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.

Objectives: . The presence of extrathyroidal extension (ETE) in patients with differentiated thyroid cancer (DTC) serves as a significant prognostic indicator. Consequently, the staging of DTC is categorized into extensive ETE and gross ETE that solely impacts the strap muscles (gross strap muscle invasion [gSMI]). However, there is a lack of sufficient evidence concerning the relationship between gSMI and prognosis, particularly in terms of tumor size. Methods: . Relevant literature was searched in Medline, Embase, Cochrane Library, and KoreaMed. All procedures were conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and carried out by two independent reviewers. The meta-analysis utilized a random-effects model to account for the diversity of the studies. Risk of Bias for Nonrandomized Studies (RoBANS) version 2.0, an evaluation tool for non-randomized studies, was employed to assess the quality of the selected research. Clinical data from observational studies that examined the relationship between the degree of ETE and prognosis were gathered, and a meta-analysis was conducted. Results: . Eighteen observational studies were included in this analysis. Subgroup analyses were conducted for each outcome. The findings revealed that the recurrence rate (odds ratio [OR], 2.498), disease-specific mortality (risk ratio [RR], 2.984), overall mortality (RR, 1.361), and lymph node (LN) metastasis (OR, 5.355) were significantly higher in patients with gSMI than in those without ETE. However, when the analysis was restricted to tumors measuring 4 cm or smaller, no significant differences in prognostic outcomes were observed, with the exception of LN metastasis. Conclusion . gSMI negatively impacts prognosis; however, this correlation diminishes with smaller tumor sizes. Thus, a more cautious approach is warranted during the treatment process.

A 4-month-old intact male Sapsaree dog was referred due to a history of postprandial regurgitation following consumption of solid food. Thoracic radiography revealed focal leftward displacement of the thoracic trachea at T1 to T4 vertebrae levels. Barium contrast radiography revealed focal dilation of the cranial thoracic esophagus at the heart base level. Persistent right aortic arch (PRAA) with an aberrant left subclavian artery branching from the patent ductus arteriosus was diagnosed by computed tomography angiography (CTA). Although barium contrast radiography can presumptive diagnose PRAA, CTA should be considered for identifying additional vascular anomalies, specific types, and surgical planning.

BACKGROUND/OBJECTIVES: The umami taste receptor (TAS1R1/TAS1R3) is endogenously expressed in skeletal muscle and is involved in myogenesis; however, there is a lack of evidence about whether the expression of the umami taste receptor is involved in muscular diseases. This study aimed to elucidate the effects of the umami taste receptor and its mechanism on muscle wasting in cancer cachexia using in vivo and in vitro models.MATERIALS/METHODS: The Lewis lung carcinoma-induced cancer cachexia model was used in vivo and in vitro, and the expressions of umami taste receptor and muscle atrophy-related markers, muscle atrophy F-box protein, and muscle RING-finger protein-1 were analyzed. RESULTS: Results showed that TAS1R1 was significantly downregulated in vivo and in vitro under the muscle wasting condition. Moreover, overexpression of TAS1R1 in vitro in the human primary cell model protected the cells from muscle atrophy, and knockdown of TAS1R1 using siRNA exacerbated muscle atrophy. CONCLUSION Taken together, the umami taste receptor exerts protective effects on muscle-wasting conditions by restoring dysregulated muscle atrophy in cancer cachexia. In conclusion, this result provided evidence that the umami taste receptor exerts a therapeutic anti-cancer cachexia effect by restoring muscle atrophy.

Objective: This study explored whether temperament profiles are associated with psychological functioning and whether character maturity affects this association in patients with panic disorders (PD). Methods: A total of 270 patients with PD were enrolled in this study. Measurements included the Temperament and Character Inventory-revised-short (TCI-RS), a self-report version of the Panic Disorder Severity Scale (PDSS-SR), Beck Depression Inventory-II (BDI-II), and Spielberger State-Trait Anxiety Inventory (STAI). Cluster analysis was used to define the patients’ temperament profiles, and the differences in discrete variables among temperament clusters were calculated using a one-way analysis of variance. An analysis of covariance was conducted to control for the impact of character maturity on psychological functioning among clusters. Results: We identified four temperament clusters of patients with PD. Significant differences in the PDSS-SR, BDI-II, STAI-state, and STAI-trait scores among the four clusters were detected [F(3, 262)=9.16, p<0.001; F(3, 266)=33.78, p<0.001; F(3, 266)=19.12, p<0.001; F(3, 266)=39.46, p<0.001]. However, after controlling for the effect of character maturity, the effect of cluster type was either eliminated or reduced ([STAI-state] cluster type: F(3, 262)=0.94, p>0.05; SD+CO: F(1, 262)=65.95, p<0.001, ηp2 =0.20). Conclusion This study enabled a more comprehensive and integrated understanding of patients by exploring the configuration of all temperament dimensions together rather than each temperament separately. Furthermore, we revealed that depending on the degree of character maturity, the psychological functioning might differ even within the same temperament cluster. These results imply that character maturity can complement inherently vulnerable temperament expression.