A Pictorial Review of Radiologic Findings of Foreign Bodies in the Thorax

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.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

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.

Word Embedding Reveals Cyfra 21-1 as a Biomarker for Chronic Obstructive Pulmonary Disease

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.

Word Embedding Reveals Cyfra 21-1 as a Biomarker for Chronic Obstructive Pulmonary Disease

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.

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019

Background@#The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19. @*Methods@#The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital. @*Results@#The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11–97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit.Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs. @*Conclusion@#The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.

CT Radiomics in Thoracic Oncology: Technique and Clinical Applications / 대한핵의학회잡지

Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.

CT Radiomics in Thoracic Oncology: Technique and Clinical Applications / 대한핵의학회잡지

Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.

Assessment of Left Ventricular Function with Single Breath-Hold Magnetic Resonance Cine Imaging in Patients with Arrhythmia

PURPOSE: To evaluate quantification results of single breath-hold (SBH) magnetic resonance (MR) cine imaging compared to results of conventional multiple breath-hold (MBH) technique for left ventricular (LV) function in patients with cardiac arrhythmia. MATERIALS AND METHODS: MR images of patients with arrhythmia who underwent MBH and SBH cine imaging at the same time on a 1.5T MR scanner were retrospectively reviewed. Both SBH and MBH cine imaging were performed with balanced steady state free precession. SBH scans were acquired using temporal parallel acquisition technique (TPAT). Fifty patients (65.4 ± 12.3 years, 72% men) were included. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), myocardial mass, and LV regional wall motion were evaluated. RESULTS: EF, myocardial mass, and regional wall motion were not significantly different between SBH and MBH acquisition techniques (all P-values > 0.05). EDV, ESV, and SV were significant difference between the two techniques. These parameters for SBH cine imaging with TPAT tended to lower than those in MBH. EF and myocardial mass of SBH cine imaging with TPAT showed good correlation with values of MBH cine imaging in Passing-Bablok regression charts and Bland-Altman plots. However, SBH imaging required significantly shorter acquisition time than MBH cine imaging (15 ± 7 sec vs. 293 ± 104 sec, P < 0.001). CONCLUSION: SBH cine imaging with TPAT permits shorter acquisition time with assessment results of global and regional LV function comparable to those with MBH cine imaging in patients with arrhythmia.

Differentitation between Primary Central Nervous System Lymphoma and Glioblastoma: Added Value of Quantitative Analysis of CT Attenuation and Apparent Diffusion Coefficient

PURPOSE: Purpose of this study was to determine if quantitative measures of CT attenuation and ADC values in combination with conventional imaging features can differentiate primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). MATERIALS AND METHODS: Twenty-six patients with histologically-proven GBM (14 men and 12 women; median age, 50 years; age range, 22 - 73 years) and 14 patients with PCNSL (11 men and 3 women; median age, 61 years; age range, 41 - 74 years) were enrolled. Maximum CT attenuation, minimum ADC, and lesion to normal parenchyma minimum ADC ratios were measured in solid tumor regions. Conventional imaging features were evaluated for the following: ill-defined margin, homogeneous enhancement pattern, degree of necrosis, extent of tumor involvement and multiplicity. The Mann-Whitney test was used to compare maximum CT attenuation and minimum ADC values for PCNSL and GBM. Fisher's exact test was used to evaluate relationships between pathologic diagnoses and imaging features. RESULTS: The CT attenuations were similar for PCNSL and GBM (37.84 +/- 6.90 HU versus 37.00 +/- 5.54 HU, p = 0.68), but minimum ADC and minimum ADC ratio were significant lower in PCNSL than in GBM (595.01 +/- 228.28 10(-6) mm2/s versus 736.52 +/- 162.05 10(-6) mm2/s; p = 0.028, 0.87 +/- 0.26 versus 1.14 +/- 0.29; p = 0.007). PCNSL showed greater homogeneous enhancement and smaller necrotic areas than GBM (p = 0.003 and p < 0.001, respectively) and was more likely to have multiple tumors than GBM (p = 0.039). When necrotic PCNSL (n = 4) and necrotic GBM (n = 24) were compared, minimum ADC and minimum ADC ratios were also significantly lower in PCNSL, but CT attenuation were not. CONCLUSION: Although CT attenuation does not provide valuable information, minimum ADC and minimum ADC ratio and some imaging features can aid the differentiation of PCNSL and GBM.