Network analysis used to investigate the symptoms of cancer patients during chemotherapy: a scoping review.

Introduction

Chemotherapy serves as a cornerstone in cancer treatment, where appropriate administration can significantly enhance survival rates in patients with malignant neoplasms [ 1 ]. However, chemotherapy inevitably induces adverse effects, with most patients developing clusters of interrelated symptoms during treatment [ 2 – 4 ]. These symptom clusters negatively affect disease remission, treatment outcomes, and patient quality of life [ 5 – 7 ]. Common adverse symptoms include psychological symptoms such as sadness, distress, etc.; gastrointestinal symptoms such as nausea, vomiting, dry mouth, loss of appetite, etc.; and other chemotherapy-related symptoms such as pain, fatigue, sleep disturbance, and numbness [ 3 , 8 , 9 ]. Therefore, systematic monitoring and analysis of chemotherapy-induced symptoms in cancer patients is crucial for developing targeted interventions.

The prevailing approach to analyzing symptoms in chemotherapy patients rely on symptom cluster identification or trajectory analysis. However, these analyses often identify non-modifiable risk factors (e.g., demographic characteristics or underlying disease pathology) [ 8 , 10 – 13 ]. Most current interventions focus on symptom severity and address isolated symptoms or clusters, offering limited clinical utility. This approach contrasts with the emerging precision care paradigm, which emphasizes individualized, Artificial intelligence (AI) facilitated data-driven strategies to tailor interventions based on symptom interactions and patient-specific characteristics. Unlike traditional methods that prioritize symptom severity alone, precision care integrates network analysis to identify key targets such as core, bridge, and sentinel symptoms [ 14 , 15 ]. These targets reflect the systemic nature of symptom interplay, enabling clinicians to disrupt symptom cascades efficiently [ 16 – 18 ]. The emerging symptom network framework offers new potential in personalized oncology care.

Core symptoms, with the highest centrality in symptom network, strongly influence the co-occurrence and worsening of multiple symptoms through strong associations, addressing these symptoms first can substantially reduce overall symptom burden, establishing them key priorities for treatment. Bridge symptoms, which link different symptom clusters, amplify cross-cluster activation; targeting these symptoms helps break vicious cycles and improves management strategies. Sentinel symptoms, acting as early warning signs of symptom onset or deterioration, enable timely intervention to prevent downstream effects, thereby improving patient prognosis. This approach improves clinical care efficiency by monitoring sentinel symptoms, prioritizing intervening core symptoms, and managing bridge symptoms to limit cluster interactions [ 16 – 18 ].

The investigation of chemotherapy-related symptom associations through symptom network analysis is an emerging field. An overview of the major research themes and findings in this area is not yet available. This scoping review synthesizes existing studies on chemotherapy-induced symptom networks in oncology populations and critically evaluates methodological advancements in symptom network research. Innovatively, the study emphasizes the importance of longitudinal studies in symptom networks, offering new perspectives on symptom management and addressing challenges related to the lack of precision in intervention symptoms and inefficiency in symptom management.

Methods

This review was conducted using the framework proposed by Arksey and O’Malley [ 19 ] and the PRISMA guidelines [ 20 ]. No ethics approval was required for this review and this project is registered in Open Science Framework.

Eligibility criteria

The research question for this scoping review was “What is the symptoms networks like for the cancer patients during chemotherapy that has been reported?”. The inclusion criteria for studies in this review were broadly defined by population, context, concept, and study design as follows.

Participants/population

Participants aged ≥ 18 years diagnosed with at least one malignancy and currently undergoing chemotherapy were included; those receiving radiotherapy were excluded.

Context

This review included studies that focused on the symptom network analysis of cancer patients undergoing chemotherapy at any point during their treatment.

Concept

A symptom is defined as the subjective perception of impaired physiological function during disease progression [ 21 ]. A symptom cluster constitutes a group of two or more concurrent symptoms demonstrating stable interrelationships and predictive validity [ 22 ]. Symptom network analysis employs mathematical modeling and iterative analytical methods to investigate complex interactions between multiple symptoms [ 23 ]. By describing the network characteristics, special symptoms such as core symptoms, bridge symptoms and sentinel symptoms can be analyzed to illustrate the correlation between symptoms [ 24 ]. Symptom network modeling demonstrates greater prognostic value than isolated symptom evaluation or cluster-based approaches [ 25 ].

Study design

Both observational (e.g., cross-sectional, longitudinal) and mixed-methods studies were eligible for inclusion. Case reports, reviews, commentaries, and non-empirical studies (e.g., theoretical articles) were excluded. This ensured a focus on original research directly analyzing symptom networks in chemotherapy patients.

Search strategy and information sources

The search strategy followed a 3-step approach to identity published studies. Studies published both in English and Chinese language before 10th September 2024 were comprehensively searched and the search strategy was adapted to meet the requirements of each database searched with guidance from a medical librarian. An initial limited search via PubMed/MEDLINE identified Keywords and derivatives of terms. This informed the development of the search strategy tailored for each information source. English databases including PubMed/MEDLINE, Cochrane, EMBASE, CINAHL (EBSCO), Web of Science, and ProQuest were searched. Chinese databases including CNKI (China National Knowledge Infrastructure), VIPC, and CSPD (China Science Periodical Database) were searched. All search strategies are available on request. In addition, forward and backward citation searches were conducted on the included articles as a supplement to the database searches. Forward citation searches involve examining the reference lists of papers to identify earlier related studies, while backward citation searches entail locating later papers that cite the known articles.

Data extraction and synthesis

The retrieved literature will be imported into EndNoteX9 for deduplication. JX will conduct the first round of screening based on titles and abstracts, with results verified by ZS and YJ. Literature without abstracts or with uncertain relevance will proceed to the second round of screening, divided into two groups: XS and NW in Group 1, and KT and KW in Group 2. Disputed items will be submitted to YZ for arbitration.

Information extracted by the four authors (XS, NW, KT, and ZT) will be categorized and imported into an Excel spreadsheet for comparative analysis, covering key study characteristics and findings, including authors, location, year, aims, cancer types, sample characteristics, treatment, study design, instruments, R packages, core symptoms, and main findings. Discrepancies will be resolved through discussion among the four authors, with YZ acting as the final arbitrator for unresolved disagreements. Finally, XS will integrate the data to ensure completeness and consistency.

Results

Overview of included studies

Initially, 8948 articles were retrieved from both Chinese and English databases, 1 article [ 26 ] was added by citation forward and backward search, and 8611 articles remained after removing 338 duplicates. The second round of title and abstract screening excluded 8584 articles, and the remaining 27 articles went to the third round of full-text screening. Finally, the reviewers reached a consensus to include 12 articles in the review. The flow diagram and reasons for exclusion are shown in Fig. 1 .

Five of the twelve articles [ 27 – 31 ] were in Chinese and seven [ 25 , 26 , 32 – 36 ] were in English, published between 2019 and 2024. Nine articles [ 28 , 33 , 35 , 36 ] focused on specific cancer populations, and three articles [ 25 , 27 , 34 ] focused on various cancer populations, including leukemia, lymphoma, multiple myeloma, breast, lung, gastrointestinal, liver, esophageal, and gynecologic cancers. With the exception of specific cancer populations such as breast cancer and gynecologic cancer populations, the remaining study populations ranged from 25.35 to 77.7% female. All of the study populations included in the researches were in the midst of a chemotherapy cycle, with two articles [ 25 , 28 ] referring to specific cycles of chemotherapy, one [ 28 ] of which studied a population in the third cycle of chemotherapy, and the other longitudinal study [ 25 ] studied symptomatic data prior to chemotherapy and during the first four cycles of chemotherapy, for a total of five times. See Table 1 for study characteristics.

One paper [ 25 ] reported data from a longitudinal study and the remaining eleven [ 26 – 36 ] reported data from cross-sectional studies. These studies varied in terms of sample size (170–1328), symptom survey instrument, and the way symptoms were categorized. Seven studies [ 26 , 27 , 29 – 31 , 33 , 36 ] used the M.D. Anderson Symptom Inventory as a symptom survey instrument, three [ 28 , 32 , 34 ] used the Memorial Symptom Assessment Scale, and the remaining two used the PROMIS instruments from the PROMIS National Center-China [ 35 ] and a self-made Twenty-Symptom List [ 25 ], respectively. Most of the studies aimed to analyze the relationships among symptoms in cancer patients undergoing chemotherapy with a specific focus, e.g., analysis of specific target symptoms (core symptoms, bridge symptoms, etc.) to provide direction for symptom management interventions or analysis of factors influencing symptoms. The research objectives of all studies are shown in Table 1 . All symptom network plots were analyzed using R software, and the networks were constructed using different R packages.

Synthesis of the findings

Fatigue was consistently identified as the most prevalent and persistent core symptom across diverse cancer types, including breast, lung, ovarian, and gastric malignancies. Eight studies [ 25 , 27 , 29 – 31 , 33 , 35 , 36 ] identified fatigue as central in symptom networks, with high strength and connectivity to other symptoms (e.g., pain, sleep disturbances, depression). Longitudinal data [ 25 ] further highlighted its stability, peaking after the first chemotherapy cycle and remaining prominent across subsequent cycles.

Heterogeneity emerged in symptom cluster identification and measurement methodologies, limiting cross-study comparability. Six studies [ 25 , 27 , 28 , 33 , 34 , 36 ] identified symptom clusters (e.g., gastrointestinal, psychoneurological), but categorization methods varied (EFA, PCA, Walktrap algorithm). Symptom survey instruments influenced symptom reporting, MDASI focused studies emphasized fatigue and distress [ 26 , 27 , 29 – 31 , 33 , 36 ], while MSAS [ 28 , 32 , 34 ] highlighted nausea. Self-made scales introduced additional variability [ 25 ], underscoring methodological discrepancies.

Clustering of symptoms

Each study undertook a detailed analysis of the collected symptom data. However, the focus of the study and the analytical approach varied considerably, as detailed in Table 2 . Six studies [ 25 , 27 , 28 , 33 , 34 , 36 ] extracted the symptom clusters of the study subjects, and three of them [ 27 , 28 , 33 ] categorized the collected symptoms into related symptom clusters named according to the characteristics of the symptom clusters by using exploratory factor analysis (EFA), for example, Ye et al. [ 27 ], categorized the symptoms of cancer patients in the intervals between chemotherapy into three symptom clusters, Psycho-emotional symptom cluster, Gastrointestinal symptom cluster and Neurological symptom cluster, of which Psycho-emotional symptom cluster is the core symptom cluster. Rha et al. [ 25 ], using principal component analysis (PCA) and hierarchical cluster analysis (HCA), the symptom data of cancer patients before the start of chemotherapy and during the first four cycles of chemotherapy were classified into three common symptom clusters. The first cluster consisted of anxiety, depression, sleep disturbance, pain, and dyspnea. The second cluster consisted of fatigue, difficulty concentrating, and drowsiness. The third cluster included loss of appetite, taste change, nausea, and vomiting. Papachristou et al. [ 34 ] distinguished eight symptom clusters through occurrence, severity, and distress network analysis: Psychological symptom cluster, Hormonal symptom cluster, Respiratory symptom cluster, Nutritional symptom cluster, Chemotherapy-related symptom cluster, Pain and abdominal symptom cluster, Gastrointestinal symptom cluster, Epithelial symptom cluster. Through Walktrap algorithm, Luo et al. [ 36 ] identified four symptom clusters in lung cancer chemotherapy patients: fatigue, gastrointestinal, psychoneurological and respiratory. The remaining six studies [ 26 , 29 – 32 , 35 ] did not extract the symptom clusters of the analyzed study participants.

Characteristics of symptoms

A comprehensive literature review demonstrated that the majority of studies identified distinct target symptoms within symptom networks. However, significant variations were observed in both the sample populations analyzed and the specific symptom types identified, as summarized in Table 2 . The identified target symptoms included core symptoms, bridge symptoms, and sentinel symptoms. Eight out of the twelve studies [ 27 , 29 – 33 , 35 ]reported core symptoms derived from the overall sample. For instance, fatigue was identified as a core symptom for the full sample in five studies [ 25 , 27 , 30 , 33 , 35 ], while worrying [ 32 ] and distress [ 29 ]were identified as core symptoms for hematologic malignancies. In a gastric cancer population study, taste alteration [ 31 ] emerged as a core symptom within the full sample. Four studies [ 26 , 28 , 35 , 36 ] reported core symptoms based on subgroups. Luo et al. [ 36 ] analyzed symptom clusters in lung cancer patients undergoing chemotherapy. Their findings identified distinct core symptoms across different clusters: fatigue (fatigue cluster), vomiting (gastrointestinal cluster), distress (psychoneurological cluster), and hemoptysis (respiratory cluster). Researchers have also investigated sentinel and bridge symptoms. Liang et al. [ 33 ] conducted a symptom cluster analysis in breast cancer patients receiving chemotherapy. Their analysis revealed nausea as the sentinel symptom for the gastrointestinal cluster, distress for the psycho-sleep-related cluster, and dry mouth for the neurocognition cluster. Disturbed sleep and lack of appetite were identified as bridge symptoms.

Treatment status

All studies investigated patients during the period of chemotherapy. One longitudinal study [ 25 ] investigated patients prior to chemotherapy and during their first through fourth chemotherapy treatments. One study [ 28 ] investigated patients during their third chemotherapy treatment. Five studies [ 26 , 32 , 34 – 36 ] investigated patients during their chemotherapy treatments but did not report the specific chemotherapy treatments. Two studies [ 27 , 30 ] investigated patients during intermittent period of chemotherapy. The remaining three studies [ 29 , 31 , 33 ] merely indicated that they were conducted during the chemotherapy period but did not report a specific period, as detailed in Table 2 . The surveys conducted during intermittent period of chemotherapy [ 27 , 30 ] all reported fatigue as a core symptom. The results of the remaining studies were variable.

Symptoms over time

Among the included studies, one [ 25 ] employed a longitudinal design, whereas the other 11 utilized a cross-sectional approach. Longitudinal study participants collected baseline data before starting chemotherapy. After initiating chemotherapy, participants documented symptom severity (cycles 1–4) before each subsequent treatment session. Assessment intervals varied between 2–4 weeks depending on individual chemotherapy protocols.

The results of the longitudinal study indicated that patients' overall symptoms reached their highest level at the initial chemotherapy session and subsequently demonstrated a gradual decline in severity with an increase in the number of chemotherapy sessions. However, symptoms such as skin and nail changes, urinary problem, sexual problem, and peripheral neuropathy demonstrated a tendency to worsen over time. The longitudinal study also identified clusters of symptoms that stabilized before chemotherapy and persisted into the fourth cycle of chemotherapy. Fatigue was the most central symptom with the highest strength.

Discussion

This scoping review synthesizes key patterns in symptom presentation and interrelationships among cancer patients undergoing chemotherapy, with three central findings emerging from the analysis. First, fatigue was consistently identified as core symptom across diverse cancer types. Second, longitudinal data revealed dynamic symptom trajectories. Third, methodological heterogeneity, particularly the use of different symptom survey instruments, influenced symptom cluster identification and core symptom reporting. These findings underscore the need for longitudinal designs to clarify causal relationships between symptoms, optimize symptom management strategies, and reach a consensus on the reporting standards of symptom networks.

Symptoms

Fatigue emerged as the most prevalent symptom across breast, lung, ovarian, and other solid tumor populations in the analyzed studies, while hematologic malignancies demonstrated lower reporting frequency and reduced symptom severity. This discrepancy may arise from the younger age and higher baseline physical fitness of hematologic cancer patients, combined with the disease's greater curability potential and lower psychological burden.

Cancer-related fatigue (CRF) has been consistently identified as the most frequently reported symptom in oncology populations [ 37 ]. CRF is characterized by a subjective feeling of severe fatigue, which is greater and longer lasting than general fatigue that would be expected under a special condition. However, the accurate pathophysiology and molecular mechanisms of CRF have not been clearly explored. The potential mechanisms of CRF can be analyzed in terms of biological factors, psychological state and behavioral factors. In terms of biological factors, some scholars believe that it may be related to the activation of pro-inflammatory cytokines by the treatment regimen, such as INF-γ (Interferon-gamma), IL-4 (Interleukin-4), and TNF-α (Tumor Necrosis Factor-alpha), which may disrupt the HPA (hypothalamic–pituitary–adrenal) axis and reduce the synthesis and release of cortisol [ 38 ]. Moreover, physiological dysfunction including skeletal muscular and mitochondrial dysfunction has also been found to play an essential role in the induction of CRF. In addition, depression and anxiety often co-occur with fatigue, and the reduction of physical activity during the illness as well as sleep disturbances can hinder the recovery process and exacerbate the perception of fatigue [ 39 , 40 ]. The high prevalence and quality-of-life implications of CRF have established its predictive factors and nursing intervention efficacy as critical research priorities [ 41 ]. Symptom network analysis provides a framework to elucidate symptom interactions and prioritize nursing intervention targets.

Studies using the same scale reported a higher likelihood of similar symptoms and symptom clusters. In the literature reviewed, over half used the MDASI (M.D. Anderson Symptom Inventory) [ 26 , 27 , 29 – 31 , 33 , 36 ], and a quarter used the MSAS (Memorial Symptom Assessment Scale) [ 28 , 32 , 34 ]. Studies using the MDASI consistently reported fatigue and distress as specific symptoms, while those using the MSAS were more likely to report nausea. This finding may be attributed to the relatively small number of studies included in this analysis, or it may be attributed to the disparities between the two scales. The MDASI assesses the severity of 13 core symptoms and 6 functional impairments [ 42 ]. The use of continuous numerical scores (0–10) that focus on severity and functional impact allows for faster scoring and may facilitate its use in limited clinical settings. The MSAS assesses 32 symptoms, each of which must be scored on three dimensions: severity (level 4), frequency (level 4), and distress (level 5) [ 43 ]. The multi-dimensional scoring has a wider coverage, but may be more time-consuming to complete and analyze. The continuous scoring of the MDASI may be more likely to capture subtle changes in symptom intensity, whereas the multidimensional categorization scale of the MSAS may increase reporting of low intensity but high frequency symptoms.

The inclusion of studies with similar symptom outcomes across different cancer types raises questions about whether these similarities are genuine or due to the use of the same scale. Collecting data on patients’ symptom experiences is essential for building a symptom network. However, there are many cancer symptom scales, including universal ones for all cancer patients [ 7 , 25 , 27 , 34 ] and specific ones for each cancer type [ 44 , 45 ]. Furthermore, discrepancies between scales are inevitable due to the varying focus of their development. Additionally, some studies use self-made scales to collect symptom data [ 34 ]. It’s important to consider whether differences in data collection methods and tools affect the results.

We identified three key discrepancies in symptom reporting among the included studies. These discrepancies are: (1) Core [ 25 – 33 , 35 , 36 ], bridge [ 27 , 33 , 36 ], and sentinel [ 33 ] symptoms were all identified as specific targets in the symptom network but not all studies report these three targets simultaneously. (2) Core symptoms are the most frequently reported specific targets, with some studies reporting them for the full sample [ 25 , 27 , 29 – 33 , 35 ] and others for subgroups [ 26 , 28 , 33 , 35 , 36 ]. (3) Groups were distinguished by symptom characteristics [ 26 , 27 , 34 ], severity [ 28 ], and model [ 25 , 26 , 33 , 35 ] fit. As a new research approach, symptom reporting in symptom networks has no consensus.

Treatment status

Rha et al. [ 25 ] observed that chemotherapy symptoms changed dynamically as treatment progressed. Although all studies collected data during chemotherapy, only two specified the exact time. Previous research [ 46 – 48 ] and Rha et al. [ 25 ] show cycle-specific variations in symptom intensity and type, underscoring the necessity to explicitly define data collection time points. Such clarity is conducive to deepening the understanding of the population’s clinical context and enabling comparative analyses of symptom profiles across cycles.

Symptom network density

In exploring the association between HIV-positive duration and symptom network evolution, Zhu et al. [ 49 ] found that patients with longer HIV-positive duration exhibited a decrease in overall symptom intensity but a significant increase in symptom network density, indicating of elevated vulnerability and rapid disease progression risk mediated by cascading symptom interactions. This finding suggests that changes in network density may serve as an early warning indicator for high-risk patients. However, the study by Rha et al. [ 25 ] only reported that symptoms peaked after the first chemotherapy session and showed an overall decreasing symptom intensity as the course of treatment progressed. The study did not report changes in network density. To date, no study has systematically analyzed the evolution of network density with the cycle of treatment. Therefore, the mechanism of the association between the cycle of chemotherapy and symptomatic network density still needs to be further empirically explored.

Symptoms over time

This review includes one longitudinal study, [ 25 ] showing a stable symptom cluster and a dynamically changing symptom network. These findings are crucial for understanding core symptom mechanisms and guiding precise symptom management. It’s worth noting that the other cross-sectional studies offer limited guidance for symptom management. This is because they cannot capture dynamic symptom changes or confirm causal relationships in symptom networks, [ 50 , 51 ]. Thus, more longitudinal studies on chemotherapy symptoms in cancer patients are urgently needed. This will enable precise symptom management and clarify intervention directions and goals.

Cancer types

Among the 12 studies, the most frequently investigated cancer types were breast, lung, and gastric cancers, followed by Hematologic neoplasms and ovarian cancers. In contrast, cancers with a higher symptom burden, such as pancreatic and liver cancers, were less frequently reported in symptom networks. This imbalance indicates that current research on cancer related symptom networks predominantly focuses on common cancer types, while less attention is devoted to uncommon cancer types. This underrepresentation contributes to the complexity of generalizing the results of existing studies.

Chemotherapy purposes

Only three studies reported the purpose of chemotherapy, of which two [ 28 , 31 ] Focused specifically on adjuvant chemotherapy and one [ 25 ] included a mixed population (87.6% adjuvant versus 12.4% palliative chemotherapy). In addition, neoadjuvant chemotherapy is playing an increasingly important role in organ-sparing strategies for locally advanced cancer [ 52 , 53 ], but none of the included trials explicitly included neoadjuvant chemotherapy. This gap limits our understanding of the symptoms in the preoperative treatment stage of tumors.

Study limitations

This review has several limitations. First, it includes only one longitudinal study, and cross-sectional designs cannot establish temporal relationships or causal links between symptoms. Second, the heterogeneity of cancer types and treatments included in the study may result in different symptom clusters and specific symptoms, and this diversity increases the complexity of generalizing the findings across cancer types and limits their generalizability across different treatment regimens. Finally, the review only includes Chinese and English-language studies, potentially overlooking important research in other languages.

Clinical implications

This review, despite its limitations, provides insights into managing symptoms during chemotherapy. It highlights the need to address symptoms during and between chemotherapy sessions, based on priority symptoms from cross-sectional studies, laying the groundwork for future longitudinal study hypotheses. Fatigue presents a potential target for intervention and needs further study to understand its links with other symptoms.

Future directions

Future research should prioritize longitudinal studies to elucidate dynamic symptom relationships and identify causal mechanisms, thereby informing targeted clinical interventions. As an emerging research paradigm [ 54 ], establishing standardized reporting frameworks for the symptom network is critical to enhance methodological rigor and facilitate clinical translation. Concurrently, developing subgroup-specific networks stratified by cancer types, treatment cycles, chemotherapy purposes or other patient characteristics will enable robust evaluation of heterogeneity across classification criteria, optimizing precision in symptom management strategies.

Supplementary Information

Supplementary Material 1.

Author contributions

The study was conceptualized by XS, NW, KT, KW, ZT, JX, ZS, YJ and YZ. JX will conduct the first round of screening based on titles and abstracts, with results verified by ZS and YJ. Literature without abstracts or with uncertain relevance will proceed to the second round of screening, divided into two groups: XS and NW in Group 1, and KT and KW in Group 2. Disputed items will be submitted to YZ for arbitration. Information extracted by the four authors (XS, NW, KT, and ZT) will be categorized and imported into an Excel spreadsheet for comparative analysis, covering key study characteristics and findings. Discrepancies will be resolved through discussion among the four authors, with YZ acting as the final arbitrator for unresolved disagreements. Finally, XS will integrate the data to ensure completeness and consistency. All authors contributed to the interpretation of the data. The first draft of the manuscript was written by XS, and all authors were involved in critically revising the manuscript for important intellectual content. All authors approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

No ethical approval is required.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

Supplementary Materials

Supplementary Material 1.

Data Availability Statement

No datasets were generated or analysed during the current study.