Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: This study aimed to examine general nurses' nursing leadership in patient care using focus group interviews. Methods: This study was conducted after obtaining approval from the ethics committee of a university.After completing a focus group interview with 13 general nurses working at a general hospital, we performed qualitative content analysis according to Kreuger's guidelines. Results: A total of 170 meaningful statement units of nursing leadership that appeared in the clinical experience of general nurses were extracted, and 10 final sub-themes and the three themes connecting them were derived. The themes derived were “leading patients into nursing,” “experiencing the power of growth,” and “facilitating situations that allow focus on patient care.” Conclusion This study helps in understanding the nursing leadership of general nurses in patient care. To encourage general nurses to exert their nursing leadership and grow as autonomous nurses, nursing educators must appropriately present the learning outcomes and content of nursing leadership. Additionally, in the clinical setting, organizational support is necessary to foster understanding and the demonstration of general nurses' nursing leadership.

Purpose: The aim was to develop guidelines for delegating nursing tasks among nurses in integrated nursing care wards. Methods: This was a methodological approach. Literature reviews were conducted on delegation policies and practices for nurses in Korea and other countries to explore the area of nursing delegation. Focus group interviews were performed with nurses to identify the strength and weakness of the delegation of nursing tasks in clinical practice, and qualitative content analysis was conducted based on the interview. Ten areas and 115 items were derived through these steps, and their validity was confirmed using the Delphi technique. Results: The delegation guidelines of nursing tasks consisted of nine domains, 21 sub-categories, and 101 items, including Nurses and nursing assistants' duties, the necessity of delegation, definition of terms, scope of delegation, considerations for delegation, procedure, characteristics, and principles of delegation, and educational content for delegation. Conclusion These guidelines can help nurses to make decisions about delegating nursing tasks according to the delegation procedure.Education on the delegation of nursing tasks is necessary for both nurses and nursing assistants. The guidelines developed in this study can serve as a standard for delegating nursing tasks to ensure patient safety.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

Purpose: The role of allogeneic stem cell transplantation (alloSCT) in multiple myeloma (MM) treatment remains controversial. We conducted a retrospective, multicenter, nationwide study in Korea to evaluate the outcomes of alloSCT in Asian patients with MM. Materials and Methods: Overall, 109 patients with MM who underwent alloSCT between 2003 and 2020 were included in this study. Data were collected from the Korean Multiple Myeloma Working Party Registry. Results: The overall response rate and stringent complete response plus complete response (CR) rates were 67.0 and 46.8%, respectively, after alloSCT. At a median follow-up of 32.5 months, the 3-year probability of progression-free survival (PFS) and overall survival (OS) rates were 69.3% and 71.8%, respectively. The 3-year probabilities of OS rates in the upfront alloSCT, tandem auto-alloSCT, and later alloSCT groups were 75.0%, 88.9%, and 61.1%, respectively. Patients who achieved CR before or after alloSCT had significantly longer OS (89.8 vs. 18 months and 89.8 vs. 15.2 months, respectively). Even though patients who did not achieve CR prior to alloSCT, those who achieve CR after alloSCT had improved PFS and OS compared to those who had no achievement of CR both prior and after alloSCT. Patients who underwent alloSCT with 1-2 prior treatment lines had improved PFS (22.4 vs. 4.5 months) and OS (45.6 vs. 15.3 months) compared to those with three or more prior treatment lines. Conclusion AlloSCT may be a promising therapeutic option especially for younger, chemosensitive patients with earlier implementation from relapse.

Background: Postpartum depression (PPD) can negatively affect infant well-being and child development. Although the frequency and risk factors of PPD symptoms might vary depending on the country and culture, there is limited research on these risk factors among Korean women. This study aimed to elucidate the potential risk factors of PPD throughout pregnancy to help improve PPD screening and prevention in Korean women. Methods: The pregnant women at 12 gestational weeks (GW) were enrolled from two obstetric specialized hospitals from March 2013 to November 2017. A questionnaire survey was administered at 12 GW, 24 GW, 36 GW, and 4 weeks postpartum. Depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale, and PPD was defined as a score of ≥ 10. Results: PPD was prevalent in 16.3% (410/2,512) of the participants. Depressive feeling at 12 GW and postpartum factors of stress, relationship with children, depressive feeling, fear, sadness, and neonatal intensive care unit admission of baby were significantly associated with a higher risk of PPD. Meanwhile, high postpartum quality of life and marital satisfaction at postpartum period were significantly associated with a lower risk of PPD. We developed a model for predicting PPD using factors as mentioned above and it had an area under the curve of 0.871. Conclusion Depressive feeling at 12 GW and postpartum stress, fear, sadness, relationship with children, low quality of life, and low marital satisfaction increased the risk of PPD. A risk model that comprises significant factors can effectively predict PPD and can be helpful for its prevention and appropriate treatment.