Factors to improve distress and fatigue in Cancer survivorship; further understanding through text analysis of interviews by machine learning.

BACKGROUND: From patient-reported surveys and individual interviews by health care providers, we attempted to identify the significant factors related to the improvement of distress and fatigue for cancer survivors by text analysis with machine learning techniques, as the secondary analysis using the single institute data from the Korean Cancer Survivorship Center Pilot Project. METHODS: Surveys and in-depth interviews from 322 cancer survivors were analyzed to identify their needs and concerns. Among the keywords in the surveys, including EQ-VAS, distress, fatigue, pain, insomnia, anxiety, and depression, distress and fatigue were focused. The interview transcripts were analyzed via Korean-based text analysis with machine learning techniques, based on the keywords used in the survey. Words were generated as vectors and similarity scores were calculated by the distance related to the text's keywords and frequency. The keywords and selected high-ranked ten words for each keyword based on the similarity were then taken to draw a network map. RESULTS: Most participants were otherwise healthy females younger than 50 years suffering breast cancer who completed treatment less than 6 months ago. As the 1-month follow-up survey's results, the improved patients were 56.5 and 58.4% in distress and fatigue scores, respectively. For the improvement of distress, dyspepsia (p = 0.006) and initial scores of distress, fatigue, anxiety, and depression (p < 0.001, < 0.001, 0.043, and 0.013, respectively) were significantly related. For the improvement of fatigue, economic state (p = 0.021), needs for rehabilitation (p = 0.035), initial score of fatigue (p < 0.001), any intervention (p = 0.017), and participation in family care program (p = 0.022) were significant. For the text analysis, Stress and Fatigue were placed at the center of the keyword network map, and words were intricately connected. From the regression anlysis combined survey scores and the quantitative variables from the text analysis, participation in family care programs and mention of family-related words were associated with the fatigue improvement (p = 0.033). CONCLUSION: Common symptoms and practical issues were related to distress and fatigue in the survey. Through text analysis, however, we realized that the specific issues and their relationship such as family problem were more complicated. Although further research needs to explore the hidden problem in cancer patients, this study was meaningful to use personalized approach such as interviews.

Latent profile analysis for assessing symptom clusters in women with breast cancer.

PURPOSE: To identify symptom clusters among breast cancer survivors and investigate differences in health-related quality of life (HRQoL) and distress based on these discerned symptom clusters using latent profile analysis. METHODS: We enrolled 655 adult breast cancer survivors aged 19 years and older, registered with the Cancer Survivor Integrated Supportive Center from May 2020 to July 2022. The study measured five symptoms-pain, fatigue, insomnia, anxiety, and depression-using a Visual Analogue Scale ranging from 0 to 10 points. Distress was measured using the National Comprehensive Cancer Network Distress Thermometer, with scores ranging from 0 to 10 points. HRQoL was determined using the EuroQol-5 Dimension questionnaire. Data analysis was conducted using the Jamovi and Mplus 8.8 software programs. RESULTS: The Cluster with Few Symptoms (46.8%) was the most common, whereas the Psychological Cluster with a very high degree of depression and anxiety accounted for 20.0%, and the Moderate symptom cluster with symptoms of 3 or more points accounted for 14.4%. Distress scores were relatively high in the Psychological Cluster and the Pain-Fatigue-Insomnia Cluster, and were lowest in the Cluster with Few Symptoms (F = 103.92, p < 0.001). HRQoL scores were highest in the Cluster with Few Symptoms and lowest in the Pain-Fatigue-Insomnia Cluster (F = 177.62, p < 0.001). CONCLUSIONS: Approximately half of breast cancer survivors who had completed the major treatment experienced persistent high symptoms such as depression and anxiety or pain, fatigue, and insomnia. IMPLICATIONS FOR CANCER SURVIVORS: These findings provide foundational data for developing tailored intervention strategies and programs based on symptom experiences.

Factors influencing psychological distress among breast cancer survivors using machine learning techniques.

Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of life of breast cancer survivors. This study aimed to assess the level of distress in breast cancer survivors and analyze the variables that significantly affect distress using machine learning techniques. A survey was conducted with 641 adult breast cancer patients using the National Comprehensive Cancer Network Distress Thermometer tool. Participants identified various factors that caused distress. Five machine learning models were used to predict the classification of patients into mild and severe distress groups. The survey results indicated that 57.7% of the participants experienced severe distress. The top-three best-performing models indicated that depression, dealing with a partner, housing, work/school, and fatigue are the primary indicators. Among the emotional problems, depression, fear, worry, loss of interest in regular activities, and nervousness were determined as significant predictive factors. Therefore, machine learning models can be effectively applied to determine various factors influencing distress in breast cancer patients who have completed primary treatment, thereby identifying breast cancer patients who are vulnerable to distress in clinical settings.