PURPOSE: This study was designed too identify the MR histoanatomical distribution of soft-tissue tumors. MATERIALS AND METHODS: A total of 290 soft-tissue tumors of 281 patients were analyzed by the use of MR imaging and were pathologically confirmed after surgical resection or a biopsy. There were 120 malignant soft-tissue tumors including tumors of an intermediate malignancy and 170 benign tumors. The histoanatomical locations were divided into three types: 'Type I' with superficial layer tumors that involved the cutaneous and subcutaneous tissue, 'Type II' with deep layer tumors that involved the muscle or tendon and 'Type III' with soft-tissue tumors that involved both the superficial and deep layers. RESULTS: Soft-tissue tumors with more than three cases with a frequency of more than 75% included dermatofibrosarcoma protuberans, glomus tumor, angiolipoma, leiomyosarcoma and lymphoma as 'Type I' tumors. 'Type II' tumors with more than three cases with a frequency of more than 75% included liposarcoma, fibromatosis, papillary endothelial hyperplasia and rhabdomyosarcoma. 'Type III' tumors with more than three cases with a frequency of more than 50% included neurofibromatosis. CONCLUSION: The MR histoanatomical distributions of soft tissue tumors are useful in the differential pathological diagnosis when a soft-tissue tumor has a nonspecific MR appearance.
Angiolipoma ; Biopsy ; Dermatofibrosarcoma ; Fibroma ; Glomus Tumor ; Humans ; Hyperplasia ; Leiomyosarcoma ; Liposarcoma ; Lymphoma ; Muscles ; Rhabdomyosarcoma ; Soft Tissue Neoplasms ; Subcutaneous Tissue ; Tendons

Biomarkers ; Diagnosis ; Lung Neoplasms ; Lung ; Prognosis

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

PURPOSE: We compared the outcomes of percutaneous transthoracic needle aspiration biopsy (PCNA) of lung masses in cases with and without prior positron emission tomography/computed tomography (PET/CT) information, and investigated the factors associated with false-negative pathological results. MATERIALS AND METHODS: From a total of 291 patients, 161 underwent PCNA without prior PET/CT imaging, while 130 underwent PET/CT before PCNA. Clinical characteristics, procedural variables, pathological results, and diagnostic success rates were compared between the 2 groups. Among patients with initial negative (non-specific benign) PCNA results, the radiological findings of these groups were compared to evaluate the predictors of false-negative lesions. RESULTS: No significant difference was found in the clinical characteristics, procedural characteristics, and pathological results of the 2 groups, nor was the diagnostic rate significantly different between them (p = 0.818). Among patients with initial negative PCNA results, radiological characteristics were similar in both the groups. In multivariate analysis, the presence of necrosis (p = 0.005) and ground-glass opacity (GGO) (p = 0.011) were the significant characteristics that indicated an increased probability of initial false-negative results in PCNA. CONCLUSION Routine PET/CT did not have any additional benefit in patients undergoing PCNA of lung masses. The presence of necrosis or GGO could indicate an increased probability of false-negative pathological results.

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Conclusion Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Conclusion Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Conclusion Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Materials and Methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Conclusion Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

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.

OBJECTIVE: To evaluate the utility of cardiovascular magnetic resonance (CMR)-derived myocardial strain measurement for the prediction of poor outcomes in patients with acute myocarditis. MATERIALS AND METHODS: We retrospectively analyzed data from 37 patients with acute myocarditis who underwent CMR. Left ventricular (LV) size, LV mass index, ejection fraction and presence of myocardial late gadolinium enhancement (LGE) were analyzed. LV circumferential strain (Ecc(SAX)), radial strain (Err(SAX)) from mid-ventricular level short-axis cine views and LV longitudinal strain (Ell(LV)), radial strain (Err(Lax)) measurements from 2-chamber long-axis views were obtained. In total, 31 of 37 patients (83.8%) underwent follow-up echocardiography. The primary outcome was major adverse cardiovascular event (MACE). Incomplete LV functional recovery was a secondary outcome. RESULTS: During an average follow-up of 41 months, 11 of 37 patients (29.7%) experienced MACE. Multivariable Cox proportional hazard regression analysis, which included LV mass index, LV ejection fraction, the presence of LGE, Ecc(SAX), Err(SAX), Ell(LV), and Err(Lax) values, indicated that the presence of LGE (hazard ratio, 42.88; p = 0.014), together with ErrLax (hazard ratio, 0.77 per 1%, p = 0.004), was a significant predictor of MACE. Kaplan-Meier analysis demonstrated worse outcomes in patient with LGE and an Err(Lax) value ≤ 9.48%. Multivariable backward regression analysis revealed that Err(Lax) values were the only significant predictors of LV functional recovery (hazard ratio, 0.54 per 1%; p = 0.042). CONCLUSION: CMR-derived Err(Lax) values can predict poor outcomes, both MACE and incomplete LV functional recovery, in patients with acute myocarditis, while LGE is only a predictor of MACE.
Echocardiography ; Follow-Up Studies ; Gadolinium ; Heart Ventricles ; Humans ; Kaplan-Meier Estimate ; Magnetic Resonance Imaging ; Myocarditis* ; Retrospective Studies ; Ventricular Dysfunction