Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.
Humans ; Retrospective Studies ; Solitary Pulmonary Nodule/diagnostic imaging* ; Radiography ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/diagnostic imaging* ; Sensitivity and Specificity ; Radiographic Image Interpretation, Computer-Assisted/methods*

Humans ; Lung ; Pneumoconiosis/diagnostic imaging* ; Radiography ; Tomography, X-Ray Computed

INTRODUCTION: Chest radiographs (CXRs) are widely used for the screening and management of COVID-19. This article describes the radiographic features of COVID-19 based on an initial national cohort of patients. METHODS: This is a retrospective review of swab-positive patients with COVID-19 who were admitted to four different hospitals in Singapore between 22 January and 9 March 2020. Initial and follow-up CXRs were reviewed by three experienced radiologists to identify the predominant pattern and distribution of lung parenchymal abnormalities. RESULTS: In total, 347 CXRs of 96 patients were reviewed. Initial CXRs were abnormal in 41 (42.7%) out of 96 patients. The mean time from onset of symptoms to CXR abnormality was 5.3 ± 4.7 days. The predominant pattern of lung abnormality was ground-glass opacity on initial CXRs (51.2%) and consolidation on follow-up CXRs (51.0%). Multifocal bilateral abnormalities in mixed central and peripheral distribution were observed in 63.4% and 59.2% of abnormal initial and follow-up CXRs, respectively. The lower zones were involved in 90.2% of initial CXRs and 93.9% of follow-up CXRs. CONCLUSION In a cohort of swab-positive patients, including those identified from contact tracing, we found a lower incidence of CXR abnormalities than was previously reported. The most common pattern was ground-glass opacity or consolidation, but mixed central and peripheral involvement was more common than peripheral involvement alone.
COVID-19 ; Humans ; Lung/diagnostic imaging* ; Radiography, Thoracic ; Retrospective Studies ; SARS-CoV-2 ; Singapore

OBJECTIVE: This study presents a preliminary report on the chest radiographic and computed tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) pneumonia in Korea.MATERIALS AND METHODS: As part of a multi-institutional collaboration coordinated by the Korean Society of Thoracic Radiology, we collected nine patients with COVID-19 infections who had undergone chest radiography and CT scans. We analyzed the radiographic and CT findings of COVID-19 pneumonia at baseline. Fisher's exact test was used to compare CT findings depending on the shape of pulmonary lesions.RESULTS: Three of the nine patients (33.3%) had parenchymal abnormalities detected by chest radiography, and most of the abnormalities were peripheral consolidations. Chest CT images showed bilateral involvement in eight of the nine patients, and a unilobar reversed halo sign in the other patient. In total, 77 pulmonary lesions were found, including patchy lesions (39%), large confluent lesions (13%), and small nodular lesions (48%). The peripheral and posterior lung fields were involved in 78% and 67% of the lesions, respectively. The lesions were typically ill-defined and were composed of mixed ground-glass opacities and consolidation or pure ground-glass opacities. Patchy to confluent lesions were primarily distributed in the lower lobes (p = 0.040) and along the pleura (p < 0.001), whereas nodular lesions were primarily distributed along the bronchovascular bundles (p = 0.006).CONCLUSION: COVID-19 pneumonia in Korea primarily manifested as pure to mixed ground-glass opacities with a patchy to confluent or nodular shape in the bilateral peripheral posterior lungs. A considerable proportion of patients with COVID-19 pneumonia had normal chest radiographs.
Cooperative Behavior ; Coronavirus ; Humans ; Korea ; Lung ; Pleura ; Pneumonia ; Radiography ; Radiography, Thoracic ; Thorax ; Tomography, X-Ray Computed

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 and was declared a global pandemic by the World Health Organization on 11 March 2020. A definitive diagnosis of COVID-19 is made after a positive result is obtained on reverse transcription-polymerase chain reaction assay. In Singapore, rigorous contact tracing was practised to contain the spread of the virus. Nasal swabs and chest radiographs (CXR) were also taken from individuals who were suspected to be infected by COVID-19 upon their arrival at a centralised screening centre. From our experience, about 40% of patients who tested positive for COVID-19 had initial CXR that appeared "normal". In this case series, we described the temporal evolution of COVID-19 in patients with an initial "normal" CXR. Since CXR has limited sensitivity and specificity in COVID-19, it is not suitable as a first-line diagnostic tool. However, when CXR changes become unequivocally abnormal, close monitoring is recommended to manage potentially severe COVID-19 pneumonia.
Adult ; Betacoronavirus ; Clinical Laboratory Techniques ; Coronavirus Infections ; complications ; diagnosis ; diagnostic imaging ; Female ; Humans ; Lung ; diagnostic imaging ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral ; complications ; diagnostic imaging ; Radiography ; Sensitivity and Specificity

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
Animals ; Area Under Curve ; Cats ; Classification ; Dataset ; Dogs ; Fourier Analysis ; Fractals ; Lung ; Machine Learning ; Neural Networks (Computer) ; Pattern Recognition, Visual ; Radiography, Thoracic ; Residence Characteristics ; ROC Curve

BACKGROUND: Vitamin D deficiency is associated with an increased risk of pulmonary tuberculosis (PTB) infection and the treatment outcome. The aim of this study was to examine the relationship between the serum 25-hydroxyvitamin D (25[OH]D) level and lung function in Korean adults according to whether or not there is a history of PTB. METHODS: The data for subjects aged 19 years or older from the Korea National Health and Nutrition Examination Survey 2008–2012 who underwent spirometry, chest radiography, and serum 25(OH)D level measurement were analyzed. RESULTS: Evidence of past PTB infection was found in 1,482 (9.6%) of 15,516 subjects. The serum 25(OH)D level was lower in the group with past PTB than in the non-PTB group (P=0.013). Respiratory dysfunction was more common in the past PTB group than in the non-PTB group (restrictive pattern, 14.0% vs. 9.6%; obstructive pattern, 29.6% vs. 8.2%; both P<0.001). After adjusting for age, sex, height, and season, the mean difference in forced expiratory volume in 1 second (FEV1) between the highest and lowest quartiles of 25(OH)D was 100.2 mL (standard error= 49.3 mL, P for trend=0.049) in the past PTB group and 34.7 mL (standard error=13.6 mL, P=0.009) in the non-PTB group. CONCLUSION: FEV1 tended to increase as the vitamin D quartile increased in both study groups. This relationship was more pronounced in subjects with a history of PTB. A higher serum 25(OH)D level might be beneficial in preserving lung function after PTB infection.
Adult ; Forced Expiratory Volume ; Humans ; Korea ; Lung ; Mass Chest X-Ray ; Nutrition Surveys ; Radiography ; Seasons ; Spirometry ; Thorax ; Treatment Outcome ; Tuberculosis ; Tuberculosis, Pulmonary ; Vitamin D ; Vitamin D Deficiency

PURPOSE: Congenital pulmonary airway malformation (CPAM)—a rare developmental anomaly—affects the lower respiratory tract in newborns. By comparing the reliability of diagnostic tools and identifying predictive factors for symptoms, we provide comprehensive clinical data for the proper management of CPAM. METHODS: We reviewed the medical records of 66 patients with prenatally diagnosed CPAM delivered at Severance Children's Hospital between January 2005 and July 2017. RESULTS: We enrolled 33 boys and 33 girls. Their mean gestational age and birth weight were 38.8 weeks and 3,050 g, respectively. Prenatal ultrasonography and postnatal radiography, lung ultrasonography, and chest computed tomography (CT) showed inconsistent findings. Chest CT showed superior sensitivity (100%) and positive predictive value (90%). Among the 66 patients, 59 had postnatally confirmed CPAM, three had pulmonary sequestration, one had cystic teratoma, and one had a normal lung. Of the 59 patients with CPAM, 21 (35%; mean age, 23.4 months) underwent surgery, including 15 who underwent video-assisted thoracoscopy. Twenty-five and 12 patients exhibited respiratory symptoms at birth and during infancy, respectively. Apgar scores and mediastinal shift on radiography were significantly associated with respiratory symptoms at birth. However, none of the factors could predict respiratory symptoms during infancy. CONCLUSION: Radiography or ultrasonography combined with chest CT can confirm an unclear or inconsistent lesion. Apgar scores and mediastinal shift on radiography can predict respiratory symptoms at birth. However, symptoms during infancy are not associated with prenatal and postnatal factors. Chest CT combined with periodic symptom monitoring is important for diagnosing and managing patients with prenatally diagnosed CPAM and to guide appropriate timing of surgery.
Birth Weight ; Bronchopulmonary Sequestration ; Cystic Adenomatoid Malformation of Lung, Congenital ; Female ; Gestational Age ; Humans ; Infant, Newborn ; Lung ; Medical Records ; Parturition ; Radiography ; Respiratory System ; Teratoma ; Thoracic Surgery, Video-Assisted ; Thoracoscopy ; Thorax ; Tomography, X-Ray Computed ; Ultrasonography ; Ultrasonography, Prenatal

Precisely segmented lung fields restrict the region-of-interest from which radiological patterns are searched, and is thus an indispensable prerequisite step in any chest radiographic CADx system. Recently, a number of deep learning-based approaches have been proposed to implement this step. However, deep learning has its own limitations and cannot be used in resource-constrained settings. Medical systems generally have limited RAM, computational power, storage, and no GPUs. They are thus not always suited for running deep learning-based models. Shallow learning-based models with appropriately selected features give comparable performance but with modest resources. The present paper thus proposes a shallow learning-based method that makes use of 40 radiomic features to segment lung fields from chest radiographs. A distance regularized level set evolution (DRLSE) method along with other post-processing steps are used to refine its output. The proposed method is trained and tested using publicly available JSRT dataset. The testing results indicate that the performance of the proposed method is comparable to the state-of-the-art deep learning-based lung field segmentation (LFS) methods and better than other LFS methods.
Dataset ; Learning ; Lung ; Methods ; Radiography, Thoracic ; Running ; Thorax

BACKGROUND: Chest radiographs (CXR) are the most commonly used imaging techniques by various clinicians and radiologists. However, detecting lung lesions on CXR depends largely on the reader's experience level, so there have been several trials to overcome this problem using post-processing of CXR. We investigated the added value of bone suppression image (BSI) in detecting various subtle lung lesions on CXR with regard to reader's expertise. METHODS: We applied a software program to generate BSI in 1,600 patients in the emergency department. Of them, 80 patients with subtle lung lesions and 80 patients with negative finding on CXR were retrospectively selected based on the subtlety scores on CXR and CT findings. Ten readers independently rated their confidence in deciding the presence or absence of a lung lesion at each of 960 lung regions on the two separated imaging sessions: CXR alone vs. CXR with BSI. RESULTS: The additional use of BSI for all readers significantly increased the mean area under the curve (AUC) in detecting subtle lung lesions (0.663 vs. 0.706; P < 0.001). The less experienced readers were, the more AUC differences increased: 0.067 (P < 0.001) for junior radiology residents; 0.064 (P < 0.001) for non-radiology clinicians; 0.044 (P < 0.001) for senior radiology residents; and 0.019 (P = 0.041) for chest radiologists. The additional use of BSI significantly increased the mean confidence regarding the presence or absence of lung lesions for 213 positive lung regions (2.083 vs. 2.357; P < 0.001) and for 747 negative regions (1.217 vs. 1.195; P = 0.008). CONCLUSION: The use of BSI increases diagnostic performance and confidence, regardless of reader's expertise, reduces the impact of reader's expertise and can be helpful for less experienced clinicians and residents in the detection of subtle lung lesions.
Area Under Curve ; Diagnosis ; Emergency Service, Hospital ; Humans ; Image Processing, Computer-Assisted ; Lung ; Radiography ; Radiography, Thoracic ; Retrospective Studies ; Thorax