Angiosarcoma associated with lymphedema is a rare soft tissue malignancy that occurs owing to chronic lymphedema, primarily following breast cancer surgery. We present a case of an 83-year-old female who developed angiosarcoma 14 years after undergoing breast cancer surgery. Diagnosis was confirmed through excisional biopsy, histopathological evaluation, and imaging. Computed tomography and magnetic resonance imaging revealed diffuse dermal thickening with an enhanced subcutaneous nodular lesion on the right arm. This unusual case emphasizes the importance of vigilant monitoring in patients with chronic lymphedema post-breast cancer surgery, as early radiologic and clinical detection can facilitate timely intervention and improve outcomes.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Mammography is the standard screening method for breast cancer, proven to reduce mortality. However, its diagnostic performance varies depending on patient characteristics and radiologist expertise. Dense breast tissue, present in approximately 70% of Korean women aged 40 to 59, limits detection by obscuring malignancies. Additionally, optimal interpretation requires extensive training, which is not always achievable. Artificial intelligence-based computer-aided diagnosis (AI-CAD) has emerged as a promising tool for enhancing mammographic accuracy and efficiency.Current Concepts: AI-CAD has shown diagnostic performance comparable to that of experienced radiologists while addressing the limitations of traditional CAD systems, particularly excessive false positives. Studies suggest AI-CAD improves radiologists' accuracy, particularly among those with limited breast imaging experience. In Europe, AI-assisted reading is increasingly recognized as a viable alternative to traditional double reading. In Korea, adoption of AI-CAD is expanding, with systems approved by the Korean Food and Drug Administration currently in clinical use. Recently, one AI-CAD system received conditional non-reimbursement designation, allowing hospitals to use it for up to 5 years while collecting clinical evidence to support future insurance coverage decisions.Discussion and Conclusion: AI-CAD has significant potential to enhance early breast cancer detection while maintaining acceptable false-positive rates, making it a valuable adjunct in screening programs. Beyond improved detection, AI-CAD may optimize workflow efficiency by triaging cases and prioritizing high-risk examinations. However, its integration into clinical practice necessitates standardized guidelines, regulatory oversight, and further validation through large-scale prospective studies. As AI technology continues to advance, ongoing investigation into its role in personalized breast cancer screening is essential.

Angiosarcoma associated with lymphedema is a rare soft tissue malignancy that occurs owing to chronic lymphedema, primarily following breast cancer surgery. We present a case of an 83-year-old female who developed angiosarcoma 14 years after undergoing breast cancer surgery. Diagnosis was confirmed through excisional biopsy, histopathological evaluation, and imaging. Computed tomography and magnetic resonance imaging revealed diffuse dermal thickening with an enhanced subcutaneous nodular lesion on the right arm. This unusual case emphasizes the importance of vigilant monitoring in patients with chronic lymphedema post-breast cancer surgery, as early radiologic and clinical detection can facilitate timely intervention and improve outcomes.

Mammography is the standard screening method for breast cancer, proven to reduce mortality. However, its diagnostic performance varies depending on patient characteristics and radiologist expertise. Dense breast tissue, present in approximately 70% of Korean women aged 40 to 59, limits detection by obscuring malignancies. Additionally, optimal interpretation requires extensive training, which is not always achievable. Artificial intelligence-based computer-aided diagnosis (AI-CAD) has emerged as a promising tool for enhancing mammographic accuracy and efficiency.Current Concepts: AI-CAD has shown diagnostic performance comparable to that of experienced radiologists while addressing the limitations of traditional CAD systems, particularly excessive false positives. Studies suggest AI-CAD improves radiologists' accuracy, particularly among those with limited breast imaging experience. In Europe, AI-assisted reading is increasingly recognized as a viable alternative to traditional double reading. In Korea, adoption of AI-CAD is expanding, with systems approved by the Korean Food and Drug Administration currently in clinical use. Recently, one AI-CAD system received conditional non-reimbursement designation, allowing hospitals to use it for up to 5 years while collecting clinical evidence to support future insurance coverage decisions.Discussion and Conclusion: AI-CAD has significant potential to enhance early breast cancer detection while maintaining acceptable false-positive rates, making it a valuable adjunct in screening programs. Beyond improved detection, AI-CAD may optimize workflow efficiency by triaging cases and prioritizing high-risk examinations. However, its integration into clinical practice necessitates standardized guidelines, regulatory oversight, and further validation through large-scale prospective studies. As AI technology continues to advance, ongoing investigation into its role in personalized breast cancer screening is essential.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Angiosarcoma associated with lymphedema is a rare soft tissue malignancy that occurs owing to chronic lymphedema, primarily following breast cancer surgery. We present a case of an 83-year-old female who developed angiosarcoma 14 years after undergoing breast cancer surgery. Diagnosis was confirmed through excisional biopsy, histopathological evaluation, and imaging. Computed tomography and magnetic resonance imaging revealed diffuse dermal thickening with an enhanced subcutaneous nodular lesion on the right arm. This unusual case emphasizes the importance of vigilant monitoring in patients with chronic lymphedema post-breast cancer surgery, as early radiologic and clinical detection can facilitate timely intervention and improve outcomes.

Mammography is the standard screening method for breast cancer, proven to reduce mortality. However, its diagnostic performance varies depending on patient characteristics and radiologist expertise. Dense breast tissue, present in approximately 70% of Korean women aged 40 to 59, limits detection by obscuring malignancies. Additionally, optimal interpretation requires extensive training, which is not always achievable. Artificial intelligence-based computer-aided diagnosis (AI-CAD) has emerged as a promising tool for enhancing mammographic accuracy and efficiency.Current Concepts: AI-CAD has shown diagnostic performance comparable to that of experienced radiologists while addressing the limitations of traditional CAD systems, particularly excessive false positives. Studies suggest AI-CAD improves radiologists' accuracy, particularly among those with limited breast imaging experience. In Europe, AI-assisted reading is increasingly recognized as a viable alternative to traditional double reading. In Korea, adoption of AI-CAD is expanding, with systems approved by the Korean Food and Drug Administration currently in clinical use. Recently, one AI-CAD system received conditional non-reimbursement designation, allowing hospitals to use it for up to 5 years while collecting clinical evidence to support future insurance coverage decisions.Discussion and Conclusion: AI-CAD has significant potential to enhance early breast cancer detection while maintaining acceptable false-positive rates, making it a valuable adjunct in screening programs. Beyond improved detection, AI-CAD may optimize workflow efficiency by triaging cases and prioritizing high-risk examinations. However, its integration into clinical practice necessitates standardized guidelines, regulatory oversight, and further validation through large-scale prospective studies. As AI technology continues to advance, ongoing investigation into its role in personalized breast cancer screening is essential.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.