Impact of Multidisciplinary Team Care on Patient-Reported Outcomes in Patients with Lung Cancer: A Systematic Review
by
Aastha Srivastava
1,2,*, 
Elizabeth Daniel
1,
Vincent Lam
1,
Ru Karen Kwedza
1,2,
Shelley Rushton
2 and
Ling Li
1 1
Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW 2109, Australia
2
Cancer Institute NSW, Sydney, NSW 2065, Australia
*
Author to whom correspondence should be addressed.
Curr. Oncol. 2025, 32(12), 697; https://doi.org/10.3390/curroncol32120697
Submission received: 22 October 2025
/
Revised: 1 December 2025
/
Accepted: 4 December 2025
/
Published: 10 December 2025
(This article belongs to the Section Thoracic Oncology)
Simple Summary
Lung cancer is one of the deadliest cancers worldwide, and its care is complex, often requiring many healthcare professionals to work together. This systematic review examined how a multidiscipline team-based approach, where doctors, nurses, therapists, and other specialists collaborate on patient care, affects the lives of people with lung cancer. The researchers reviewed 11 international studies. These studies included over 10,000 patients with lung cancer. They found that patients cared for by teams generally experienced: better patient reported outcomes, such as physical health, less pain and fatigue, improved emotional well-being, and stronger social support. Patients also reported higher satisfaction with their care, feeling more informed and supported throughout treatment. The study identified factors that help or hinder this team-based care, such as early referrals, clear communication, and availability of resources. The findings show that coordinated, patient-focused care can improve quality of life for these patients. This highlights the importance of teamwork in healthcare. These insights can guide hospitals and healthcare systems to adopt more effective approaches, ultimately making cancer care more supportive, personalized, responsive and tailored to the needs of patients and their families.
Abstract
Background: Multidisciplinary team (MDT) care is now recognized as the most effective approach to managing lung cancer treatment. While MDTs aim to improve coordination, decision-making, and patient outcomes, their impact on patient-reported outcomes, particularly quality of life (QoL), remains unclear. Objective: This systematic review aimed to examine how the involvement of a multidisciplinary team (MDT) in the care of patients with lung cancer affects patient-reported outcomes and to investigate the enablers and barriers for implementing and running MDT care in lung cancer management. Methods: We systematically searched Medline, Embase, Cochrane, and Scopus (up to March 2024) to identify studies comparing QoL outcomes in patients with lung cancer managed with and without MDT care. The review was conducted and reported in accordance with the PRISMA 2020 guidelines. Risk of bias was assessed using the CASP tool, and findings were synthesized narratively. QoL outcomes were grouped into physical, functional, emotional, and social domains, and quantitative and qualitative data were synthesized narratively due to heterogeneity across studies. Results: Eleven studies met the inclusion criteria, comprising a total of 10,341 patients, with 3760 in MDT groups and 6581 in non-MDT groups. The methodological quality of the studies varied, with 10 papers rated as moderate to high quality. The findings suggest that MDT care may contribute positively to emotional support, and physical well-being. Better patient satisfaction and communication in MDT settings. Limitation: Heterogeneity and the lack of standardized PRO tools in outcome measures and study design limited comparability. Conclusions: MDT care may have a beneficial impact on certain aspects of quality of life in patients with lung cancer, particularly emotional and physical well-being. However, more robust and standardized research is needed to determine the full extent of its benefits on patient-reported outcomes.
1. Background
Lung cancer remains the leading cause of cancer-related deaths around the world. According to the World Health Organization (WHO), approximately 1.8 million deaths were reported in 2020 alone. This shows the urgent need for effective and coordinated treatment strategies [1]. The management of lung cancer is inherently complex. Therefore, it often requires quick decision-making and teamwork among different specialties to achieve the best patient outcomes.
In response to the complexities of cancer care, Multidisciplinary Teams (MDTs) have emerged as a central model of integrated, patient-centered care [2,3]. An MDT brings together healthcare professionals from various disciplines, like oncology, surgery, radiology, pathology, nursing, and allied health to work on personalized treatment plans for patients with cancer. This team-based approach ensures that all aspects of patient’s care are taken into account, promoting well-rounded and coordinated management [4,5]. Key characteristics of an effective MDT include teamwork, practical tasks, and flexibility, such as virtual MDT meetings initiated by the COVID-19 pandemic. This change suggests a new direction for cancer care [6].
Numerous studies and systematic reviews [7,8] have demonstrated that MDT involvement improves diagnostic accuracy, reduces time to treatment and results in better adherence to clinical guidelines [9]. As a result, MDTs are now seen as essential for high-quality cancer care and have quickly been adopted in health systems globally [10].
As the benefits of MDTs in improving survival rates and treatment planning are well-known, there is a growing need to understand their impact from the patient’s perspective. This is especially important for understanding patients’ quality of life and the overall care experience [6]. Patients with lung cancer often face significant physical, emotional, and psychosocial burdens, and understanding how multidisciplinary care affects these domains is critical for delivering truly patient-centered care [11,12]. Despite growing interest in patient-reported outcomes (PROs), there is limited evidence on how MDTs affect patients’ perspectives. This review will help us understand the impact of MDT on patient well-being, satisfaction, and quality of life. It will also guide the creation of more effective, patient-centered models of lung cancer care.
This systematic review aimed to
- To examine how the involvement of a multidisciplinary team (MDT) in the care of patients with lung cancer affects their overall quality of life, with a focus on understanding patients’ physical, emotional, social, and functional well-being through patient-reported outcomes (PROs).
- To investigate the enablers and barriers for implementing and running MDT care in lung cancer management. This included studying system-, process-, and patient-level factors that affect how MDTs function, communicate, and provide care.
By identifying these factors, the study will help us understand the challenges that might limit MDT effectiveness, and the key facilitators that promote successful, collaborative team-based care in lung cancer treatment settings.
2. Methods
2.1. Review Design
2.2. Definitions and Scope of Multidisciplinary Team (MDT)
Lung cancer MDT is a group of healthcare professionals from different disciplines who work together to review and coordinate diagnosis, treatment, and supportive care. Core members include a medical or respiratory oncologist, thoracic surgeon, radiation oncologist (if radiotherapy is needed), radiologist, pathologist, and a specialist nurse or care coordinator [14]. Additional essential disciplines may include nuclear medicine and palliative care, depending on case complexity. Optional members often include physiotherapists, dietitians, occupational therapists, pharmacists, psychologists, social workers, general practitioners, and clinical-trials coordinators [14].
2.3. Search Strategies and Databases
The search strategy was developed based on the PICO (population, intervention, comparison, outcome) framework [15] to align with the review’s objective of evaluating the impact of multidisciplinary team (MDT) care on quality of life in patients with lung cancer (Table 1).
We searched the following databases: Medline, Embase, Cochrane Library, and Scopus. The search terms used for Medline are presented in Table 1. The final search was conducted on 13 August 2024, and detailed search strings are presented in Appendix A Table A1.
2.4. Inclusion and Exclusion Criteria
Studies were included if they involved patients diagnosed with any type and stage of lung cancer and if they examined the role of a multidisciplinary team (MDT) in patient management (Table 2). Studies had to describe MDT care models, such as tumor boards, coordinated care plans, or integrated approaches, and include a comparative group. Included studies were required to report outcomes related to quality of life, covering physical, emotional, social, and functional well-being, or patient-reported outcomes. Studies with appropriate designs, like randomized controlled trials, cohort studies, cross-sectional studies, and before–after studies, were considered. Studies that did not involve patients with lung cancer, lacked MDT care or a comparison group, did not report relevant outcomes, or used inappropriate designs, such as case reports, editorials, reviews, or non-peer-reviewed sources like conference abstracts, were excluded.
2.5. Study Selection
Three reviewers (A.S., I.L., E.D.) double-screened the titles and abstracts, and the full text screening was conducted. Any disagreement was first discussed between the reviewers. In the absence of a consensus, opinion was sought from a third reviewer (L.L.) for resolution.
2.6. Data Extraction
Two reviewers (A.S. and E.D.) conducted data extraction using a standardized form that included categories on (1) study characteristics, including study setting and design, (2) MDT definition and implementation, and (3) outcome of the intervention. The two reviewers, A.S. and E.D., met regularly to discuss and resolve any discrepancies or disagreements in data interpretation and data extraction of the studies. A third reviewer (L.L.) was consulted on the process and in case of disagreements. A chronicle synthesis of the findings was then conducted.
2.7. Critical Appraisal
The quality of included studies was assessed independently by two reviewers (A.S. and E.D.) using appropriate critical appraisal checklists [16]. Tools were selected based on study design, including checklists for RCTs, cohort studies, and pre–post intervention studies. Detailed assessments are presented in Appendix A Table A2.
2.8. Data Synthesis
A narrative synthesis of findings was conducted due to the heterogeneity in study design, outcome measures, and MDT models. Key findings were grouped according to dimensions of quality of life and patient-reported outcomes. Studies were grouped by study design, lung cancer stage, type of treatment, and PRO instrument. Within each comparison, the better-performing group was identified. Where available, published MCID thresholds were used to interpret clinical significance; for instruments without MCIDs, statistically significant improvements were interpreted as meaningful within the study context. Where applicable, barriers and enablers of MDT implementation were categorized using the Consolidated Framework for Implementation Research (CFIR) [17].
3. Results
A total of 6071 records were identified through database searching. After screening 4022 title and abstract, the full texts of 23 articles were obtained and assessed for final data extraction. Following eligibility screening, 9 articles met the eligibility criteria and an additional 3 articles were identified from other sources, bringing the total to 11 articles included in the review. We conducted a narrative synthesis of the findings (Table 3). Details of the studies screened and included at each stage are presented in the PRISMA flowchart in Figure 1. This review focuses on the outcomes of MDT versus non-MDT care and reports how these effects changed over time. The studies used validated PRO tools, but as the follow-up times and MDT structures differed, the results varied. MDTs worked best for symptoms like fatigue, pain, and anxiety. Patient satisfaction, as reported in one study, was very high. Several system, process, and patient factors helped or hindered MDT care. These factors explain why MDT outcomes differed across studies.
3.1. Study Characteristics
A narrative synthesis of the findings was conducted (Table 3). This review included data from eleven international studies investigating the impact of MDT care on patients with lung cancer, with a total of 10,341 patients: 3760 in MDT groups and 6581 in non-MDT groups. The proportion of male patients ranged from 33% to 70%, and the median or mean age spanned from 63 to 76 years across studies. Most studies focused on non-small cell lung cancer (NSCLC), with stages I to IV, and some emphasizing advanced or palliative care stages (IIIB–IV).
Study settings varied, including comprehensive cancer centers, community-based systems, and universities, across the USA (n = 5), Australia (2), Japan (1), China (n = 1), Denmark (n = 1), and Taiwan (n = 1). Most of the studies employed a randomized clinical trial (n = 6) and cohort study design (n = 3), followed by pre–post study designs (n = 2).
The composition and key characteristics of MDTs are summarized in Table 3. Non-randomized studies were at risk of confounding due to differences in stage mix, treatment intensity, and hospital volume. In pre–post studies (Borneman (2008) [18]; Smeltzer (2018) [27]), stage mix and treatment intensity were partially compared at baseline, with Smeltzer (2018) [27] adjusting for baseline PRO scores and surgical method using ANCOVA. Cohort studies (Raz (2016) [24]; Gregersen (2024) [23]; Shao (2023) [25]) either reported baseline similarities without formal adjustment Raz (2016) [24] or accounted for confounders via frequency matching, propensity score matching, or multivariable regression (Gregersen (2024) [23]; Shao (2023) [25]). RCTs and controlled clinical trials (Chen (2023) [19]; Edbrooke (2019) [20]; Ferrell (2015) [21]; Friedman (2016) [22]; Schofields (2013) [26]; Wang (2014) [28]) addressed key baseline differences through randomization or ANCOVA.
Follow-up times for PRO assessments ranged from 1 month to 12 months, and lack of standard 3- or 12-month follow-ups introduced heterogeneity (Borneman (2008) [18]; Chen (2023) [19]; Edbrooke (2019) [20]; Wang (2014) [28]). All studies used standardized, validated PRO instruments, though cultural adaptation was inconsistently reported.
3.2. Intervention Description
Across the reviewed studies, MDT compositions varied, but they consistently included core medical professionals [29] such as respiratory medicine, thoracic surgery, medical oncology, radiation oncology, pathology, radiology, a nurse specialist, and palliative care. Other team members [29] included nuclear medicine, social work, physiotherapy, psychiatry or psychology, dietetics, and occupational therapy. These roles were often included to support holistic care. Most teams met weekly, though some, like Chen (2023) [19], met monthly. Others, such as Gregersen (2024) [23], provided multidisciplinary input without a formal meeting structure. A summary of MDT composition is presented in Table 4.
3.3. Patient Reported Measures—Quality of Life
MDT care appears most effective in improving physical, functional, and emotional domains, with the greatest benefits observed for fatigue, pain, physical functioning, and anxiety/depression. Benefits for social well-being and overall QoL are more variable, reflecting heterogeneity in patient populations, team composition, and care context. These findings suggest that MDT care can enhance multiple aspects of QoL (Table 5). Detailed information for each study is provided in Appendix A, Table A3.
Physical well-being outcomes were reported better in MDT groups in ten studies (Table 5). Patients in MDT groups experienced less fatigue, pain, and dyspnea. They also showed improved mobility and nutritional status. For example, Shao (2023) [25] found improvements in fatigue and 6 min walk distance. Chen (2023) [19] reported reduced pain levels and improved nutritional status. However, some studies such as Gregersen (2024) [23] did not show meaningful differences in fatigue, insomnia, or appetite loss between MDT and non-MDT groups. In fact, symptoms such as dyspnea and diarrhea slightly worsened in the MDT group, though these differences were not statistically significant.
Functional well-being was generally improved with MDT care. Studies by Ferrell (2015) [21], Raz (2016) [24], and Schofields (2013) [26] showed significantly higher scores in functional domains for MDT patients. Notably, Raz (2016) [24] interpreted changes using minimal clinically important difference (MCID) thresholds, demonstrating that observed improvements were clinically meaningful. Edbrooke (2019) [20] also found modest but statistically significant improvements in physical and role functioning. However, Gregersen 2024 [23], again, showed no significant benefit in functional scores.
Emotional well-being improved with MDT care in several studies. Chen (2023) [19], Shao 2023 [25], and Raz 2016 [24] found lower anxiety and depression levels and higher emotional functioning scores in MDT patients. However, Edbrooke (2019) [20] and Gregersen (2024) [23] found no significant differences. An aspect of Gregersen’s study, “burden of illness,” was worse in the MDT group (p = 0.04).
Social well-being was generally better in MDT groups, with higher social or family support and fewer financial difficulties. Raz (2016) [24] and Ferrell (2015) [21] showed the most notable improvements. Still, Gregersen (2024) [23] reported no meaningful differences, and Smeltzer (2018) [27] observed slightly lower social well-being scores in the MDT group, though still statistically significant.
3.4. Overall Patient Satisfaction
Only one study, Friedman 2016 [22], examined patient satisfaction with MDT care. Most patients reported very high satisfaction across several areas. Respect for patient questions was rated as “very good” by 82.2% of respondents, with 17.8% rating it as “good.” Similarly, 82.9% rated the clarity of their condition and treatment explanations as “very good,” while 15.4% rated it as “good.” Satisfaction with the time spent during consultations was rated “very good” by 69.0% and “good” by 29.3% of patients. Regarding the usefulness of written recommendations, 72.0% of patients rated it “very good” and 26.0% “good.” Finally, when asked about their likelihood of recommending the service, 80.9% indicated “very good” and 18.3% responded “good”.
3.5. Barriers and Enablers
Across the system, process, and patient levels, several enablers and barriers influenced the implementation of MDT care (Figure 2). Enablers like early referrals, structured assessments, and co-located clinics support timely, coordinated, and patient-centered care. These factors improved overall well-being, including better symptom control, emotional support, social engagement, and functional status. In contrast, barriers like limited resources, fragmented care, and psychosocial issues reduce care effectiveness. They created delays, unmet needs, and a lower quality of life. The presence or absence of these factors significantly affected patient outcomes in MDT settings. Detailed study-level summaries are provided in Appendix A Table A4, Table A5, Table A6 and Table A7.
At the system level, enablers include early referral to palliative care and the use of standardized tools. Borneman (2008) [18] structured models like the E-Warm model. Chen (2023) [19] structured digital integration such as telehealth, whereas Raz (2016) [24] structured e-referrals. Financial benefits, including reduced redundant testing and improved cost-efficiency, also support system-level change (Friedman 2016) [22]. However, barriers persisted, such as limited healthcare and supportive care resources (Chen 2023 [19] and Ferrell 2015 [21]), a lack of palliative care specialists (Raz 2016 [24]), and infrastructural demands like significant scheduling changes for co-located MDT clinics (Smeltzer 2018 [27]).
At the process level, enablers include interdisciplinary case conferences, patient education interventions (Borneman, 2008 [18]), structured MDT meetings (Raz 2016 [24]), and the involvement of nurse navigators (Friedman 2016 and Smeltzer 2018 [22,27]). Multidisciplinary collaboration and tailored educational approaches also enhanced care processes as per Chen (2023) [19] and Ferrell (2015) [21]. Barriers, however included clinicians’ concerns over autonomy as highlighted in a study by Smeltzer (2018) [27]. Raz (2016) [24] showcased the lack of structured MDT meetings [24] and challenges in scheduling co-located services can hinder seamless integration.
At the patient level, education interventions and regular assessments are key enablers mentioned by Borneman (2008) [18] and Chen (2023) [19]. Barriers included psychosocial issues, cultural norms, and limited follow-up (Borneman 2008 [18] and Chen 2023 [19]) Tailored education faced challenges in late-stage care (Ferrell 2015 [21]) and fragmented care persisted in serial referral systems (Smeltzer 2018 [27]). Overall, these findings illustrated that while structured, multidisciplinary approaches can improve quality of life, implementing them needs to consider resource limitations, cultural factors, and structural fragmentation.
4. Discussion
In the 11 studies reviewed here, MDT care consistently showed improvements in physical functioning, emotional well-being, symptom burden, and overall QoL, highlighting the value of coordinated, multidisciplinary input for complex lung cancer care. These findings offer several implications for policy and service design. However, these benefits depend on how MDT interventions are structured and how often they occur. They are also affected by factors related to the healthcare system, the process, and the patients themselves. First, policymakers should prioritize investment in MDT models that embed routine symptom assessment, psychosocial support, and rehabilitation pathways, as these domains showed the strongest benefits. Second, effective MDT services require protected time, appropriate staffing, and clear clinical leadership to maintain continuity, communication, and timely decision-making. Lastly, adapting MDT care to resource-limited settings may require flexible strategies such as streamlined team composition, tele-MDT meetings, targeted training, and integration of community or primary care clinicians to ensure equitable access to multidisciplinary input even where specialist resources are constrained.
4.1. Multidisciplinary Team Composition and Process Characteristics
The variability in MDT composition and meeting formats observed in the included studies mirrors findings from the previous literature, such as Pillay (2016) [30]. This study highlighted that consistent team membership, strong leadership, and clear roles are vital for effective teamwork. In our review, we found that more structured and frequent MDT engagements (e.g., weekly formal meetings) were generally associated with better outcomes. This aligns with the findings of Prades (2015) [31], which emphasized the benefit of regular, protocol-driven interactions among core oncology professionals.
4.2. Physical and Functional Well-Being
Fatigue, dyspnea, pain, 6 min walk distance, and lung cancer physical subscale (e.g., Raz (2016) [24], Shao (2023) [25], Edbrooke (2019) [20]) show significant improvements in MDT patients (p < 0.05). Gregersen (2024) [23] shows mostly non-significant differences for fatigue, pain, insomnia, diarrhea, constipation, and mobility (p > 0.05), indicating minimal impact in these areas. Raz (2016) [24] and Ferrell (2015) [21] reported an association between MDT care and improved physical and functional well-being, primarily through better symptom control, streamlined care pathways, and proactive intervention. This correlates with the findings of Kochovska (2020) [32], who reported that MDTs facilitate earlier palliative care referral, which is associated with better pain management and symptom relief. Moreover, MDTs that integrate digital tools, as seen in Raz (2016) [24], echo the findings of Larson (2018) [33], who found that e-referrals and telehealth services reduce time to treatment and improve functional independence by minimizing delays in service delivery.
Conversely, where these system-level innovations were absent or inconsistently applied, such as in Gregersen (2024) [23] and Smeltzer (2018) [27], improvements in functional domains were negligible. This supports the assertion by Taplin (2015) [34] that MDT effectiveness is highly dependent on operational infrastructure, including data sharing systems and coordinated workflows.
4.3. Emotional Well-Being and Psychosocial Integration
Early emotional well-being measures Ferrell (2015) [21] show strong improvement (p < 0.001). Meanwhile, Gregersen (2024) [23] shows mostly non-significant changes for emotional functioning and future worries (p > 0.05).
The consistent emotional benefits observed in studies such as those by Chen (2023) [19] and Raz (2016) [24] underline the value of integrating psychological support within MDTs. This aligns with results from Jacobsen and Wagner (2012) [35], who found that oncology teams incorporating mental health specialists achieved better patient-reported outcomes on anxiety and depression scales. These findings reinforce the call for psychosocial oncology to be a standard element in MDTs, as advocated by the International Psycho-Oncology Society (IPOS) and endorsed in the 2018 ASCO guidelines [36].
However, studies like those by Smeltzer (2018) [27] and Borneman (2008) [18] caution that inadequate staffing or the lack of formal psychosocial protocols may negatively impact the effect of MDT care on emotional betterment. This aligns with the concept of “structural competency”—described by Metzl and Hansen (2014) [37]—which suggests that if patients’ mental health needs are not intentionally addressed, their emotional outcomes are likely to remain poor.
4.4. Social and Spiritual Well-Being
Gregersen (2024) [23] and Smeltzer (2018) [27] show no or negative effects on social functioning (p > 0.05). However, the findings regarding improved social and spiritual well-being in studies like those by Raz (2016) [24] and Ferrell (2015) [21] highlight the broader, holistic benefits of MDT care. These results are consistent with the work of Levit (2013) [38], who argue for the integration of supportive and spiritual care into oncology as a means of improving whole-person care. Tailored education and the inclusion of social workers and spiritual care professionals were important enablers in this context.
In contrast, when these roles were not clearly defined or underutilized, as seen in Borneman (2008) [18] or Gregersen (2024) [23], the social and spiritual domains of QoL either stagnated or declined. These outcomes highlight the need for equity-driven implementation of MDTs that address not just treatment needs but also the relational and existential challenges faced by patients with lung cancer.
4.5. Patient Satisfaction and Care Experience
Studies such as Friedman 2016 [22] mirror findings from O’Daniel (2008) [39], who demonstrated that patients report greater trust and clarity when decisions are made by a team rather than a single physician. This aligns with frameworks like the Picker Principles of Patient-Centered Care [40], which stressed the importance of communication, coordination, and emotional support as core to a positive care experience.
Nevertheless, satisfaction gains are dependent upon the visibility and coherence of MDT operations. Studies with poorly implemented MDTs (e.g., Gregersen 2024 [23]) often failed to show meaningful improvements, possibly due to patients not perceiving the benefits of multidisciplinary involvement if coordination is not evident in their care journey.
4.6. Other Barriers
The influence of barriers, such as limited staffing, resource constraints, and cultural barriers to care engagement, cannot be overstated. These issues were echoed in the studies by Borneman (2008) [18] and Smeltzer (2018) [27], and are consistent with global evidence, such as the findings by Andrulis (2007) [41], who emphasizes the need for cultural tailoring and health literacy-sensitive approaches to maximize MDT effectiveness.
4.7. Implications for Practice and Future Research
MDTs should adopt a more patient centric approach by ensuring that patients know who is involved in their care. This will encourage engagement, satisfaction, and trust. MDTs must be culturally responsive and fit the needs of different populations. Training in health literacy and cultural humility is crucial to reducing disparities and ensuring that everyone receives inclusive care. MDTs should include mental health professionals, social workers, and spiritual care providers to address the full range of patient needs. Additionally, using digital health tools like telehealth services, electronic referrals, and data systems can improve access to MDT care.
Evidence from the included studies indicates that the benefits of MDT care differ by patient subgroup. Early-stage patients gain most from MDT involvement, as it provides precise diagnostic assessment, staging, and treatment planning. In contrast, advanced-stage patients primarily gain from early supportive and palliative interventions. Meanwhile, patients with complex symptoms or psychosocial needs experience enhanced holistic care through the coordinated input of allied health and support services (Borneman 2008 [18]; Chen 2023 [19]; Edbrooke 2019 [20]; Ferrell 2015 [21]; Friedman 2016 [22]; Gregersen 2024 [23]). These findings highlight the importance of tailoring MDT involvement according to disease stage, symptom burden, and psychosocial complexity, providing practical guidance for clinicians in prioritizing MDT resources.
Future studies should use a long-term study design to evaluate the ongoing impact of MDT care on quality of life over time. There is also a need to assess how MDTs perform across diverse patient groups, including those who are underrepresented, culturally diverse, and socioeconomically disadvantaged.
5. Strength and Limitations
This review is the first to look at how multidisciplinary team (MDT) care affects patient-reported quality of life (QoL) in people with lung cancer. It also explores what helps and what hinders the effective use of MDTs. key strength is its structured methodology for the study design. It follows PRISMA standards and is supported by a thorough search strategy across major databases. The study uses established quality appraisal tools, CASP, to carefully evaluate the reliability and risk of bias in the included studies.
This review identified substantial variations among the included studies, with variations in study design, outcome measures, and the definition and implementation of MDT care. However, the heterogeneity across the studies hinders meaningful direct comparisons and precludes the possibility of conducting a meta-analysis. A key limitation of this review is the lack of standardized and validated tools for measuring quality of life in the included studies. Many studies relied on unidimensional or non-validated instruments, reducing the reliability and depth of the findings. Only peer-reviewed original research articles published in English were included to ensure accurate data extraction and interpretation, as language barriers could lead to misinterpretation of study methods, results, or outcomes. We acknowledge that this may introduce a potential language bias, which is a limitation of the review. However, studies from six different countries/regions are included in our review, including those where English is not the official language. These would help reduce bias from language and culture in our review. A further limitation is that few included studies adjusted for important demographic and social variables (e.g., age, employment, religion), reducing the ability to account for potential confounding.
To further explore potential reasons for non-significant findings, several factors—such as MDT maturity, resource limitations, and cultural context—may have influenced outcomes; however, these were not systematically examined or reported in the included studies. Future research investigating these factors could enhance our understanding of the variability in results. Where baseline characteristics were reported, we provide explanations accordingly, but firm conclusions cannot be drawn due to the lack of consistent reporting across studies. Additionally, the possibility of reverse causality contributing to non-significant findings is considered here for interpretative purposes, which remains within the scope of this review.
6. Conclusions
This review shows that MDT care benefits patients with lung cancer beyond survival. It improves physical, functional, emotional, and social well-being. Thus, MDTs support holistic, person-centered care and address complex patient needs across the disease trajectory. However, different Qo and measurement tools were used across included studies, which makes comparisons difficult and limits the generalizability. Therefore, standardized and validated instruments are beneficial for future research. Consistent reporting frameworks would also improve the quality of evidence. In the future, investment in strong MDT structures, combined with standardized patient-reported outcomes, will be essential for optimizing lung cancer care.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/curroncol32120697/s1: Table S1: PRISMA_2020_checklist_Impact of MDT on PRO in Lung Cancer Patients SR [13].
Author Contributions
Conceptualization, A.S. and L.L.; methodology, A.S.; formal analysis, A.S. and E.D.; writing—original draft preparation, A.S.; writing—review and editing, A.S., E.D., V.L., R.K.K., S.R. and L.L.; visualization, A.S.; supervision, L.L.; project administration, A.S. and L.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
As this is a systematic review, no new data was created or analyzed. All data used in this study were derived from previously published studies, and relevant references are cited within the article.
Acknowledgments
We would like to thank Jeremy from the Macquarie University Library for his valuable assistance in developing the search strategies for this review. We also acknowledge Isabella Lynch for her support with screening the titles and abstracts.
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix A
Table A1.
Search strings.
| Database | Search String |
|---|---|
| Medline |
|
| Embase |
|
| Cochrane | respiratory tract neoplasms or exp lung neoplasms or exp pleural neoplasms or tracheal neoplasms in Title Abstract Keyword OR (lung or pleural or respiratory tract) adj2 (cancer * or neoplasm * or tumor * or tumour * or malignanc * or metast * or carcinoma *) in Title Abstract Keyword OR (lung cancer * or lung neoplasm * or non small cell lung carcinoma * or nonsmall cell carcinoma * or NSCLC or SCLC or small cell lung cancer *) in Title Abstract Keyword AND Patient Care Team in Title Abstract Keyword OR (multidisciplinary team * or MDT * or multidisciplinary care team * or multidisciplinary tumor board * or MTBs or multidisciplinary clinic * or multidisciplinary team meeting *) |
| Scopus | (TITLE-ABS-KEY (“respiratory tract neoplasms”) OR TITLE-ABS-KEY (“lung neoplasms”) OR TITLE-ABS-KEY (“pleural neoplasms”) OR TITLE-ABS-KEY (“tracheal neoplasms”) OR TITLE-ABS-KEY ((lung OR pleural OR “respiratory tract”) W/2 (cancer * OR neoplasm * OR tumor * OR tumour * OR malignanc * OR metast * OR carcinoma *)) OR TITLE-ABS-KEY (“lung cancer *”) OR TITLE-ABS-KEY (“lung neoplasm *”) OR TITLE-ABS-KEY (“non small cell lung carcinoma *”) OR TITLE-ABS-KEY (“nonsmall cell carcinoma *”) OR TITLE-ABS-KEY (nsclc) OR TITLE-ABS-KEY (sclc) OR TITLE-ABS-KEY (“small cell lung cancer *”) AND TITLE-ABS-KEY (“patient care team”) OR TITLE-ABS-KEY (“multidisciplinary team *”) OR TITLE-ABS-KEY (mdt *) OR TITLE-ABS-KEY (“multidisciplinary care team *”) OR TITLE-ABS-KEY (“multidisciplinary tumor board *”) OR TITLE-ABS-KEY (mtbs) OR TITLE-ABS-KEY (“multidisciplinary clinic *”) OR TITLE-ABS-KEY (“multidisciplinary team meeting *”)) AND (LIMIT-TO (LANGUAGE, “english”)) |
The asterisk * () is a truncation symbol used in database searches to capture multiple word endings. For example, “cancer *” retrieves “cancer” and “cancers,” “tumor *” retrieves “tumor” and “tumors,” and “malignanc *” retrieves “malignancy” and “malignancies.” This ensures comprehensive retrieval of relevant studies.
Table A2.
CASP result.
| Author | Positive/Methodologically Sound | Negative/Relatively Poor Methodology | Unknowns |
|---|---|---|---|
| Borneman (2008) [18] |
|
|
|
| Chen (2023) [19] |
|
|
|
| Edbrooke (2019) [20] |
|
|
|
| Ferrell (2015) [21] |
|
|
|
| Friedman (2018) [22] |
|
|
|
| Gregersen (2024) [23] |
|
|
|
| Raz (2016) [24] |
|
|
|
| Shao (2023) [25] |
|
|
|
| Schofields (2013) [26] |
|
|
|
| Smeltzer (2018) [27] |
|
|
|
| Wang (2014) [28] |
|
|
|
Table A3.
Patient-Reported Quality of Life: Physical, Functional, Emotional, Social, and Overall Well-Being.
Table A3.
Patient-Reported Quality of Life: Physical, Functional, Emotional, Social, and Overall Well-Being.
| Author | Scale | Non-MDT | MDT | p Value | ||
|---|---|---|---|---|---|---|
| Physical Well-Being (N = 10) | ||||||
| Borneman (2008) [18] | Physical QOL | 5.8 (SD = 2) | 5.5 (SD = 1.5) | <0.003 | ||
| Chen (2023) [19] | Nutritional Status No (0–1)—PG-SGA | 30 (21.43%) | 53 (37.86%) | 0.007 | ||
| Nutritional Status Mild (2–8)—PG—SGA | 78 (55.71%) | 67 (47.86%) | 0.007 | |||
| Nutritional Status Severe (≥9)—PG—SGA | 32 (22.86%) | 20 (14.29%) | 0.007 | |||
| No Pain (0)—NRS | 76 (54.29%) | 53 (37.86%) | 0.003 | |||
| Mild Pain (1–3)—NRS | 52 (37.14%) | 67 (47.86%) | 0.003 | |||
| Moderate Pain (4–6)—NRS | 12 (8.57%) | 20 (14.29%) | 0.003 | |||
| Severe Pain (7–10)—NRS | 0 | 0 | 0.003 | |||
| Edbrooke (2019) [20] | Fatigue—EORTC QLQ-C30 | 39.7 | 30.9 | 0.01 | ||
| Dyspnoea—EORTC QLQ-C30 | 29.7 | 20.5 | 0.03 | |||
| Ferrell (2015) [21] | Physical well-being (Early) | 19.5 ± 6.2 | 23.3 ± 3.3 | 0.004 | ||
| Physical well-being (Late) | 21.2 ± 6.2 | 22.2 ± 4.9 | 0.004 | |||
| Physical well-being (Total) | 20.3 ± 6.2 | 22.8 ± 4.2 | 0.004 | |||
| Gregersen (2024) [23] | Fatigue—EORTC QLQ-C30 | +26.8 ± 31.3 | +30.0 ± 33.7 | 0.50 | ||
| Insomnia—EORTC QLQ-C30 | −1.77 ± 38.0 | −10.2 ± 42.1 | 0.89 | |||
| Pain—EORTC QLQ-C30 | +2.27 ± 33.4 | −1.74 ± 32.5 | 0.63 | |||
| Dyspnea—EORTC QLQ-C30 | −6.82 ± 32.4 | +0.75 ± 32.3 | 0.06 | |||
| Appetite Loss—EORTC QLQ-C30 | −2.78 ± 46.6 | +0.75 ± 47.1 | 0.40 | |||
| Diarrhea—EORTC QLQ-C30 | −5.05 ± 35.1 | +2.24 ± 27.2 | 0.06 | |||
| Constipation—EORTC QLQ-C30 | −2.27 ± 25.1 | −1.99 ± 28.8 | 0.82 | |||
| Nausea/Vomiting—EORTC QLQ-C30 | −1.89 ± 25.9 | +2.86 ± 27.0 | 0.40 | |||
| Mobility—EORTC QLQ-ELD14 | +3.11 ± 22.7 | +1.87 ± 20.1 | 0.69 | |||
| Joint Stiffness—EORTC QLQ-ELD14 | +5.30 ± 31.9 | +6.97 ± 32.7 | 0.69 | |||
| Raz (2016) [24] | Lung Cancer Subscale | 23.6 ± 4.2 | 29.4 ± 2.1 | <0.001 | ||
| Physical Well-being | 22.2 ± 4.1 | 25.2 ± 2.3 | <0.001 | |||
| Shao (2023) [25] | 6-Minute Walk Distance (6MWD, m) | 352 ± 84 | 393 ± 92 | 0.02 | ||
| Dyspnea (mMRC) | 2.1 ± 1.0 | 1.6 ± 0.9 | <0.01 | |||
| Fatigue (FAS) | 22.5 ± 5.3 | 18.9 ± 5.1 | <0.01 | |||
| Schofields (2013) [26] | Physical Well-Being (FACT-L) | 18.6 ± 5.5 | 21.2 ± 4.3 | 0.005 | ||
| Fatigue (Distress thermometer) | 65.6% reported | 45.8% reported | 0.02 | |||
| Pain (Distress thermometer) | 49.2% reported | 28.8% reported | 0.02 | |||
| Smeltzer (2018) [27] | Physical Well-Being | 24.9 ± 7.0 | 24.9 ± 7.6 | <0.001 | ||
| Wang (2014) [28] | Fever | 23.97% | 25.46% | - | ||
| Dyspnea and Respiratory Abnormalities | 13.53% | 13.23% | - | |||
| Chest Pain | 10.20% | 7.94% | - | |||
| Abdominal Pain | 9.39% | 9.99% | - | |||
| Dizziness and Fainting | 7.50% | 7.17% | - | |||
| Nausea and Vomiting | 7.00% | 7.00% | - | |||
| Malaise and Fatigue | 4.24% | 5.17% | - | |||
| Hemoptysis | 4.21% | 3.29% | - | |||
| Headache | 2.66% | 2.00% | - | |||
| Cough | 2.24% | 1.06% | - | |||
| Sleep disturbances | 2.17% | 1.70% | - | |||
| Functional Well-Being (N = 8) | ||||||
| Borneman (2008) [18] | Functional Scale | not reported | 21.1 (SD = 7.1) | |||
| Edbrooke (2019) [20] | Physical Functioning—EORTC QLQ-C30 | 64.5 | 70.8 | 0.04 | ||
| Role Functioning—EORTC QLQ-C30 | 51.0 | 64.6 | 0.04 | |||
| Ferrell (2015) [21] | Functional Well-Being (Early) | 14.6 ± 4.9 | 19.5 ± 3.5 | <0.001 | ||
| Functional Well-Being (Late) | 17.5 ± 5.4 | 16.6 ± 6.0 | <0.001 | |||
| Functional Well-Being (Total) | 16.1 ± 5.4 | 18.0 ± 5.1 | <0.001 | |||
| Gregersen (2024) [23] | Role Functioning—EORTC QLQ-C30 | −6.94 ± 49.0 | −14.4 ± 46.1 | 0.21 | ||
| Physical Functioning—EORTC QLQ-C30 | −9.82 ± 25.5 | −9.20 ± 26.2 | 0.89 | |||
| Maintaining Purpose—EORTC QLQ-ELD14 | +1.26 ± 29.8 | +1.62 ± 29.1 | 0.75 | |||
| Raz (2016) [24] | Functional Well-Being | 14.4 ± 5.1 | 22.6 ± 2.6 | <0.001 | ||
| Shao (2023) [25] | EORTC QLQ-C30 Physical Function | 61.2 ± 17.6 | 71.5 ± 15.9 | <0.01 | ||
| Schofields (2013) [26] | Functional Well-Being (FACT-L) | 13.3 ± 6.3 | 16.4 ± 6.0 | 0.01 | ||
| Smeltzer (2018) [27] | Functional Well-Being | 22.0 ± 7 | 22.1 ± 7.3 | <0.001 | ||
| Emotional Well-Being (N = 9) | ||||||
| Borneman (2008) [18] | Psychological QOL | 4.5 (SD = 2.2) | 4.7 (SD = 1.4) | |||
| Chen (2023) [19] | Anxiety Subscale—(HADS-A) | 2.66 ± 2.86 | 1.45 ± 2.86 | <0.001 | ||
| Depression Subscale—(HADS-D) | 3.54 ± 4.61 | 1.5 ± 2.05 | <0.001 | |||
| Depression (0–4)—No | 111 (79.29%) | 127 (90.71%) | 0.003 | |||
| Depression—Mild (5–9) | 5 (3.57%) | 12 (8.57%) | 0.003 | |||
| Depression—Moderate (10–14) | 5 (3.57%) | 1 (0.71%) | 0.003 | |||
| Depression—Moderately severe (15–19) | 5 (3.57%) | 1 (0.71%) | 0.003 | |||
| Edbrooke (2019) [20] | Emotional Functioning—EORTC QLQ-C30 | 74.1 | 76.4 | 0.42 | ||
| Ferrell (2015) [21] | Emotional Well-Being (Early) | 16.7 ± 4.8 | 20.8 ± 3.5 | <0.001 | ||
| Emotional Well-Being (Late) | 17.6 ± 4.9 | 19.0 ± 3.9 | <0.001 | |||
| Emotional Well-Being (Total) | 17.6 ± 4.9 | 19.9 ± 3.8 | <0.001 | |||
| Gregersen (2024) [23] | Emotional Functioning—EORTC QLQ-C30 | +5.67 ± 18.3 | +8.25 ± 22.6 | 0.26 | ||
| Future Worries—EORTC QLQ-ELD14 | −10.9 ± 29.1 | −12.3 ± 32.5 | 0.57 | |||
| Burden of Illness—EORTC QLQ-ELD14 | −3.66 ± 35.5 | +6.84 ± 35.4 | 0.04 | |||
| Raz (2016) [24] | Emotional Well-Being (FACT-L) | 19.4 ± 3.6 | 23.2 ± 1.8 | <0.001 | ||
| Psychological Distress | 4.0 ± 2.3 | 1.0 ± 1.4 | <0.001 | |||
| Shao (2023) [25] | Anxiety (HADS-A) | 9.3 ± 3.7 | 6.7 ± 3.2 | <0.01 | ||
| 8.5 ± 3.9 | 6.2 ± 3.5 | <0.01 | ||||
| Schofields (2013) [26] | Distress Score (NCCN DT) | 4.5 ± 2.4 | 3.1 ± 2.6 | 0.01 | ||
| Anxiety (HADS) | 7.7 ± 4.6 | 5.5 ± 3.7 | 0.01 | |||
| Depression (HADS) | 5.7 ± 3.7 | 4.2 ± 3.6 | 0.04 | |||
| Smeltzer (2018) [27] | Emotional well-being | 24.0 ± 6 | 24.2 ± 6.0 | <0.001 | ||
| Social Well-Being (N = 7) | ||||||
| Borneman (2008) [18] | Social QOL | 4.9 (SD = 2.3) | 5 (SD = 1.9) | - | ||
| Spiritual QOL | 6.3 (Sd = 2.1) | 5.8 (Sd = 2.2) | - | |||
| Edbrooke (2019) [20] | Social Functioning—EORTC QLQ-C30 | 78.1 | 82.4 | 0.25 | ||
| Ferrell (2015) [21] | Social/Family Well-Being (Early) | 20.4 ± 6.9 | 24.5 ± 5.0 | <0.001 | ||
| Social/Family Well-Being (Late) | 24.1 ± 4.3 | 22.7 ± 6.5 | <0.001 | |||
| Social/Family Well-Being (Total) | 22.3 ± 6.0 | 23.6 ± 5.8, | <0.001 | |||
| Gregersen (2024) [23] | Social Functioning—EORTC QLQ-C30 | +2.75 ± 24.5, p = 0.25 | −4.48 ± 28.9, p = 0.24 | 0.24 | ||
| Family Support—EORTC QLQ-C30 | 0.51 ± 41.0 | +1.24 ± 36.2 | 0.44 | |||
| Financial Difficulties—EORTC QLQ-C30 | +1.27 ± 11.3 | +0.75 ± 7.61 | 0.62 | |||
| Raz (2016) [24] | Social/Family Well-Being | 19.1 ± 9.1 | 25.6 ± 3.6 | <0.001 | ||
| FACIT-Spiritual Well-Being | 32.7 ± 9.5 | 43.1 ± 6.8 | <0.001 | |||
| Schofields (2013) [26] | Social Well-Being (FACT-L) | 17.0 ± 6.0 | 19.3 ± 5.7 | 0.04 | ||
| Smeltzer (2018) [27] | Social Well-Being | 18.3 ± 3.9 | 17.7 ± 5.2 | <0.001 | ||
| Overall Quality of Life (N = 9) | ||||||
| Borneman (2008) [18] | Overall QOL (0–10) | 5.0 ± 2.1 | 4.6 ± 2.1 | 0.53 | ||
| Chen (2023) [19] | FACT-L Scale | 111.66 ± 14.90 | 117.81 ± 11.15 | <0.001 | ||
| Trial Outcome Index (TOI) | 70.66 ± 11.35 | 75.62 ± 8.62 | <0.001 | |||
| Lung Cancer Subscale | 29.64 ± 3.94 | 30.90 ± 2.96 | <0.003 | |||
| Edbrooke (2019) [20] | Global QoL—EORTC QLQ-C30 | 62.4 | 67.1 | |||
| Ferrell (2015) [21] | Overall FACT-L Early | 93.7 ± 20.6 | 115.4 ± 12.6 | <0.001 | ||
| Overall FACT-L Late | 105.3 ± 20.1 | 105.8 ± 18.8 | <0.001 | |||
| Gregersen (2024) [23] | Global QoL—EORTC QLQ-C30 | +1.52 ± 23.8 | +0.93 ± 23.7 | 0.57 | ||
| Raz (2016) [24] | FACT-L Total Score (0–140) | 98.7 ± 20.5 | 126.1 ± 8.2 | <0.001 | ||
| Shao (2023) [25] | Global QoL (EORTC QLQ-C30 | 51.7 ± 20.3 | 62.5 ± 19.8 | <0.01 | ||
| Schofields (2013) [26] | Total QoL Score (FACT-L Total) | 83.3 ± 18.1 | 91.6 ± 15.5 | 0.01 | ||
| Smeltzer (2018) [27] | Lung Cancer Scale | 32.2 ± 5.8 | 31.9 ± 5.5 | <0.001 | ||
| Green represents Better in MDT. | White represents No Difference. | Red represents Non-MDT Better. | ||||
Table A4.
Physical Well-being Outcomes vs. Enablers and Barriers.
| Study | Physical Well-Being Outcomes | Key Enablers | Key Barriers | Result |
|---|---|---|---|---|
| Raz (2016) [24] | ↑ Physical well-being: 22.2 → 25.2 (p < 0.001) | Digital tools (e-referrals, telehealth), weekly MDTs, tailored education | Limited availability of palliative care specialists, lack of MDT structure | Better in MDT |
| Chen (2023) [19] | ↑ Lung cancer subscale: 29.64 → 30.90 (p < 0.003) | E-Warm MDT model, regular assessments, multidisciplinary collaboration | Limited resources, low awareness, limited psych/nutritional support | Better in MDT |
| Edbrooke (2019) [20] | ↓ Fatigue: –6.7 points (p = 0.03) ↓ Dyspnoea: –6.2 points (p = 0.03) | Tailored 8-week MDT rehab program, Home-based delivery improving accessibility, Multidisciplinary involvement, Structured and individualized exercise plans | Patient frailty (advanced stage lung cancer), variation in home environments and adherence, limited system capacity for coordinated home-based MDT services | Better in MDT |
| Ferrell (2015) [21] | ↑ Physical well-being early: 19.5 → 23.3 (p = 0.004) ↑ Total: 20.3 → 22.8 | Standardized assessments, structured meetings, tailored education | Late stage focus of palliative care | Better in MDT |
| Smeltzer (2018) [27] | ↑ Physical well-being: 24.9 → 24.9 (p < 0.001) | Nurse navigator, co-located MDT clinic, outreach to underserved patients | Scheduling challenges, specialist autonomy concerns, fragmented care | Statistically better, not clinically |
| Borneman (2008) [18] | ↓ Physical QOL: 5.8 → 5.5 (p < 0.003, lower = better) | Early referral, standardized tools, interdisciplinary case conferences | Misconceptions, psychosocial barriers, limited follow-up | Better in MDT |
| Gregersen (2024) [23] | No significant differences in physical metrics Pain: +2.27 → −1.74 (p = 0.63) Dyspnea: −6.82 → +0.75 (p = 0.06) | Randomized allocation, ethical adherence, geriatric MDT involvement | High attrition, clinicians not blinded | No meaningful difference |
| Shao (2023) [25] | ↑ 6MWD: 352 → 393 m (p = 0.02); ↓ mMRC, ↓ Fatigue (p < 0.01) | Home-based MDT visits (physio, dietitian, nurse, psychologist), structured 8-week program, high adherence | Resource-intensive, coordination complexity, limited scalability in systems lacking home care infrastructure | Better in MDT |
| Schofields (2013) [26] | ↑ FACT-L physical well-being: 18.6 → 21.2 (p = 0.005) ↓ Fatigue: 65.6% → 45.8% ↓ Pain: 49.2% → 28.8% | Structured distress assessment (NCCN tool), weekly MDTs, lung cancer nurse coordinators | Time constraints, variation in services across sites | Better in MDT |
| Wang (2014) [28] | No meaningful difference across symptoms | Large sample, symptom tracking [18] | No MDT intervention, limited detail on enablers/barriers | No difference |
↑ indicates an increase or improvement in the outcome; ↓ indicates a decrease or reduction in the outcome.
Table A5.
Functional Well-being Outcomes vs. Enablers and Barriers.
| Study | Functional Outcome | Key Enablers | Key Barriers | Result |
|---|---|---|---|---|
| Raz (2016) [24] | +8.2 in functional well-being | Digital integration, weekly MDTs, tailored education | Non prominent | Better in MDT |
| Chen (2023) [19] | +6.15 in FACT-L | Structured MDT, regular assessments | Low awareness, limited psychological support | Better in MDT |
| Edbrooke (2019) [20] | ↑ Physical Functioning: +6.0 points (p = 0.01) | Multidisciplinary team coordination (PT, nurse, OT, etc.), personalized activity and care plans, emphasis on functional goal setting, and home-based setting enabled engagement | Difficulty in standardizing functional interventions at home, long-term follow-up | Better in MDT |
| Ferrell (2015) [21] | +4.9 early, +1.9 total | Structured meetings, education | Late-stage focus | Better in MDT |
| Gregersen (2024) [23] | No significant change | Randomized design, questionnaires | High attrition, clinician blinding | No meaningful difference |
| Smeltzer (2018) [27] | +0.1 | Nurse navigator, co-located clinic | Autonomy concerns, fragmented care | No meaningful difference |
| Shao (2023) [25] | ↑ EORTC QLQ-C30 physical function: 61.2 → 71.5 (p < 0.01) | Structured 8-week home-based MDT care, high adherence, baseline ECOG 0 higher in MDT | Blinding not feasible, process complexity, scalability concerns | Better in MDT |
| Schofields (2013) [26] | ↑ Functional well-being: 13.3 → 16.4 (p = 0.01) | Structured needs assessment (NCCN Distress Thermometer), nurse coordinator support, weekly MDTs | Time constraints, service variability across sites, occasional gaps in referrals | Better in MDT |
| Borneman (2008) [18] | 21.1 (no comparator) | Early referral, education | Psychosocial barriers, limited follow-up | Unclear benefit |
↑ indicates an increase or improvement in the outcome.
Table A6.
Emotional Well-being Outcomes vs. Enablers and Barriers.
| Study | Emotional Outcome | Key Enablers | Key Barriers | Result |
|---|---|---|---|---|
| Chen (2023) [19] | ↓ Anxiety: 2.66 → 1.45 (p < 0.001) ↓ Depression: 3.54 → 1.5 ↑ Normal Mood Cases: 79.3% → 90.7% | Structured E-Warm model, regular assessments, collaborative MDT care | Low awareness, cultural norms, limited psychological support | Better in MDT |
| Edbrooke (2019) [20] | ↑ Emotional Functioning: +3.2 points (p = 0.21) ↓ HADS Anxiety: –0.8 (p = 0.61) ↓ HADS Depression: –0.7 (p = 0.55) | Supportive care integrated into MDT model, home-based setting reduced travel stress | No formal psychological or psychiatric intervention included | Better in MDT |
| Ferrell (2015) [21] | ↑ Emotional Well-being (Early): 16.7→20.8 ↑ Total: 17.6 → 19.9 (p < 0.001) | Tailored education, structured MDT meetings, standardized assessments | Late stage focus of care | Better in MDT |
| Raz (2016) [24] | ↑ Emotional Well-being: 19.4 → 23.2 ↓ Distress: 4.0 → 1.0 (p < 0.001) | Digital tools, weekly MDT meetings, individualized sessions | Lack of structured MDTs, limited palliative specialists | Better in MDT |
| Smeltzer (2018) [27] | ↑ Emotional well-being: 24.0 → 24.2 (p < 0.001) | Nurse navigator, co-located MDT clinic, admin support | Scheduling conflicts, autonomy concerns, fragmented referrals | Better in MDT |
| Borneman (2008) [18] | ↑ Psychological QOL: 4.5 → 4.7 | Early referral, interdisciplinary conferences, patient education | Misconceptions, psychosocial barriers, limited follow-up | Slight improvement |
| Gregersen (2024) [23] | Emotional Functioning: +5.67 → +8.25 (p = 0.26) Future Worries: ↓ (ns) | Randomized MDT allocation, ethical rigor, comprehensive measures | High attrition, clinicians not blinded | No meaningful difference |
| Shao (2023) [25] | ↓ Anxiety (HADS-A): 9.3 → 6.7 (p < 0.01) ↓ Depression (HADS-D): 8.5 → 6.2 (p < 0.01) | Home-based MDT (including psychologist), structured 8-week intervention, regular monitoring | Emotional distress may hinder engagement; cognitive/functional burden; no long-term follow-up | Better in MDT |
| Schofields (2013) [26] | ↓ Anxiety: 7.7 → 5.5 (p = 0.01) ↓ Depression: 5.7 → 4.2 (p = 0.04) ↓ Distress: 4.5 → 3.1 (p = 0.01) | Use of NCCN distress thermometer, lung cancer nurse coordinators, systematic psychosocial screening | Emotional readiness of patients, stigma around mental health, variable access to psychology services | Better in MDT |
↑ indicates an increase or improvement in the outcome; ↓ indicates a decrease or reduction in the outcome.
Table A7.
Social Well-being Outcomes vs. Enablers and Barriers.
| Study | Social Outcome | Key Enablers | Key Barriers | Result |
|---|---|---|---|---|
| Edbrooke (2019) [20] | ↑ Social Functioning: +3.4 points (p = 0.15) | Holistic MDT care, including psychosocial support; home-based setting enabled patients to remain connected with family and carers | Short duration (8 weeks) may not capture long-term social gains | Better In MDT |
| Ferrell (2015) [21] | ↑ Social/Family Well-being (Early): 20.4 → 24.5 ↑ Total: 22.3 → 23.6 (p < 0.001) | Tailored education, structured MDTs, patient-centered planning | Late-stage care focus | Significantly better in MDT |
| Raz (2016) [24] | ↑ Social Well-being: 19.1 → 25.6 ↑ Spiritual Well-being: 32.7 → 43.1 (p < 0.001) | Weekly MDTs, digital referrals, personalized sessions | Poor MDT structure, limited specialists | Significantly better |
| Borneman (2008) [18] | Social QOL: 4.9 → 5.0 Spiritual QOL: 6.3 → 5.8 (no significant difference) | Early palliative referral, case conferences, education | Misconceptions, psychosocial barriers | No meaningful difference |
| Gregersen (2024) [23] | ↓ Social Functioning: +2.75 → −4.48 (p = 0.24) Family Support: ~no change (p = 0.44) | Comprehensive CGA, randomization, ethical oversight | High attrition, lack of blinding | No meaningful difference |
| Smeltzer (2018) [27] | ↓ Social well-being: 18.3 → 17.7 (p < 0.001) | Co-located MDT clinic, nurse navigator | Specialist autonomy concerns, fragmented care | Non-MDT better |
| Chen (2023) [19] | Not reported | E-Warm model, structured assessments, collaborative MDTs | Low awareness, lack of psychosocial support | Not assessed |
| Shao (2023) [25] | ↑ Satisfaction with care team: 13.99 → 15.97 (p < 0.01) ↑ Global QoL: 51.7 → 62.5 (p < 0.01) | Psychosocial support integrated in MDT (nurse, psychologist), high engagement, personalized approach | Resource-intensive, limited scalability, no long-term data on social participation | Better in MDT |
| Schofields (2013) [26] | ↑ Social Well-being: 17.0 → 19.3 (p = 0.04) | Nurse coordination, structured psychosocial screening via MDT | Variable service availability across sites; patients may underreport needs due to stigma or stoicism | Better in MDT |
↑ indicates an increase or improvement in the outcome; ↓ indicates a decrease or reduction in the outcome.
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Figure 1.
PRISMA flowchart.
Figure 2.
Enablers and barriers to the implementation of multidisciplinary team (MDT) care. Physical well-being (P), emotional well-being (E), social well-being (S), and functional well-being (F).
Figure 2.
Enablers and barriers to the implementation of multidisciplinary team (MDT) care. Physical well-being (P), emotional well-being (E), social well-being (S), and functional well-being (F).
Table 1.
Search Strategy Based on the PICO (Population, Intervention, Comparison, Outcome) Framework.
Table 1.
Search Strategy Based on the PICO (Population, Intervention, Comparison, Outcome) Framework.
| PICO Concept Areas | MeSH Terms and Free Text Terms |
|---|---|
| Population (P): Patients diagnosed with lung cancer | “Lung Cancer” “lung neoplasm” “small lung cancer” |
| Intervention (I): Multidisciplinary team management | “Multidisciplinary team” “multidisciplinary team” “multidisciplinary care team” “tumor board” “multidisciplinary clinic” “multidisciplinary approach” |
| Comparison (C): Standard or non-multidisciplinary team management | N/A |
| Outcome (O): Effectiveness of treatment outcomes and survival rates, patient satisfaction | “Quality of Life”/exp OR “Patient-Reported Outcomes” OR “Patient Reported Outcome Measures” OR “Palliative Care”/exp OR “Symptom Management” |
Table 2.
Inclusion Criteria and Exclusion Criteria.
| Category | Inclusion Criteria | Exclusion Criteria |
|---|---|---|
| Population | Patients diagnosed with any type of lung cancer (NSCLC, SCLC, and other lung neoplasms) at any disease stage (early, locally advanced, or metastatic). | Studies not including patients with lung cancer. |
| Intervention/Exposure | Involvement of a multidisciplinary team (MDT) in patient management, including roles such as oncologists, pulmonologists, palliative care specialists, nurses, and social workers. | Studies focusing solely on single-specialty care (e.g., surgery or chemotherapy only) or complementary/alternative medicine not integrated into MDT care. |
| Models of MDT Care | Studies describing different MDT care models (e.g., tumor boards, coordinated care plans, integrated care approaches). | Studies not describing or involving MDT-based care. |
| Comparative Group | Inclusion of a comparative group (e.g., historical or contemporaneous cohort without MDT care). Groups are inclusive of peri-operative care, systemic therapy, and palliative care pathways. | Studies lacking a comparative group (no control or comparison cohort). |
| Outcomes | Reporting on quality of life (physical well-being, emotional well-being, social well-being and functional well-being). Reporting on patient-reported outcomes (symptoms, side effects, emotional well-being, satisfaction with care). | Studies that do not report relevant outcomes related to MDT or QoL or Patient reported measures. |
| Study Design | Randomized controlled trials, controlled clinical trials, before–after studies, retrospective/prospective cohort studies, cross-sectional studies. | Case reports, editorials, opinion pieces, reviews, and studies without appropriate methodological design. |
| Language | Published in English. | Published in languages other than English. |
| Publication Type | Peer-reviewed original research articles. | Non-peer-reviewed publications, abstracts only, or conference posters without full data. |
Table 3.
Study Characteristics Table.
| Author | Study Design Country | Setting | MDT Group (n) Non-MDT Group (n) | Male (%) Age (y) | Lung Cancer Type and Stage | Follow-Up Time Window | Quality of Study (Using CASP) |
|---|---|---|---|---|---|---|---|
| Borneman (2008) [18] | Pre-post study and Descriptive study United States | National Cancer Institute–designated Comprehensive Cancer Centre | Barrier Study—28 and 18 QoL Pilot-10 | 48% (Barriers Study), 33% (QoL Pilot) 64 (Barriers Study), 67 (QoL Pilot) | Stage I–IV | 1 month 3 months | Moderate |
| Chen (2023) [19] | Randomized Controlled Trial China | Chongqing University Cancer Hospital | 140 (Early palliative care group) 140 (Standard care group) | 70% Mean 63 | Stage IIIB-IV NSCLC | 6 months | High |
| Edbrooke (2019) [20] | Randomized Controlled Trial Australia | Home-based rehabilitation | 41 41 | 55% 72 years | Inoperable NSCLC and SCLC, mostly stage III–IV | 9 weeks 6 months | Moderate |
| Ferrell (2015) [21] | Controlled Clinical Trial United States | California (outpatient thoracic surgery and medical oncology clinics) | 272 219 | 38.5% <65 (46.4%), 65–74 (34%), ≥75 (19.6%) | NSCLC (Stages I–IV) | 3 months 6 months 12 months | Low |
| Friedman (2016) [22] | Cohort Study United States | Lehigh Valley Health Network | 52 57 | Stage III NSCLC | No follow-up | Moderate | |
| Gregersen (2024) [23] | Randomized Controlled Trial Denmark | Aarhus University Hospital (Oncological Outpatient Clinic) | 182 181 | 55% Mean 76 (SD 4.6) | Cancer patients (prefrail and frail, non-surgical) | 3 months | High |
| Raz (2016) [24] | Before–after (pre–post) study United States | National Cancer Institute-Designated Comprehensive Cancer Centre | 38 33 | 42.40% | Not explicitly stated | 6 months 12 months | High |
| Shao (2023) [25] | Randomized Controlled Trial Japan | Home-based care | 36 35 | 76% 74 | Inoperable NSCLC and SCLC; mainly Stage III–IV | 3 months 6 months | High |
| Schofields (2013) [26] | Randomized Controlled Trial Australia | Multicenter (three oncology clinics in Victoria) | 59 61 | 48.3% 66 years | Advanced (stage III/IV) (NSCLC) | 8 weeks 12 weeks | Moderate |
| Smeltzer (2018) [27] | Cohort Study United States | Community-based healthcare system in Memphis, TN | 178 348 | 50% | Various lung cancer stages, including Stage IV | 3 months 6 months | Moderate |
| Wang (2014) [28] | Cohort Study Taiwan | National Health Insurance system | 2724 5448 | Not specified Mean 64.75 | Newly diagnosed patients with lung cancer | 3 months 6 months 12 months | Moderate |
Table 4.
MDT composition and meeting frequency.
| Author | Core MDT Members | Allied Health/Support Members | MDT Meeting Frequency |
|---|---|---|---|
| Borneman (2008) [18] | Medicine, nurse specialists | Social work, chaplaincy, counseling, nursing assistants | Not reported |
| Chen (2023) [19] | Medical oncologists, oncology nurse specialists | Dietitians, psychologists | Monthly |
| Edbrooke (2019) [20] | Physiotherapist, Nurse | Occupational therapist, dietitian, and palliative care physician | 8-week homebased program |
| Ferrell (2015) [21] | Oncologists, thoracic surgeons, nurse specialists, palliative physicians | Social workers, chaplains, dietitians, physical therapists | Weekly |
| Friedman (2016) [22] | Thoracic surgeons, medical and radiation oncologists, palliative care | Diagnostic radiology, pulmonary medicine, nutrition | Weekly |
| Gregersen (2024) [23] | Geriatricians, specialized nurses | Medication review, nutrition support, psychological support (via follow-ups, not formal MDT) | Not a formal MDT meeting |
| Raz (2016) [24] | Thoracic surgeons, nurse specialist, pulmonologists | Pain specialists, social workers, chaplains, dietitians, physical therapists | Weekly |
| Shao (2023) [25] | Physiotherapist | Dietitians, nurses, psychologists. | Delivered 8-week program: 2 home visits/week + 1 call/week |
| Schofields (2013) [26] | Oncologists, lung cancer nurse | Psychologists, Palliative care nurses | Weekly |
| Smeltzer (2018) [27] | Thoracic surgeon, medical oncologist, radiation oncologist, pulmonologist | Radiologist, nurse navigator | Weekly |
| Wang (2014) [28] | Physicians, nursing specialist | Psychological consultants, social workers, case managers | Not reported |
Table 5.
Summary of Patient-Reported Quality of Life Outcomes Across Domains (Physical, Functional, Emotional, Social, and Overall Well-Being).
Table 5.
Summary of Patient-Reported Quality of Life Outcomes Across Domains (Physical, Functional, Emotional, Social, and Overall Well-Being).
| Domain | Number of Studies Reporting Improvement | Findings |
|---|---|---|
| Physical well-being | 8/10 | Most studies report reduced fatigue, pain, dyspnea, and improved mobility/nutrition; some minor symptoms worsening in select studies. |
| Functional well-being | 6/8 | Improvements mostly in physical and role functioning; effect less consistent in one study. |
| Emotional well-being | 7/9 | Anxiety and depression reduced; some domains unchanged or worse (e.g., burden of illness). |
| Social well-being | 5/7 | Family support and financial outcomes improved; some studies showed minimal change or slight decrease. |
| Overall QoL | 6/9 | Higher total QoL scores in most studies; effect varies by patient group and study. |
The studies included used various validated tools and scales to measure different aspects of quality of life (QoL). This approach showed the complex nature of patient-reported outcomes. The tools assessed areas such as physical, functional, emotional, and social well-being, as well as overall QoL.
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MDPI and ACS Style
Srivastava, A.; Daniel, E.; Lam, V.; Kwedza, R.K.; Rushton, S.; Li, L. Impact of Multidisciplinary Team Care on Patient-Reported Outcomes in Patients with Lung Cancer: A Systematic Review. Curr. Oncol. 2025, 32, 697. https://doi.org/10.3390/curroncol32120697
AMA Style
Srivastava A, Daniel E, Lam V, Kwedza RK, Rushton S, Li L. Impact of Multidisciplinary Team Care on Patient-Reported Outcomes in Patients with Lung Cancer: A Systematic Review. Current Oncology. 2025; 32(12):697. https://doi.org/10.3390/curroncol32120697
Chicago/Turabian StyleSrivastava, Aastha, Elizabeth Daniel, Vincent Lam, Ru Karen Kwedza, Shelley Rushton, and Ling Li. 2025. "Impact of Multidisciplinary Team Care on Patient-Reported Outcomes in Patients with Lung Cancer: A Systematic Review" Current Oncology 32, no. 12: 697. https://doi.org/10.3390/curroncol32120697
APA StyleSrivastava, A., Daniel, E., Lam, V., Kwedza, R. K., Rushton, S., & Li, L. (2025). Impact of Multidisciplinary Team Care on Patient-Reported Outcomes in Patients with Lung Cancer: A Systematic Review. Current Oncology, 32(12), 697. https://doi.org/10.3390/curroncol32120697
