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.
Arrhythmias, Cardiac* ; Humans ; Retrospective Studies ; Stroke Volume ; Ventricular Function, Left*

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.
Biology ; Biomarkers ; Humans ; Lung Neoplasms ; Phenotype ; Population Characteristics ; Precision Medicine

Biomarkers ; Diagnosis ; Lung Neoplasms ; Lung ; Prognosis

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.

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.

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.

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.

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.
Central Nervous System ; Diffusion ; Glioblastoma ; Humans ; Lymphoma ; Male ; Necrosis