Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Background: This study aimed to determine the skeletal and dental effects in pediatric and adolescent Korean patients with ClassII Division 1 malocclusion treated using the Invisalign Mandibular Advancement (MAⓇ ) appliance. Methods: The study included patients aged 6 to 18 years who received orthodontic treatment with the MAⓇ appliance for Class II Division 1 malocclusion at the Department of Pediatric Dentistry, Wonkwnag University Daejeon Dental Hospital, between July 1, 2018, and December 31, 2021. The treatment group consisted of 20 patients, 10 boys and 10 girls. The control participants were also 10 boys and 10 girls. Lateral cephalometric radiographs were taken before and after treatment, and 41 measurements of skeletal and dental changes were measured and analyzed using the V-CephTM 8.0 (Osstem Implant). All analyses were performed using SPSS software (IBM SPSS for Windows, ver 26.0; IBM Corp.), and statistical significance was tested using paired and independent samples t-tests for within-group and between-group comparisons, respectively. Results: The patients in the treatment group showed significant decreases in ANB (A point, Nasion, B point), maxillary protrusion,maxillary anterior incisor labial inclination, and maxillary protrusion after treatment. However, when compared with the growth changes observed in the control group, only ANB and maxillary protrusion decreased, with no significant differences in SNA, SNB, and mandibular length. Conclusion Collectively, the results of this study confirm that the use of MAⓇ appliance in pediatric and adolescent Korean patients with Class II Division 1 malocclusion results in a reduction of anteroposterior skeletal and dental disharmony.

Thoracic foreign bodies (FBs) are serious and relatively frequent in emergency departments. Thoracic FBs may occur in association with aspiration, ingestion, trauma, or iatrogenic causes. Imaging plays an important role in the identification of FBs and their dimensions, structures, and locations, before the initiation of interventional treatment. To guide proper clinical management, radiologists should be aware of the radiologic presentations and the consequences of thoracic FBs. In this pictorial essay, we reviewed the optimal imaging settings to identify FBs in the thorax, classified thoracic FBs into four types according to their etiology, and reviewed the characteristic imaging features and the possible complications.

Background: Although patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD.Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding. Methods: To determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD. Results: Cytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL, P = 0.611). The emphysema index was significantly correlated with the serum Cyfra 21-1 level (correlation coefficient = 0.219,P = 0.015). Conclusion Word embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening.Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

Background: Although patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD.Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding. Methods: To determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD. Results: Cytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL, P = 0.611). The emphysema index was significantly correlated with the serum Cyfra 21-1 level (correlation coefficient = 0.219,P = 0.015). Conclusion Word embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.

Biomarkers ; Diagnosis ; Lung Neoplasms ; Lung ; Prognosis