Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Background: and Purpose The pain lateralization in cluster headache (CH) may be related to the asymmetry in the functions of the brain hemispheres. The right-sided dominance of pain in CH has been found inconsistently across studies, and so we aimed to characterize this and identify the factors influencing pain lateralization during current and previous bouts. Methods: This study enrolled 227 patients from the Korean Cluster Headache Registry between October 2018 and December 2020. We evaluated the side of pain during current and previous bouts, demographic features, and clinical characteristics, including handedness. Multivariable logistic regression analyses were performed to identify factors associated with the side of pain. Results: The 227 patients with CH included 131 (57.7%) with right-sided pain and 86 (37.9%) with left-sided pain during the current bout (p<0.001). The 189 patients with previous bouts of CH included 86.8% who consistently reported the same side of pain throughout multiple bouts (side-locked pain), with a higher prevalence of pain on the right than the left side (55.0% vs. 31.7%, p<0.001). Multivariable analyses revealed that higher age at diagnosis (odds ratio [OR]=1.045, p=0.031) and shorter CH attacks (OR=0.992, p=0.017) were associated with left-side-locked pain. However, handedness was not associated with the lateralization of leftside-locked pain. Conclusions This study has confirmed the predominance of right-sided pain throughout multiple CH bouts. We found that higher age at diagnosis and shorter CH attacks were associated with left-side-locked pain, suggesting that certain clinical factors are associated with the pain laterality. However, the underlying mechanisms linking these factors to lateralized pain remain unclear and therefore require further investigation.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Background: and Purpose The pain lateralization in cluster headache (CH) may be related to the asymmetry in the functions of the brain hemispheres. The right-sided dominance of pain in CH has been found inconsistently across studies, and so we aimed to characterize this and identify the factors influencing pain lateralization during current and previous bouts. Methods: This study enrolled 227 patients from the Korean Cluster Headache Registry between October 2018 and December 2020. We evaluated the side of pain during current and previous bouts, demographic features, and clinical characteristics, including handedness. Multivariable logistic regression analyses were performed to identify factors associated with the side of pain. Results: The 227 patients with CH included 131 (57.7%) with right-sided pain and 86 (37.9%) with left-sided pain during the current bout (p<0.001). The 189 patients with previous bouts of CH included 86.8% who consistently reported the same side of pain throughout multiple bouts (side-locked pain), with a higher prevalence of pain on the right than the left side (55.0% vs. 31.7%, p<0.001). Multivariable analyses revealed that higher age at diagnosis (odds ratio [OR]=1.045, p=0.031) and shorter CH attacks (OR=0.992, p=0.017) were associated with left-side-locked pain. However, handedness was not associated with the lateralization of leftside-locked pain. Conclusions This study has confirmed the predominance of right-sided pain throughout multiple CH bouts. We found that higher age at diagnosis and shorter CH attacks were associated with left-side-locked pain, suggesting that certain clinical factors are associated with the pain laterality. However, the underlying mechanisms linking these factors to lateralized pain remain unclear and therefore require further investigation.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Background: and Purpose The pain lateralization in cluster headache (CH) may be related to the asymmetry in the functions of the brain hemispheres. The right-sided dominance of pain in CH has been found inconsistently across studies, and so we aimed to characterize this and identify the factors influencing pain lateralization during current and previous bouts. Methods: This study enrolled 227 patients from the Korean Cluster Headache Registry between October 2018 and December 2020. We evaluated the side of pain during current and previous bouts, demographic features, and clinical characteristics, including handedness. Multivariable logistic regression analyses were performed to identify factors associated with the side of pain. Results: The 227 patients with CH included 131 (57.7%) with right-sided pain and 86 (37.9%) with left-sided pain during the current bout (p<0.001). The 189 patients with previous bouts of CH included 86.8% who consistently reported the same side of pain throughout multiple bouts (side-locked pain), with a higher prevalence of pain on the right than the left side (55.0% vs. 31.7%, p<0.001). Multivariable analyses revealed that higher age at diagnosis (odds ratio [OR]=1.045, p=0.031) and shorter CH attacks (OR=0.992, p=0.017) were associated with left-side-locked pain. However, handedness was not associated with the lateralization of leftside-locked pain. Conclusions This study has confirmed the predominance of right-sided pain throughout multiple CH bouts. We found that higher age at diagnosis and shorter CH attacks were associated with left-side-locked pain, suggesting that certain clinical factors are associated with the pain laterality. However, the underlying mechanisms linking these factors to lateralized pain remain unclear and therefore require further investigation.

Objective: To investigate the impact of circadian rhythm disruptions on mental health among college students and explore effective interventions for maintaining stable circadian rhythms. Methods: A comprehensive review of literature was conducted, focusing on sleep patterns, circadian rhythms, and their effects on mental health. Studies were analyzed to identify common factors contributing to circadian misalignment in college students and effective treatments. Data from large-scale studies and specific clinical trials were utilized to understand the relationship between circadian rhythms and psychiatric disorders. Results: Disruptions in circadian rhythms were linked to increased prevalence of psychiatric disorders such as depression, anxiety, and bipolar disorder. Biological changes during adolescence, academic pressures, and extensive use of electronic devices were major contributing factors. Effective interventions included light therapy, chronotherapy, melatonin supplementation, and cognitive behavioral therapy for insomnia. Conclusion Stable circadian rhythms are crucial for mental health, particularly in college students who are vulnerable to disruptions due to lifestyle factors. Implementing interventions such as regular sleep schedules, light exposure management, and behavioral therapies can significantly improve mental health outcomes. Further research and targeted mental health programs are essential to address circadian misalignment and its associated psychiatric disorders in this population.

Objective: To investigate the impact of circadian rhythm disruptions on mental health among college students and explore effective interventions for maintaining stable circadian rhythms. Methods: A comprehensive review of literature was conducted, focusing on sleep patterns, circadian rhythms, and their effects on mental health. Studies were analyzed to identify common factors contributing to circadian misalignment in college students and effective treatments. Data from large-scale studies and specific clinical trials were utilized to understand the relationship between circadian rhythms and psychiatric disorders. Results: Disruptions in circadian rhythms were linked to increased prevalence of psychiatric disorders such as depression, anxiety, and bipolar disorder. Biological changes during adolescence, academic pressures, and extensive use of electronic devices were major contributing factors. Effective interventions included light therapy, chronotherapy, melatonin supplementation, and cognitive behavioral therapy for insomnia. Conclusion Stable circadian rhythms are crucial for mental health, particularly in college students who are vulnerable to disruptions due to lifestyle factors. Implementing interventions such as regular sleep schedules, light exposure management, and behavioral therapies can significantly improve mental health outcomes. Further research and targeted mental health programs are essential to address circadian misalignment and its associated psychiatric disorders in this population.

Objective: To investigate the impact of circadian rhythm disruptions on mental health among college students and explore effective interventions for maintaining stable circadian rhythms. Methods: A comprehensive review of literature was conducted, focusing on sleep patterns, circadian rhythms, and their effects on mental health. Studies were analyzed to identify common factors contributing to circadian misalignment in college students and effective treatments. Data from large-scale studies and specific clinical trials were utilized to understand the relationship between circadian rhythms and psychiatric disorders. Results: Disruptions in circadian rhythms were linked to increased prevalence of psychiatric disorders such as depression, anxiety, and bipolar disorder. Biological changes during adolescence, academic pressures, and extensive use of electronic devices were major contributing factors. Effective interventions included light therapy, chronotherapy, melatonin supplementation, and cognitive behavioral therapy for insomnia. Conclusion Stable circadian rhythms are crucial for mental health, particularly in college students who are vulnerable to disruptions due to lifestyle factors. Implementing interventions such as regular sleep schedules, light exposure management, and behavioral therapies can significantly improve mental health outcomes. Further research and targeted mental health programs are essential to address circadian misalignment and its associated psychiatric disorders in this population.

Objective: To investigate the impact of circadian rhythm disruptions on mental health among college students and explore effective interventions for maintaining stable circadian rhythms. Methods: A comprehensive review of literature was conducted, focusing on sleep patterns, circadian rhythms, and their effects on mental health. Studies were analyzed to identify common factors contributing to circadian misalignment in college students and effective treatments. Data from large-scale studies and specific clinical trials were utilized to understand the relationship between circadian rhythms and psychiatric disorders. Results: Disruptions in circadian rhythms were linked to increased prevalence of psychiatric disorders such as depression, anxiety, and bipolar disorder. Biological changes during adolescence, academic pressures, and extensive use of electronic devices were major contributing factors. Effective interventions included light therapy, chronotherapy, melatonin supplementation, and cognitive behavioral therapy for insomnia. Conclusion Stable circadian rhythms are crucial for mental health, particularly in college students who are vulnerable to disruptions due to lifestyle factors. Implementing interventions such as regular sleep schedules, light exposure management, and behavioral therapies can significantly improve mental health outcomes. Further research and targeted mental health programs are essential to address circadian misalignment and its associated psychiatric disorders in this population.