Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast Cancer
ABSTRACT Background Understanding cancer symptom cluster through network analyses is a new approach in oncology, revealing interconnected and influential relationships among reported symptoms. We aimed to assess these relationships using network analysis in posttreatment breast cancer patients, focu...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70268 |
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| author | Louise Baussard Marie Ernst Anh Diep Guy Jerusalem Audrey Vanhaudenhuyse Nolwenn Marie Isabelle Bragard Marie‐Elisabeth Faymonville Olivia Gosseries Charlotte Grégoire |
| author_facet | Louise Baussard Marie Ernst Anh Diep Guy Jerusalem Audrey Vanhaudenhuyse Nolwenn Marie Isabelle Bragard Marie‐Elisabeth Faymonville Olivia Gosseries Charlotte Grégoire |
| author_sort | Louise Baussard |
| collection | DOAJ |
| description | ABSTRACT Background Understanding cancer symptom cluster through network analyses is a new approach in oncology, revealing interconnected and influential relationships among reported symptoms. We aimed to assess these relationships using network analysis in posttreatment breast cancer patients, focusing on the five dimensions of cancer‐related fatigue (CRF), and on other common difficulties encountered by oncological patients (i.e., pain, anxiety, depression, sleep difficulties, cognitive impairments, and emotion regulation and mental adaptation difficulties). Method This study involved a complementary analysis of data from two interventional studies. Participants completed questionnaires before and after the intervention, with baseline scores being used in this article. Partial correlation network analysis modeled the relationships between symptoms in five distinct networks, each of them including one specific dimension of CRF. The core symptom in each network was identified based on the highest centrality indices. Results Depression emerged as the core symptom in all networks, strongly associated with all fatigue dimensions (partial correlations ranging from 0.183 to 0.269) except mental fatigue. These findings indicate robust connections between symptoms, as variations in depression scores directly or indirectly influence fatigue and other symptoms within the cluster. Conclusion Our results support the multidimensional aspect of CRF, and its links with other common symptoms. To effectively reduce patient CRF, interventions should address not only fatigue but also the closely related symptoms from the cluster, such as depression, given its centrality in the cluster. Trial Registration: ClinicalTrials.gov (NCT03144154 and NCT04873661). Retrospectively registered on May 1, 2017 and April 29, 2021, respectively. |
| format | Article |
| id | doaj-art-61aa77c900e843ba97566c63e5ce96a8 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-61aa77c900e843ba97566c63e5ce96a82025-08-20T03:47:24ZengWileyCancer Medicine2045-76342024-10-011319n/an/a10.1002/cam4.70268Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast CancerLouise Baussard0Marie Ernst1Anh Diep2Guy Jerusalem3Audrey Vanhaudenhuyse4Nolwenn Marie5Isabelle Bragard6Marie‐Elisabeth Faymonville7Olivia Gosseries8Charlotte Grégoire9Epsylon Laboratory University Paul Valéry Montpellier 3 Nîmes FranceBiostatistics and Research Methods Centre University Hospital and University of Liège Liège BelgiumBiostatistics and Research Methods Centre University Hospital and University of Liège Liège BelgiumMedical Oncology Department University Hospital and University of Liège Liège BelgiumInterdisciplinary Algology Centre University Hospital of Liège Liège BelgiumSensation and Perception Research Group GIGA‐Consciousness, University of Liège Liège BelgiumResearch and Continuing Education Department, CRIG Research Center Haute Ecole Libre Mosane (HELMo) Liège BelgiumSensation and Perception Research Group GIGA‐Consciousness, University of Liège Liège BelgiumSensation and Perception Research Group GIGA‐Consciousness, University of Liège Liège BelgiumSensation and Perception Research Group GIGA‐Consciousness, University of Liège Liège BelgiumABSTRACT Background Understanding cancer symptom cluster through network analyses is a new approach in oncology, revealing interconnected and influential relationships among reported symptoms. We aimed to assess these relationships using network analysis in posttreatment breast cancer patients, focusing on the five dimensions of cancer‐related fatigue (CRF), and on other common difficulties encountered by oncological patients (i.e., pain, anxiety, depression, sleep difficulties, cognitive impairments, and emotion regulation and mental adaptation difficulties). Method This study involved a complementary analysis of data from two interventional studies. Participants completed questionnaires before and after the intervention, with baseline scores being used in this article. Partial correlation network analysis modeled the relationships between symptoms in five distinct networks, each of them including one specific dimension of CRF. The core symptom in each network was identified based on the highest centrality indices. Results Depression emerged as the core symptom in all networks, strongly associated with all fatigue dimensions (partial correlations ranging from 0.183 to 0.269) except mental fatigue. These findings indicate robust connections between symptoms, as variations in depression scores directly or indirectly influence fatigue and other symptoms within the cluster. Conclusion Our results support the multidimensional aspect of CRF, and its links with other common symptoms. To effectively reduce patient CRF, interventions should address not only fatigue but also the closely related symptoms from the cluster, such as depression, given its centrality in the cluster. Trial Registration: ClinicalTrials.gov (NCT03144154 and NCT04873661). Retrospectively registered on May 1, 2017 and April 29, 2021, respectively.https://doi.org/10.1002/cam4.70268breast cancercancer‐related fatiguenetwork analysispsychoneurological symptom cluster |
| spellingShingle | Louise Baussard Marie Ernst Anh Diep Guy Jerusalem Audrey Vanhaudenhuyse Nolwenn Marie Isabelle Bragard Marie‐Elisabeth Faymonville Olivia Gosseries Charlotte Grégoire Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast Cancer Cancer Medicine breast cancer cancer‐related fatigue network analysis psychoneurological symptom cluster |
| title | Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast Cancer |
| title_full | Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast Cancer |
| title_fullStr | Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast Cancer |
| title_full_unstemmed | Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast Cancer |
| title_short | Network Analyses Applied to the Dimensions of Cancer‐Related Fatigue in Women With Breast Cancer |
| title_sort | network analyses applied to the dimensions of cancer related fatigue in women with breast cancer |
| topic | breast cancer cancer‐related fatigue network analysis psychoneurological symptom cluster |
| url | https://doi.org/10.1002/cam4.70268 |
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