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.

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.

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.

Objective: Numerous studies have identified hemodynamic changes in adults with major depressive disorder (MDD) by using functional near-infrared spectroscopy (fNIRS). However, studies on adolescents with MDD are limited. As adolescence is a stage of rapid brain development, differences may occur depending on age. This study used fNIRS as an objective tool to investigate hemodynamic changes in the frontal lobe according to depression severity and age in adolescents with MDD. Methods: Thirty adolescents (12 aged 12−15 years and 18 aged 16−18 years) were retrospectively investigated. The Children’s Depression Inventory was used as a psychiatric evaluation scale, fNIRS was used as an objective brain function evaluation tool, and the Verbal Fluency Test was performed. Results: During the Verbal Fluency Test, in the younger MDD group, oxygenated-hemoglobin concentration increased in the right dorsolateral prefrontal cortex region as the severity of depression increased. In the older MDD group, the oxygenated-hemoglobin concentration decreased in the right dorsolateral prefrontal cortex region as the severity of depression increased. Conclusion These results suggest that fNIRS may be an objective tool for identifying age differences among adolescents with MDD. To generalize the results and verify fNIRS as a potential biomarker tool, follow-up studies with a larger sample group should be conducted.

In the field of orthopedics, deep vein thrombosis (DVT) is a rare but serious condition that can be life-threatening once it occurs. DVT is most commonly reported after hip and knee arthroplasty or multiple traumas, but rarely cases in the foot and ankle regions have been encountered. This paper reports a case of DVT that occurred after ankle ligament repair surgery with a review of relevant literature.

As fluoroscopy-guided interventional procedures gain popularity, the associated health threats from radiation exposure to interventionalists during these procedures are increasing. Therefore, an understanding of the potential risks of radiation and careful consideration on minimizing exposure to radiation during the procedures are of paramount importance. The Korean Pancreatobiliary Association has developed a clinical practice guideline to minimize radiation exposure during fluoroscopy-guided interventional procedures. This guideline provides recommendations to deal with the risk of radiation exposure to interventionalists who perform fluoroscopy-guided procedures, and emphasizes the importance of proper and practical approaches to avoid unnecessary radiation exposure.

Background: s/Aims: The hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) is classified as the advanced stage (BCLC stage C) with extremely poor prognosis, and in current guidelines is recommended for systemic therapy. This study aimed to evaluate the surgical outcomes and long-term prognosis after hepatic resection (HR) for patients who have HCC combined with PVTT. Methods: We retrospectively analyzed 332 patients who underwent HR for HCC with PVTT at ten tertiary referral hospitals in South Korea. Results: The median overall and recurrence-free survival after HR were 32.4 and 8.6 months, while the 1-, 3-, and 5-year overall survival rates were 75%, 48%, and 39%, respectively. In multivariate analysis, tumor number, tumor size, AFP, PIVKA−II, neutrophil-to-lymphocyte ratio, and albumin–bilirubin (ALBI) grade were significant prognostic factors. The risk scoring was developed using these seven factors–tumor, inflammation and hepatic function (TIF), to predict patient prognosis. The prognosis of the patients was well stratified according to the scores (log-rank test, p < 0.001). Conclusions HR for patients who have HCC combined with PVTT provided favorable survival outcomes. The risk scoring was useful in predicting prognosis, and determining the appropriate treatment strategy for those patients who have HCC with PVTT.