Seminars in Oncology Nursing最新文献

Objective: Breast cancer, one of the most prevalent cancers among women, not only results in significant physical changes but also causes considerable emotional distress and disruptions in social roles and relationships. While depression is the most commonly observed psychiatric disorder in cancer patients, there is a lack of sufficient research on the effectiveness, potential drug interactions, and side effects of antidepressants in this population. This gap underscores the increasing need for non-pharmacological approaches, such as psychotherapy. Therefore, the aim of this study was to assess the effectiveness of Interpersonal Psychotherapy (IPT) in addressing psychosocial challenges faced by breast cancer patients.

Methods: This systematic review was registered in the PROSPERO database (CRD42024576746). A systematic literature search was conducted across five databases: Cochrane Library, PubMed, Web of Science, Scopus, and Ovid MEDLINE. The review was carried out according to PRISMA guidelines. Study selection and data extraction were independently performed by two researchers using the Covidence platform. Disagreements were resolved through discussion to reach consensus.

Results: Four studies (two randomized controlled trials [RCTs], two pilots) met inclusion criteria. IPT significantly reduced depression, anxiety, and psychological distress, with some studies also reporting improvements in quality of life. One study showed strong effect sizes (Cohen's d > 1.0) for depression. Telephone-based IPT was feasible and beneficial. Findings support IPT as an effective psychosocial intervention for breast cancer patients.

Conclusion: IPT appears to be an effective intervention for reducing anxiety and depression and enhancing quality of life in patients with breast cancer. However, further high-quality, large-scale RCTs are needed to strengthen the evidence base and confirm its clinical utility.

Purpose: Artificial Intelligence (AI) has the potential to enhance supportive care for cancer survivors from diagnosis through treatment and into survivorship. This study aimed to provide an overview of available evidence on AI applications in supportive care for individuals with colorectal cancer (CRC).

Methods: This scoping review was conducted following the Joanna Briggs Institute guidelines. Studies published between 2014 and 2025 were retrieved from six databases: PubMed, Embase, CINAHL, Scopus, Web of Science, and PsycINFO, using a combination of search terms relevant to "colorectal cancer," "artificial intelligence," and "supportive care." Data on study characteristics, participants, settings, types of AI technologies, and supportive care dimensions were extracted for analysis.

Results: Out of 1,792 articles, 40 were identified as eligible for inclusion in this scoping review. AI applications for CRC supportive care are primarily in early development, focusing on machine learning-based prediction models that provide informational support for post-surgical side effects. The use of AI for physical support in symptom management and emotional support during cancer treatment and beyond was limited.

Conclusion: Implementing AI technology offers an opportunity to enhance supportive care for patients with CRC. This study suggests that current AI applications for CRC supportive care primarily focus on informational support, underscoring the need for further development of AI to provide comprehensive support, including psychological, social, spiritual, and practical aspects.

Implications for nursing practice: Further research is needed to develop AI-driven tools that comprehensively address the supportive care needs of cancer patients and enhance their outcomes.

Objectives: About 75% of newly diagnosed bladder cancers are non-muscle invasive bladder cancer (NMIBC). NMIBC and its treatments affect patient functioning and quality of life. Exercise is feasible, safe, and beneficial for many cancer patient groups, however, no studies have examined exercise for NMIBC. We aimed to examine the feasibility, safety, and preliminary efficacy of high-intensity interval training (HIIT) for patients with NMIBC during or after intravesical therapy.

Methods: The Bladder cancer and exeRcise trAining during or after intraVesical thErapy (BRAVE) trial randomized 25 NMIBC patients scheduled for or on surveillance after intravesical therapy to either usual care (n = 12) or HIIT (n = 13). The HIIT group performed thrice-weekly, supervised HIIT for 12 weeks.

Results: In 39 months, 293 patients were screened, 177 (60.4%) were eligible, and 25 (14.1%) were randomized. Median exercise attendance was 100%. From baseline to 12 weeks, VO_2peak increased by 1.2 mL/kg/min in the HIIT group compared to a decrease of 0.7 mL/kg/min in the usual care group (adjusted between-group difference, 2.0 mL/kg/min; 95% CI: -0.4 to 4.4; P = .10; d = 0.37). Compared to the usual care group at 12 weeks, the HIIT group significantly improved 6-minute walk distance (adjusted between-group difference, 41 meters; 95% CI: 6-77; P = .025; d = 0.32) and the timed 8-foot up-and-go (adjusted between-group difference, -1.0 second; 95% CI: -1.9 to -0.2; P = .019; d = -0.44).

Conclusions: Despite modest accrual, the BRAVE trial demonstrated that HIIT during or after intravesical therapy was safe and feasible for most NMIBC patients and resulted in meaningful improvements in several indicators of physical functioning.

Implications for nursing practice: Oncology nurses can inform NMIBC patients that high-intensity interval training may be safe, feasible, and potentially effective in improving physical functioning during or after intravesical therapy.

Objectives: To synthesize current educational approaches to AI literacy in oncology nursing, identify key competency domains along with barriers and enablers, and offer clinically oriented recommendations for the safe and effective integration of AI into clinical practice.

Methods: Structured search of MEDLINE via PubMed (2015-2025) using MeSH and free-text terms, complemented with free sources (Google Scholar, OpenAlex/Lens), handsearching of key journals, and backward/forward citation chasing. Study selection was performed by two independent reviewers, with disagreements resolved by consultation with a third author; the process is summarized in a PRISMA 2020 flow diagram.

Results: Findings confirm that digital and AI literacy are fundamental for oncology nurses. Effective use of AI requires a grasp of basic ML principles, data interpretation, and ethics. Educational strategies include integration into formal curricula and innovative formats such as microlearning, simulations, and virtual reality. Key barriers are uneven digital skills, resistance to technology, and lack of structured programs. Successful education is further supported by multidisciplinary collaboration and patient involvement. Evidence suggests that AI enhances clinical decision-making, personalized care, safety, and nurse autonomy.

Conclusions: Incorporating AI competencies into nursing education is crucial for improving safety and quality in oncology care. Educational reforms should foster critical thinking, ensure ongoing evaluation, and preserve empathy towards patients. Verified and flexible programs enable sustainable literacy development aligned with technological and ethical standards.

Implications for nursing practice: Nurses educated in AI can improve clinical decision-making, reduce errors, and provide empathetic, individualized care. AI should be regarded solely as a tool that supports nurses' work, not as a replacement. Interdisciplinary and patient-centered approaches support the safe integration of AI into daily oncology nursing practice. This review uniquely focuses on oncology nursing, integrates peer-reviewed and professional/grey sources, and offers practical curriculum and clinical integration recommendations that complement recent reviews.

Objectives: This study aimed to explore pediatric oncology nurses' perspectives on the integration of artificial intelligence (AI) into pediatric oncology care, focusing on its potential advantages, implementation challenges, and ethical considerations.

Methods: A hermeneutically informed descriptive phenomenological qualitative design was employed. One-to-one, semistructured interviews were conducted with 18 pediatric oncology nurses between April and June 2025. All interviews were audio-recorded, transcribed verbatim, and analyzed thematically in MAXQDA using Braun and Clarke's six-phase framework (2006). Reporting followed the COREQ checklist.

Results: Participants' mean age was 36.8 ± 6.9 years, and most had >10 years of professional experience. Thematic analysis identified five main themes with related subthemes: (1) the potential of AI in pediatric oncology care; (2) implementation challenges and concerns; (3) nurse-AI collaboration; (4) ethical considerations; and (5) competence and training needs. Nurses highlighted AI's potential to accelerate diagnostic/treatment processes, reduce error, and enhance patient safety, while also noting barriers related to infrastructure, ethics, and professional skills.

Conclusions: Pediatric oncology nurses perceived AI as a valuable tool to support clinical decision-making, improve patient safety, and increase care efficiency; however, ethical concerns, infrastructural limitations, and insufficient training constrain effective integration.

Implications for nursing practice: Strengthening technological competencies, ensuring ethical safeguards, and providing continuous training are essential for successful AI integration. Combining clinical expertise with AI competence may promote safer, more effective pediatric oncology care.

Objectives: The aim of this study was to explore the ruminative thoughts experienced by oncology nurses, examine their impact on both personal and professional life, and identify the coping strategies employed in the context of cancer care.

Methods: This inductive qualitative study employed in-depth interviews with 20 oncology nurses, conducted between March and July 2021, and utilized content analysis for data analyze.

Results: The main theme identified was Ruminative thoughts and coping strategies of oncology nurses. It highlighted how oncology nurses' ruminative thoughts, driven by patient losses, fears of inadequacy, and cancer-related anxieties, affect their mental well-being and personal growth. The main theme is built upon the subthemes "Confronting professional and personal realities," "Redefining self and values," and "Overcoming strategies."

Conclusions: This study reveals that oncology nurses' ruminative thoughts, driven by patient deaths, treatment inadequacy, and cancer fears, lead to mental fatigue but also foster personal growth. Coping strategies like distraction, thought suppression, and adopting a commitment to excellence mindset help them manage these challenges.

Implications for nursing practice: Awareness of ruminative thoughts and the implementation of interventions such as resilience training, mindfulness-based practices, and team-based care approaches can reduce mental fatigue among nurses, enhancing their well-being and the quality of care they provide.

Objective: To explore the association between cancer information overload and attitudes toward cancer screening among internal medicine patients.

Aims: To identify predictors of screening attitudes using statistical and machine learning models.

Methods: A cross-sectional study was conducted with 410 internal medicine outpatients. Data were collected using the Cancer Information Overload Scale and the Attitude Scale for Cancer Screening. Analyses included t-tests, ANOVA, and regression. Seven machine learning models (KNN, SVM, ANN, RF, XGBoost, CART, Elastic Net) were compared using 10-fold cross-validation (R², RMSE, MAE). Statistical significance was set at P < .05.

Results: Participants' mean age was 38.25 ± 12.46 years; 69.8% were female. Mean information overload and attitude scores were 17.14 ± 4.91 and 100.13 ± 13.58, respectively. Regression analysis showed a significant negative association between information overload and screening attitudes (β = -0.321, P < .001; R² = 0.103; 95% CI [-1.144, -0.634]). Among machine learning models, Elastic Net Regression (α = 0.2, λ = 1) achieved the best performance (RMSE = 12.6, MAE = 10.8), confirming cancer information overload as the strongest predictor.

Conclusion: Cancer information overload is inversely associated with screening attitudes. Machine learning models enhance interpretability, emphasizing the importance of managing information burden to improve cancer screening engagement.

Objectives: Oncology nurses provide relational care with patients and families that require high levels of skill and empathy. This emotionally demanding work can lead to compassion fatigue, burnout, and unprocessed grief. Strategies to support oncology nurses are crucial for maintaining their well-being and delivering high-quality care. The purpose of this study was to analyze stories written by oncology nurses to understand the emotional experiences of caring for people with cancer.

Methods: A secondary qualitative analysis of 35 oncology nurses' stories was conducted using Braun and Clarke's thematic analysis framework. The nurses were participants in two Storytelling Through Music intervention studies, which included writing stories to process work-related emotions. Themes were developed to identify patterns and shared experiences across the narratives.

Results: The meta-theme of "Seeking Emotional Balance" emerged and was interwoven throughout the six themes: emotional labor, above and beyond, connections and mutual healing, cumulative grief and loss, coping and remembrance, and finding meaning. Nurses described the challenges of maintaining emotional balance while navigating professional and personal emotional demands.

Conclusion: Oncology nurses face unique relational and emotional challenges. While some found resilience in patient connections, others experienced chronic distress and burnout. Storytelling provides a reflective outlet to process emotions, strengthen resilience, and foster shared understanding among peers. Storytelling interventions show promise as tools for emotional regulation and professional sustainability.

Implications for nursing: Deep nurse-patient connections foster meaning and resilience but can blur boundaries, increasing risks like countertransference. Reflective practices help safeguard nurses' well-being and care quality. At the individual level, nurses should adopt self-care strategies and engage in reflective practices. Organizational support is vital. Institutions can provide emotional resilience training, implement bereavement overload policies, and offer group storytelling opportunities to reduce stress, enhance regulation, and build supportive peer connections.

Objectives: Pain-fatigue-sleep disturbance symptom (PFS) cluster is the most common symptom cluster in patients with lung cancer following chemotherapy, which significantly impacts their quality of life. This study aims to develop and validate a machine learning-based prediction model for the severe PFS cluster and identify the relevant factors in patients with lung cancer following chemotherapy.

Methods: A total of 612 patients were enrolled in the study, and logistic regression, along with four machine learning algorithms, was used. The area under the curve (AUC), accuracy, sensitivity, specificity, and Brier score were utilized for model evaluation. The Shapley additive interpretation and restricted cubic splines were employed to assess the significance of feature coefficients. A web-based application was developed to facilitate the practical implementation of the best model in clinical settings.

Results: The random forest model was identified as optimal, exhibiting the best discrimination and calibration in the test set (AUC: 0.765 and Brier score: 0.159) and excellent performance in the validation set (AUC: 0.914 and Brier score: 0.124). The factors encompassed in the model construction comprised stress, C-reactive protein, depression, body mass index (BMI), anxiety, neutrophils, age, gender, pathological classification, and Eastern Cooperative Oncology Group performance status. A nonlinear relationship existed between stress, BMI, age, and the severe PFS cluster.

Conclusions: The developed web program would assist health care professionals in accurately identifying patients experiencing the severe PFS cluster in clinical practice and facilitating efficient symptom management.

Implications for nursing practice: Clinical nurses can use a web-based calculator developed in this study to effectively identify patients with the severe PFS cluster and provide targeted interventions.