Symptom Scale. Based on R software, a concurrent symptom association network was established. The characteristic indicators of the network structure were evaluated, and their stability and accuracy were tested. The centrality characteristics of the nodes were analyzed, and the predictability indicators of each node were calculated. Finally, the core symptoms were identified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 249 cases were finally included in this study. The most common symptom was restless sleep (95.18%), and the most serious symptom was forgetfulness (92.37%). Exploratory factor analysis showed that the cumulative variance contribution rate was 61.603%, and three symptom clusters were identified: emotion-functional symptom cluster, multiple somatic symptom cluster, and digestive tract - neurological symptom cluster. The results of symptom network analysis show that the strongest correlations within the symptom clusters are vomiting and numbness (r=0.360), restlessness and distress during sleep (r=0.341), and relationships with others and walking (r=0.317). The strongest connections among symptom clusters were drowsiness and poor appetite (r=0.262). Central index analysis: Drowsiness (EI=1.140) was the core symptom. The symptoms most affected by bridge expectations are sadness, drowsiness and poor appetite (BEI=0.807, 0.805, 0.718).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Drowsiness is the core symptom of patients with lung cancer during the intermission of chemotherapy. Nursing staff can identify the symptoms and changes of patients with lung cancer during the intermission of chemotherapy early based on the concurrent network, accurately determine the key intervention targets, and reduce the burden of symptom management for patients with lung cancer during the intermission of chemotherapy.\u003c/p\u003e","manuscriptTitle":"Network analysis of concurrent symptoms in Patients with lung Cancer during the Intermission of chemotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-01 11:46:17","doi":"10.21203/rs.3.rs-7571800/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f450dfa7-8a56-4d6a-8e72-dcd759073077","owner":[],"postedDate":"November 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-24T02:54:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-01 11:46:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7571800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7571800","identity":"rs-7571800","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}