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: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Background: Immunocompromised (IC) pediatric patients are at increased risk of severe acute respiratory syndrome coronavirus 2 infection, but the viral kinetics and seroimmunologic response in pediatric IC patients are not fully understood. Methods: From April to June 2022, a prospective cohort study was conducted. IC pediatric patients hospitalized for coronavirus disease 2019 (COVID-19) were enrolled. Serial saliva swab and serum specimens were subjected to reverse transcription polymerase chain reaction assays with mutation sequencing, viral culture, anti-spike-protein, anti-nucleocapsid antibody assays, plaque reduction neutralization test (PRNT) and multiplex cytokine assays. Results: Eleven IC children were evaluated. Their COVID-19 symptoms resolved promptly (median, 2.5 days; interquartile range, 2.0–4.3). Saliva swab specimens contained lower viral loads than nasopharyngeal swabs (P = 0.008). All cases were BA.2 infection, and 45.5% tested negative within 14 days by saliva swab from symptom onset. Eight (72.7%) showed a time-dependent increase in BA.2 PRNT titers, followed by rapid waning. Multiplex cytokine assays revealed that monocyte/macrophage activation and Th 1 responses were comparable to those of non-IC adults. Activation of interleukin (IL)-1Ra and IL-6 was brief, and IL-17A was suppressed. Activated interferon (IFN)-γ and IL-18/IL-1F4 signals were observed. Conclusion IC pediatric patients rapidly recovered from COVID-19 with low viral loads.Antibody response was limited, but cytokine analysis suggested an enhanced IFN-γ- and IL-18-mediated immune response without excessive activation of inflammatory cascades. To validate our observation, immune cell-based functional studies need to be conducted among IC and non-IC children.

Background: Immunocompromised (IC) pediatric patients are at increased risk of severe acute respiratory syndrome coronavirus 2 infection, but the viral kinetics and seroimmunologic response in pediatric IC patients are not fully understood. Methods: From April to June 2022, a prospective cohort study was conducted. IC pediatric patients hospitalized for coronavirus disease 2019 (COVID-19) were enrolled. Serial saliva swab and serum specimens were subjected to reverse transcription polymerase chain reaction assays with mutation sequencing, viral culture, anti-spike-protein, anti-nucleocapsid antibody assays, plaque reduction neutralization test (PRNT) and multiplex cytokine assays. Results: Eleven IC children were evaluated. Their COVID-19 symptoms resolved promptly (median, 2.5 days; interquartile range, 2.0–4.3). Saliva swab specimens contained lower viral loads than nasopharyngeal swabs (P = 0.008). All cases were BA.2 infection, and 45.5% tested negative within 14 days by saliva swab from symptom onset. Eight (72.7%) showed a time-dependent increase in BA.2 PRNT titers, followed by rapid waning. Multiplex cytokine assays revealed that monocyte/macrophage activation and Th 1 responses were comparable to those of non-IC adults. Activation of interleukin (IL)-1Ra and IL-6 was brief, and IL-17A was suppressed. Activated interferon (IFN)-γ and IL-18/IL-1F4 signals were observed. Conclusion IC pediatric patients rapidly recovered from COVID-19 with low viral loads.Antibody response was limited, but cytokine analysis suggested an enhanced IFN-γ- and IL-18-mediated immune response without excessive activation of inflammatory cascades. To validate our observation, immune cell-based functional studies need to be conducted among IC and non-IC children.

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: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

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: Immunocompromised (IC) pediatric patients are at increased risk of severe acute respiratory syndrome coronavirus 2 infection, but the viral kinetics and seroimmunologic response in pediatric IC patients are not fully understood. Methods: From April to June 2022, a prospective cohort study was conducted. IC pediatric patients hospitalized for coronavirus disease 2019 (COVID-19) were enrolled. Serial saliva swab and serum specimens were subjected to reverse transcription polymerase chain reaction assays with mutation sequencing, viral culture, anti-spike-protein, anti-nucleocapsid antibody assays, plaque reduction neutralization test (PRNT) and multiplex cytokine assays. Results: Eleven IC children were evaluated. Their COVID-19 symptoms resolved promptly (median, 2.5 days; interquartile range, 2.0–4.3). Saliva swab specimens contained lower viral loads than nasopharyngeal swabs (P = 0.008). All cases were BA.2 infection, and 45.5% tested negative within 14 days by saliva swab from symptom onset. Eight (72.7%) showed a time-dependent increase in BA.2 PRNT titers, followed by rapid waning. Multiplex cytokine assays revealed that monocyte/macrophage activation and Th 1 responses were comparable to those of non-IC adults. Activation of interleukin (IL)-1Ra and IL-6 was brief, and IL-17A was suppressed. Activated interferon (IFN)-γ and IL-18/IL-1F4 signals were observed. Conclusion IC pediatric patients rapidly recovered from COVID-19 with low viral loads.Antibody response was limited, but cytokine analysis suggested an enhanced IFN-γ- and IL-18-mediated immune response without excessive activation of inflammatory cascades. To validate our observation, immune cell-based functional studies need to be conducted among IC and non-IC children.