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.

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.

Increases of neutrophils and osteoclasts are pathological changes of periodontitis. RANKL is an osteoclast differentiation factor. The effect of periodontopathogen LPS on RANKL-expressing neutrophils has not been clarified yet. We evaluated numerical changes of RANKL-expressing neutrophils in air pouches of mice injected with LPSs of Fusobacterium nucleatum and Porphyromonas gingivalis. Mice with air pouches were assigned into saline (C)-, E. coli LPS- (Ec LPS)-, F. nucleatum LPS (Fn LPS)-, P. gingivalis LPS (Pg LPS)-, and Fn LPS and Pg LPS (Fn + Pg LPS)-injected groups. CD11b +Ly6G + neutrophils and CD11b +Ly6G+RANKL + neutrophils in blood and air pouch exudates were determined by flow cytometry. In blood, compared to the C group, the Fn LPS group showed increases of CD11b +Ly6G + neutrophils and CD11b +Ly6G +RANKL + neutrophils whereas the Pg LPS group showed no significant differences. These increases in the Fn LPS group were not different to those in the Ec LPS group. In exudates, Fn LPS and Pg LPS groups showed increases of CD11b +Ly6G + neutrophils and CD11b +Ly6G +RANKL + neutrophils compared to the C group. Increased levels in the Fn LPS group were not different to those in the Ec LPS group, but Pg LPS group was lower than those in the Ec LPS group. In blood and exudates, the Fn+ Pg LPS group showed no difference in levels of these neutrophils compared to the Ec LPS group. LPSs of F. nucleatum and P. gingivalis increased RANKL-expressing neutrophils although the degrees of increases were different. These suggest that periodontopathogen LPS can act as a stimulant to increase RANKL-expressing neutrophils.

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.

Increases of neutrophils and osteoclasts are pathological changes of periodontitis. RANKL is an osteoclast differentiation factor. The effect of periodontopathogen LPS on RANKL-expressing neutrophils has not been clarified yet. We evaluated numerical changes of RANKL-expressing neutrophils in air pouches of mice injected with LPSs of Fusobacterium nucleatum and Porphyromonas gingivalis. Mice with air pouches were assigned into saline (C)-, E. coli LPS- (Ec LPS)-, F. nucleatum LPS (Fn LPS)-, P. gingivalis LPS (Pg LPS)-, and Fn LPS and Pg LPS (Fn + Pg LPS)-injected groups. CD11b +Ly6G + neutrophils and CD11b +Ly6G+RANKL + neutrophils in blood and air pouch exudates were determined by flow cytometry. In blood, compared to the C group, the Fn LPS group showed increases of CD11b +Ly6G + neutrophils and CD11b +Ly6G +RANKL + neutrophils whereas the Pg LPS group showed no significant differences. These increases in the Fn LPS group were not different to those in the Ec LPS group. In exudates, Fn LPS and Pg LPS groups showed increases of CD11b +Ly6G + neutrophils and CD11b +Ly6G +RANKL + neutrophils compared to the C group. Increased levels in the Fn LPS group were not different to those in the Ec LPS group, but Pg LPS group was lower than those in the Ec LPS group. In blood and exudates, the Fn+ Pg LPS group showed no difference in levels of these neutrophils compared to the Ec LPS group. LPSs of F. nucleatum and P. gingivalis increased RANKL-expressing neutrophils although the degrees of increases were different. These suggest that periodontopathogen LPS can act as a stimulant to increase RANKL-expressing neutrophils.