Objectives: This study examined the symptoms and emotions expressed by older adults with cancer and their caregivers in South Korean online cancer communities. It aimed to identify narrative patterns and provide insights to inform personalized care strategies. Methods: We analyzed 6,908 user-generated posts collected from major online cancer communities in South Korea. Keyword frequency analysis, term frequency-inverse document frequency, 2-gram analysis, and latent Dirichlet allocation-based topic modeling were applied to explore language patterns. Sentiment analysis identified 12 emotional categories, and Pearson correlation coefficients were calculated to examine associations between symptoms and emotional expressions. All data were cleaned and standardized prior to analysis. Results: Many users expressed anxiety (20.63%) and depression (19.59%), frequently associated with chemotherapy and sleep disturbances. Among reported symptoms, sleep problems carried the highest negative sentiment (79.81%), underscoring their profound impact on well-being. Topic modeling consistently revealed seven recurring themes, including treatment decision-making, symptom management, and concerns about family, demonstrating the layered and personalized experiences of older cancer patients and their caregivers. Conclusions This study explored treatment-related and symptom-related difficulties faced by older adults with cancer. Many reported significant emotional strain, especially anxiety, depression, and sleep disturbances. These findings highlight the necessity for supportive strategies addressing both psychological and physical aspects of care. Future research could investigate the utility of large language models in analyzing these narratives, provided the data is ethically managed and appropriate for such use.

Objectives: This study examined the symptoms and emotions expressed by older adults with cancer and their caregivers in South Korean online cancer communities. It aimed to identify narrative patterns and provide insights to inform personalized care strategies. Methods: We analyzed 6,908 user-generated posts collected from major online cancer communities in South Korea. Keyword frequency analysis, term frequency-inverse document frequency, 2-gram analysis, and latent Dirichlet allocation-based topic modeling were applied to explore language patterns. Sentiment analysis identified 12 emotional categories, and Pearson correlation coefficients were calculated to examine associations between symptoms and emotional expressions. All data were cleaned and standardized prior to analysis. Results: Many users expressed anxiety (20.63%) and depression (19.59%), frequently associated with chemotherapy and sleep disturbances. Among reported symptoms, sleep problems carried the highest negative sentiment (79.81%), underscoring their profound impact on well-being. Topic modeling consistently revealed seven recurring themes, including treatment decision-making, symptom management, and concerns about family, demonstrating the layered and personalized experiences of older cancer patients and their caregivers. Conclusions This study explored treatment-related and symptom-related difficulties faced by older adults with cancer. Many reported significant emotional strain, especially anxiety, depression, and sleep disturbances. These findings highlight the necessity for supportive strategies addressing both psychological and physical aspects of care. Future research could investigate the utility of large language models in analyzing these narratives, provided the data is ethically managed and appropriate for such use.

Objectives: This study examined the symptoms and emotions expressed by older adults with cancer and their caregivers in South Korean online cancer communities. It aimed to identify narrative patterns and provide insights to inform personalized care strategies. Methods: We analyzed 6,908 user-generated posts collected from major online cancer communities in South Korea. Keyword frequency analysis, term frequency-inverse document frequency, 2-gram analysis, and latent Dirichlet allocation-based topic modeling were applied to explore language patterns. Sentiment analysis identified 12 emotional categories, and Pearson correlation coefficients were calculated to examine associations between symptoms and emotional expressions. All data were cleaned and standardized prior to analysis. Results: Many users expressed anxiety (20.63%) and depression (19.59%), frequently associated with chemotherapy and sleep disturbances. Among reported symptoms, sleep problems carried the highest negative sentiment (79.81%), underscoring their profound impact on well-being. Topic modeling consistently revealed seven recurring themes, including treatment decision-making, symptom management, and concerns about family, demonstrating the layered and personalized experiences of older cancer patients and their caregivers. Conclusions This study explored treatment-related and symptom-related difficulties faced by older adults with cancer. Many reported significant emotional strain, especially anxiety, depression, and sleep disturbances. These findings highlight the necessity for supportive strategies addressing both psychological and physical aspects of care. Future research could investigate the utility of large language models in analyzing these narratives, provided the data is ethically managed and appropriate for such use.

Purpose: This study aimed to examine the level of self-concept and psychosocial well-being among women with BRCA1 or BRCA2 gene mutations and to identify factors affecting their psychosocial well-being. Methods: A cross-sectional design was used.Data were collected from an online community comprising Korean patients with breast cancer and their families. A total of 98 women with BRCA1 or BRCA2 gene mutations completed the online questionnaire comprising the BRCA self-concept scale, the psychosocial well-being index-short form, demographic characteristics, and disease-related characteristics. Descriptive statistics, frequencies, independent t-tests, one-way ANOVA, Pearson’s correlation, and multiple regression were used for data analysis. Results: The total self-concept level at 82.13±15.45 (range: 17~119), and the psychosocial well-being level at 28.81±9.51 (range: 0~54) indicated a high-risk group of psychosocial well-being among the surveyed women with BRCA gene mutations compared with Korean general office workers. Self-concept (β=.57, p<.001) and monthly family income (≥4,500 USD)(β=-.24, p=.048) significantly affected the psychosocial well-being of women with BRCA1 or BRCA2 gene mutations. Conclusion The level of self-concept and psychosocial well-being of women with BRCA1 or BRCA2 gene mutations should be assessed carefully, and tailored consultation and educational programs should be developed to overcome a negative self-concept. Supportive systems for financially vulnerable women with BRCA1 or BRCA2 gene mutations should be considered.

The title of page 189 should be corrected.

INTRODUCTION: Human attachment is known to be closely associated with psychophysiological phenomenon. However, there have not been enough researches on the relationship of the attachment with sleep, especially with insomnia. The objective of the present study was to investigate the relationship between adult attachment styles and insomnia in community-dwelling population. METHODS: One hundred seventy seven community-dwelling adults (74 males and 103 females ;mean age 41.23+/-8.44) participated in the current study. To assess the attachment styles (secure, dismissing, preoccupied and fearful), self-reporting Relationship Style Questionnaires (RSQ) were completed by the participants. Presence, type, frequency and duration of insomnia in the last month were also investigated. RESULTS: Compared to subjects without insomnia, subjects with insomnia had higher fearful attachment scores (t =2.87, p=0.005). Higher fearful attachment score were found in all subtypes of insomnia (sleep-onset insomnia, t =2.33, p=0.021;maintenance insomnia, t=2.92, p=0.004;terminal insomnia, t=2.89, p=0.004). Subjects with frequent (>or =3 per week) insomnia had higher fearful attachment scores than subjects with infrequent (or =6 months) had higher preoccupied attachment scores relative to subjects with transient insomnia (<6 months), (t=2.57, p=0.012). CONCLUSION: In the current study, attachment styles were different depending on the characteristics of insomnia. The fearful attachment was associated with the presence of insomnia, while the preoccupied attachment was associated with the chronicity of insomnia. These findings suggest that there may be some relationship between the adult attachment styles and the clinical features of insomnia.
Adult ; Female ; Humans ; Male ; Surveys and Questionnaires ; Sleep Initiation and Maintenance Disorders

The purpose of this study was to evaluate the accuracy and precision of the three-dimensional (3D) imaging system of children’s facial soft tissue by comparing linear measurements. The subjects of the study were 15 children between the ages of 7 and 12. Twenty-three landmarks were pointed on the face of each subject and 16 linear measurements were directly obtained 2 times using an electronic caliper. Two sets of 3D facial images were made by the 3D scanner. The same 16 measurements were obtained on each 3D image. In the accuracy test, the total average difference was 0.9 mm. The precision of 3D photogrammetry was almost equivalent to that of direct measurement. Thus, 3D photogrammetry by the 3D scanner in children had sufficient accuracy and precision to be used in clinical setting. However, the 3D imaging system requires the subject’s compliance for exact images. If the clinicians provide specific instructions to children while obtaining 3D images, the 3D device is useful for investigating children’s facial growth and development. Also the device can be a valuable tool for evaluating the results of orthodontic and orthopedic treatments.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.