Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients. METHODS: In this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method. RESULTS: Of the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94. CONCLUSIONS: In this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.

[A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis].

Pneumoconiosis ranks first among the newly-emerged occupational diseases reported annually in China, and imaging diagnosis is still one of the main clinical diagnostic methods. However, manual reading of films requires high level of doctors, and it is difficult to discriminate the staged diagnosis of pneumoconiosis imaging, and due to the influence of uneven distribution of medical resources and other factors, it is easy to lead to misdiagnosis and omission of diagnosis in primary healthcare institutions. Computer-aided diagnosis system can realize rapid screening of pneumoconiosis in order to assist clinicians in identification and diagnosis, and improve diagnostic efficacy. As an important branch of deep learning, convolutional neural network (CNN) is good at dealing with various visual tasks such as image segmentation, image classification, target detection and so on because of its characteristics of local association and weight sharing, and has been widely used in the field of computer-aided diagnosis of pneumoconiosis in recent years. This paper was categorized into three parts according to the main applications of CNNs (VGG, U-Net, ResNet, DenseNet, CheXNet, Inception-V3, and ShuffleNet) in the imaging diagnosis of pneumoconiosis, including CNNs in pneumoconiosis screening diagnosis, CNNs in staging diagnosis of pneumoconiosis, and CNNs in segmentation of pneumoconiosis foci to conduct a literature review. It aims to summarize the methods, advantages and disadvantages, and optimization ideas of CNN applied to the images of pneumoconiosis, and to provide a reference for the research direction of further development of computer-aided diagnosis of pneumoconiosis.

[Study on evaluation method of circular small shadow profusion in chest CT reconstruction images of pneumoconiosis].

Objective: To select chest CT image patterns for the diagnosis of pneumoconiosis and establish a method for determining the profusion of circular small shadows in chest CT. Methods: In April 2021, 66 cases of occupational pneumoconiosis patients with digital radiography (DR) chest radiographs and chest CT imaging data with circular small shadow as the main manifestations were selected as the study objects. 1.5 mm and 5 mm chest CT axial images, 1 mm and 5 mm chest CT coronal multi-plane recombination (MPR) images, and 5 mm chest CT coronal maximum intensity projection (MIP) images were used to observe the different characteristics of pneumoconiosis patients, and were compared and analyzed with DR chest radiographs to establish the experimental chest CT standards. The consistency of the profusion results between the experimental chest CT standards and GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis was verified. Results: All the 66 objects were male, including 33 cases of stage Ⅰ pneumoconiosis, 17 cases of stage Ⅱ pneumoconiosis and 16 cases of stage Ⅲ pneumoconiosis. By observing five chest CT images of 66 objects, we found that chest CT images of different modes could clearly display and identify abnormal images such as small circular shadow, large shadow, small shadow aggregation, honeycomb glass shadow, flake glass shadow, uniform low-profusion glass shadow, mesh glass shadow, cable shadow, linear shadow, subpleural spinous shadow, subpleural nodules, various kinds of emphysema and lung texture distortion and fracture. Small shadow aggregation was usually accompanied by the appearance of large shadow. The vascular shadows in 5 mm CT images had good ductility, and small nodules were easy to distinguish. The coronal MIP image of 5 mm chest CT used edge enhancement technology, which was prone to small shadow fusion and fibrotic shadow fusion. The coronal MPR image of 5 mm chest CT was highly consistent with the DR chest radiographs in terms of the integrity of film reading. GBZ 70-2015 standard was used to compare the profusion of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, and the consistency test Kappa=0.64. GBZ 70-2015 standard and experimental chest CT standard were used to compare the profusion results of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, respectively, and the consistency test Kappa=0.80, with high consistency. Conclusion: 5 mm coronal MPR image is suitable for chest CT imaging in the diagnosis of pneumoconiosis. Following the selection path and method of GBZ 70-2015 profusion criterion, the established experimental chest CT standard in determining the profusion of small circular shadows in 5 mm coronal MPR images of chest CT with pneumoconiosis has a high consistency with GBZ 70-2015 standard.

Artificial Intelligence in Quantitative Chest Imaging Analysis for Occupational Lung Disease.

Occupational lung disease manifests complex radiologic findings which have long been a challenge for computer-assisted diagnosis (CAD). This journey started in the 1970s when texture analysis was developed and applied to diffuse lung disease. Pneumoconiosis appears on radiography as a combination of small opacities, large opacities, and pleural shadows. The International Labor Organization International Classification of Radiograph of Pneumoconioses has been the main tool used to describe pneumoconioses and is an ideal system that can be adapted for CAD using artificial intelligence (AI). AI includes machine learning which utilizes deep learning or an artificial neural network. This in turn includes a convolutional neural network. The tasks of CAD are systematically described as classification, detection, and segmentation of the target lesions. Alex-net, VGG16, and U-Net are among the most common algorithms used in the development of systems for the diagnosis of diffuse lung disease, including occupational lung disease. We describe the long journey in the pursuit of CAD of pneumoconioses including our recent proposal of a new expert system.

Tetrandrine slows the radiographic progression of progressive massive fibrosis in pneumoconiosis: a retrospective cohort study.

OBJECTIVE: This study aims to explore the clinical effect of Tetrandrine (Tet) on progressive massive fibrosis (PMF) of pneumoconiosis. METHODS: This retrospective study collected 344 pneumoconiosis patients with PMF, and 127 were eligible for the final analysis, including 57 patients in the Tet group and 70 patients in the control group. The progress of imaging and lung function were compared between the two groups. RESULTS: After 13 months (median) of treatment, the size of PMF was smaller in the Tet group than that in the control group (1526 vs. 2306, p=0.001), and the size was stable in the Tet group (1568 vs. 1526, p= 0.381), while progressed significantly in the control group (2055 vs. 2306, p=0.000). The small nodule profusion and emphysema were also milder than that in the control group (6.0 vs. 7.5, p=0.046 and 8.0 vs. 12, p=0.016 respectively). Pulmonary ventilation function parameters FVC and FEV1 improved in the Tet group (3222 vs. 3301, p=0.021; 2202 vs. 2259, p=0.025 respectively) and decreased in the control group (3272 vs. 3185, p= 0.00; 2094 vs. 1981, p=0.00 respectively). FEV1/FVC was also significantly higher in the Tet group than that in the control group (68.45vs. 60.74, p=0.001). However, similar result was failed to observed for DLco%, which showed a significant decrease in both groups. CONCLUSION: Tet has shown great potential in the treatment of PMF by slowing the progression of pulmonary fibrosis and the decline of lung function.

[Study on optimization of image processing parameters of pneumoconiosis by DR].

Objective: To explore the effect of different post-processing parameters of digital radiography (DR) on the quality of chest X-ray for pneumoconiosis diagnosis, and to provide suggestions on parameter setting suitable for this kind of DR machine. Methods: From January 1, 2022 to June 30, 2022, the chest films of 35 workers in the department of radiology of Hangzhou occupational disease prevention and treatment hospital were randomly selected and printed after setting different image post-processing parameters. The quality of chest film was evaluated by the measurement of optical densitometer and the combination of subjective and objective by professional physicians. Results: When the density is set to 2 and the contrast/detail contrast is 4.5, the optical density of each area of DR chest film meets the requirements of chest X-ray quality, and the qualified rate of physician quality evaluation is the highest. Conclusion: Reasonable setting of image post-processing parameters can improve the quality of chest radiograph.

[Application of artificial intelligence in digital chest radiography diagnosis of pneumoconiosis].

Pneumoconiosis is the occupational disease with the highest burden in China currently. The diagnosis of pneumoconiosis mainly relies on manual reading of X-ray high-kilovoltage or digital photography chest radiograph, which has some problems such as low efficiency, strong subjectivity, and cannot accurately judge the critical lesions. With the progress of machine-aided diagnosis technology, the efficient, objective and quantitative of artificial intelligence diagnosis technology just solve the shortcomings above. This paper reviews the research progress in digital chest radiography diagnosis of pneumoconiosis using artificial intelligence technology, especially deep learning model, combined with the limitations of conventional manual reading, in order to clarify the application prospect of artificial intelligence technology in the diagnosis of pneumoconiosis by digital chest radiography, and provide a direction for future research in this field.

[Research advances in imaging techniques related to the diagnosis of pneumoconiosis].

Occupational pneumoconiosis (hereinafter referred to as pneumoconiosis) is the most harmful occupational disease in China. According to the current standard GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis, pneumoconiosis is mainly diagnosed and staged by high kilovolt or digital radiography. Chest radiography in pneumoconiosis is the most widely studied and mature imaging technique in the diagnosis of pneumoconiosis. However, this technique has some limitations in the screening of some early pneumoconiosis and occupational health examination, and there is a certain risk of missed diagnosis and misdiagnosis. With the continuous development of imaging examination technology, computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography and artificial intelligence technology as auxiliary imaging examination methods have shown different diagnostic values in the research of auxiliary diagnosis of pneumoconiosis. This paper summarizes the advantages and problems in the application of various kinds of imaging techniques, which provides a direction for the future research of imaging techniques related to the diagnosis of pneumoconiosis.

[Application of a light-weighted convolutional neural network for automatic recognition of coal workers' pneumoconiosis in the early stage].

Objective: To construct and verify a light-weighted convolutional neural network (CNN), and explore its application value for screening the early stage (subcategory 0/1 and stage Ⅰ of pneumoconiosis) of coal workers' pneumoconiosis (CWP) from digital chest radiography (DR) . Methods: A total of 1225 DR images of coal workers who were examined at an Occupational Disease Prevention and Control Institute in Anhui Province from October 2018 to March 2021 were retrospectively collected. All DR images were collectively diagnosed by 3 radiologists with diagnostic qualifications and gave diagnostic results. There were 692 DR images with small opacity profusion 0/- or 0/0 and 533 DR images with small opacity profusion 0/1 to stage Ⅲ of pneumoconiosis. The original chest radiographs were preprocessed differently to generate four datasets, namely 16-bit grayscale original image set (Origin16), 8-bit grayscale original image set (Origin 8), 16-bit grayscale histogram equalized image set (HE16) and 8-bit grayscale histogram equalized image set (HE8). The light-weighted CNN, ShuffleNet, was applied to train the generated prediction model on the four datasets separately. The performance of the four models for pneumoconiosis prediction was evaluated on a test set containing 130 DR images using measures such as the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, and Youden index. The Kappa consistency test was used to compare the agreement between the model predictions and the physician diagnosed pneumoconiosis results. Results: Origin16 model achieved the highest ROC area under the curve (AUC=0.958), accuracy (92.3%), specificity (92.9%), and Youden index (0.8452) for predicting pneumoconiosis, with a sensitivity of 91.7%. And the highest consistency between identification and physician diagnosis was observed for Origin16 model (Kappa value was 0.845, 95%CI: 0.753-0.937, P<0.001). HE16 model had the highest sensitivity (98.3%) . Conclusion: The light-weighted CNN ShuffleNet model can efficiently identify the early stages of CWP, and its application in the early screening of CWP can effectively improve physicians' work efficiency.

Prevalence and severity of abnormal lung function among US former coal miners with and without radiographic coal workers' pneumoconiosis.

OBJECTIVES: Examination of lung function abnormalities among coal miners has historically focused on actively working miners. This likely underestimates the true burden of chronic respiratory disease. The objective of this study was to characterise patterns and severity of lung function impairment among a population of former coal miners. METHODS: Cross-sectional data from 2568 former coal miners evaluated at eight US Black Lung clinics in a 12-month period were retrospectively analysed for patterns of prebronchodilator spirometric abnormality and severity of lung function impairment. Spirometry data from a subset of former miners with chest radiographs were analysed based on the presence and severity of coal workers' pneumoconiosis (CWP). RESULTS: Abnormal spirometry was identified in 56.6% of subjects. The age-standardised prevalence of airflow obstruction among miners aged ≥45 years was 18.9% overall and 12.2% among never smokers. Among 1624 subjects who underwent chest radiography, the prevalence and severity of abnormal spirometry increased with worsening radiographic category for pneumoconiosis. Of never-smoking former miners without radiographic CWP, 39.0% had abnormal spirometry; 25.1% had abnormally low forced expiratory volume in 1 s (FEV1), and 17.1% had moderate to severe FEV1 impairment. CONCLUSIONS: Abnormal spirometry is common among former coal miners. While ever-smoking former miners had higher rates of airflow obstruction, never-smoking former miners also demonstrated clinically significant airflow obstruction, including those without radiographic pneumoconiosis. These findings demonstrate the importance of recognising physiological as well as imaging manifestations of coal mine dust lung diseases in former miners.

Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis.

PURPOSE: This paper aims to develop a successful deep learning model with data augmentation technique to discover the clinical uniqueness of chest X-ray imaging features of coal workers' pneumoconiosis (CWP). PATIENTS AND METHODS: We enrolled 149 CWP patients and 68 dust-exposure workers for a prospective cohort observational study between August 2021 and December 2021 at First Hospital of Shanxi Medical University. Two hundred seventeen chest X-ray images were collected for this study, obtaining reliable diagnostic results through the radiologists' team, and confirming clinical imaging features. We segmented regions of interest with diagnosis reports, then classified them into three categories. To identify these clinical features, we developed a deep learning model (ShuffleNet V2-ECA Net) with data augmentation through performances of different deep learning models by assessment with Receiver Operation Characteristics (ROC) curve and area under the curve (AUC), accuracy (ACC), and Loss curves. RESULTS: We selected the ShuffleNet V2-ECA Net as the optimal model. The average AUC of this model was 0.98, and all classifications of clinical imaging features had an AUC above 0.95. CONCLUSION: We performed a study on a small dataset to classify the chest X-ray clinical imaging features of pneumoconiosis using a deep learning technique. A deep learning model of ShuffleNet V2 and ECA-Net was successfully constructed using data augmentation, which achieved an average accuracy of 98%. This method uncovered the uniqueness of the chest X-ray imaging features of CWP, thus supplying additional reference material for clinical application.

The diagnosis of early pneumoconiosis in dust-exposed workers: comparison of chest radiography and computed tomography.

BACKGROUND: Chest radiography (CR) is employed as the evaluation of pneumoconiosis; however, we sometimes encounter cases in which computed tomography (CT) is more effective in detecting subtle pathological changes or cases in which CR yields false-positive results. PURPOSE: To compare CR to CT in the diagnosis of early-stage pneumoconiosis. MATERIAL AND METHODS: CR and CT were performed for 132 workers with an occupational history of mining. We excluded 23 cases of arc-welder's lung. Five readers who were experienced chest radiologists or pulmonologists independently graded the pulmonary small opacities on CR of the remaining 109 cases. We then excluded 37 cases in which the CT data were not sufficient for grading. CT images of the remaining 72 cases were graded by the five readers. We also assessed the degree of pulmonary emphysema in those cases. RESULTS: The grade of profusion on CR (CR score) of all five readers was identical in only 5 of 109 cases (4.6%). The CR score coincided with that on CT in 40 of 72 cases (56%). The CT score was higher than that on CR in 13 cases (18%). On the other hand, the CT score was lower than that on CR in 19 cases (26%). The incidence of pulmonary emphysema was significantly higher in patients whose CR score was higher than their CT score. CONCLUSION: CT is more sensitive than CR in the evaluation of early-stage pneumoconiosis. In cases with emphysema, the CR score tends to be higher in comparison to that on CT.

Postexposure progression of pneumoconiosis among former Appalachian coal miners.

BACKGROUND: The prevalence of pneumoconiosis among working United States underground coal miners has been increasing for the past two decades, with the highest rates of disease observed among miners in the central Appalachian states of Kentucky, Virginia, and West Virginia. Surveillance for this disease in the United States focuses on working coal miners, who continue to be occupationally exposed to dust. This study examines the radiographic evidence for postexposure progression of pneumoconiosis in a population of former coal miners no longer occupationally exposed to coal mine dust who were seen at a community radiology clinic in eastern Kentucky. METHODS: Data were obtained and analyzed from clinical records of former coal miners who had a clinic encounter during January 1, 2017-August 1, 2019, a recorded final year of employment, and ≥2 postemployment digital chest radiographs. Radiographs were classified according to the International Labour Office guidelines by at least two B Readers. A final summary pneumoconiosis severity score (range, 0-13), accounting for both small and large opacities, was assigned to each chest radiograph. Progression was defined as an increase in severity score between a miner's radiographs over time. RESULTS: Data for 130 former coal miners were analyzed. All miners were male and most (n = 114, 88%) had worked primarily in Kentucky. Information on race/ethnicity was not available. The most common job types were roof bolters (n = 51, 39%) and continuous miner operators (n = 46, 35%). Forty-one (31.5%) miners had evidence of radiographic disease progression after leaving the workforce, with a median of 3.6 years between first and latest postretirement radiograph. A total of 80 (62%) miners had evidence of pneumoconiosis on their latest radiograph, and two-thirds (n = 53) of these were classified as progressive massive fibrosis (PMF), the most severe form of the disease. CONCLUSIONS: Postexposure progression can occur in former coal miners, emphasizing the potential benefits of continued radiographic follow-up postemployment. In addition to participating in disease screening throughout their careers to detect pneumoconiosis early and facilitate intervention, radiographic follow-up of former coal miners can identify new or progressive radiographic findings even after workplace exposure to respirable coal mine dust ends. Identification of progressive pneumoconiosis in former miners has potential implications for clinical management and eligibility for disability compensation.

[Progressive massive fibrosis in pneumoconiosis is mimicking lung malignancy on (18)F-FDGPET-CT: two cases report].

Occupational pneumoconiosis is one of the main occupational diseases in China. Progressive massive fibrosis in pneumoconiosis should be distinguished from lung cancer for their similar imaging features which is often identified by (18)F-FDG PET-CT in clinic. Here we reported two cases of pneumoconiosis. Both of them were suspected of carrying malignant tumors by preoperative PET-CT exam, however, nodules in these two patients were all proved to be benign by intraoperative pathology which suggested that there is false-positive possibility in the distinguishment of pneumoconiosis nodules by (18)F-FDG PET-CT.

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning.

PURPOSE: The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms. MATERIALS AND METHODS: 1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people. RESULTS: Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively. CONCLUSION: The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.

Early signs of pneumoconiosis in a dental technician in Italy: a case report.

BACKGROUND: Dental technicians are at high risk of pneumoconiosis, usually driven by inhalation of mixed dusts, including metals. An etiological diagnosis is not easy to be performed, particularly in advanced stages. CASE PRESENTATION: We describe the case of an early pneumoconiosis occurring in a 47-year-old dental technician who developed respiratory symptoms shortly after beginning work. She described the work environment as dusty and lacking relevant primary prevention tools. A chest CT showed multiple peripheral pseudonodular lesions in both lower lobes; bronchoalveolar lavage and bronchial aspirate evidenced numerous macrophages with reflective metal bodies included into the cytoplasm, that at scanning electron microscopy coupled to Energy Dispersive X-Ray Analysis resulted Zirconium and Aluminum, whereas Tungsten (W) was localized outside cells. End of shift urinary concentrations of W were substantially raised as compared to pre-shift (1.1 vs. 0.2 µg/L). CONCLUSIONS: We concluded for diagnosis of early work-related pneumoconiosis due to abnormal occupational exposure to metals. The case demonstrates the need also for dental professionals to comply with industrial hygiene standards and to be monitored by occupational health physicians.

[Discussion of grading method of small opacity profusion of pneumoconiosis on CT scans and the corresponding reference images].

Objective: To explore the CT grading method of small opacity profusion of pneumoconiosis, and draw up the corresponding CT reference film. Methods: In December 2019, Three hundred thirty-seven cases of pneumoconosis and suspected pneumoconiosis were examined by chest radiography and Computed Tomography (CT) in the same period. According to Diagnosis of Occupational Pneumoconiosis (GBZ 70-2015) , small opacity profusion of pneumoconiosis in each zone of lung was divided. On CT scans, it was divided into 5 grades of 0, 0+, 1, 2 and 3. Grade 0 corresponded to Sub-grade 0/- and Sub-grade 0/0 of Grade 0 in chest radiograph. Grade 0+ was equivalent to Sub-grade 0/1 of Grade 0. Grade 1, 2, 3 were equivalent to Grade 1, 2 and 3, respectively (including each sub-grade) . The CT image quality of each zone of lung was divided into 1 to 4 levels. Results of level 4 were not included in statistical analyses.Based on the results of small opacity profusion in each zone of lung, consistency analysis was performed between chest radiograph and CT. The selection method of reference films was developed. Based on the types and grades of small opacity, the final reference films were determined. Results: There were 1877 zones of lung with CT image quality from level 1 to 3, including 335 in upper right, 319 in middle right, 284 in lower right, 334 in upper left, 320 in middle left and 286 in lower left. The Kappa values of small opacity profusion in upper right zone, upper left zone, left middle zone, and lower left zone were all between 0.4-0.75. In middle right zone and lower right zone, they were all above 0.75.Among all 6 zones of lung, the diagnostic concordance rates between CT and chest radiograph were all above 80%.The corresponding CT reference films were proposed, including type p and q in Grade 2 and 3, type r in Grade 2, type s and t in Grade 0+ to 3. Conclusion: The CT grading method for small opacity profusion of pneumoconiosis is feasible, and the application value of its reference films needs to be further verified.

[Application of multi-slice spiral CT refarmation reconstruction technique and DR photography of pneumoconiosis patients at stage three].

Objective: To investigate the value of CT multiplanar reconstruction (MPR) in the diagnosis of stage Ⅲ pneumoconiosis and complications. Methods: In September 2020, 94 patients with stage Ⅲ pneumoconiosis in Guangzhou 12th people's hospital were selected for digital radiography (DR) and MPR. The detection rate of the number of large shadows and the incidence of related complications were compared and analyzed. The counting data were expressed by frequency and percentage (%) , and the comparison was performed by chi square test. Results: 178 and 132 large shadows were detected in MPR and DR chest films respectively. Compared with Dr examination, MPR had higher detection rates of pneumoconiosis related complications such as pulmonary tuberculosis, emphysema, pleural thickening, adhesion, pneumonia, pleural effusion, enlargement of hilar and mediastinal lymph nodes and calcification (P<0.05) , There was no significant difference in the detection rate of pulmonary bullae (P>0.05) . Compared with Dr, MPR had a higher detection rate in the diagnosis of cavity, calcification, bronchiectasis and parascar emphysema (P<0.05) . Conclusion: MPR is better in detecting large shadow and complications of stage Ⅲpneumoconiosis, and has important value.

[Control study of chest CT imaging features of aluminosis and silicosis patients].

Objective: To understand the chest CT features of aluminosis caused by alumina and to improve the understanding of the imaging findings of alumina pneumoconiosis. Methods: The chest CT findings of 17 cases of alumina-induced pneumoconiosis and 30 cases of silicosis (the control group) diagnosed in Zibo Occupational Disease Prevention Hospital from April 2015 to July 2020 were analyzed retrospectively. The characteristics of fibrosis of the two kinds of pneumoconiosis and the incidence of size, density, distribution, tractive bronchiectasis, pleural thickening and interlobular septal thickening of pneumoconiosis nodules were compared. Results: Alumina pneumoconiosis showed nodules with thickened interlobular septal of 66.67% (12/18) , honeycomb lung of 22.22% (4/18) , ground glass shadow of 61.11% (11/18) , simple nodules of 11.11% (2/18) , and no fusion mass. In the control group, the long-line fibrosis of nodules with thickened interlobular septal were 16.67% (5/30) , 6.67% (2/30) with honeycomb lung and ground glass density shadow, 23.33% (7/30) with fusion mass and 53.33% (16/30) with simple nodule. There were significant differences in CT findings of nodules with thickened interlobular septal, ground glass density shadow, fused mass and simple nodules between the two groups (P<0.05) . The interstitial beaded nodules were seen in 18 cases of alumina pneumoconiosis, 50.00% (9/18) of them were beaded nodules, 61.33% (46/75) of low density nodules and 38.89% (7/18) of central lobular nodules were seen in alumina pneumoconiosis. The average width of nodules was (1.29±0.38) mm. Central lobular nodules were seen in all 30 cases of silicosis, 10.00% (3/30) were mainly beaded nodules, low density nodules were 36.29% (90/248) , and the average width diameter of nodules was (1.85±0.58) mm. There were significant differences between the two groups (P<0.05) . Alumina pneumoconiosis was often accompanied by traction bronchiectasis, pleural thickening and interlobular septal thickening (11, 18, 17 cases, 61.11%, 100.00%, 94.44%) , compared with the control group (9, 18, 18 cases, 30.00%, 60.00%, 60.00%) . The differences were statistically significant (P<0.05) . The maximum CT value of noncalcified mediastinal lymphnodes in alumina pneumoconiosis was (103.43±26.33) HU, which was higher than that of the control group[ (75.22±16.70) HU], and the difference was statistically significant (P<0.05) . Conclusion: Alumina pneumoconiosis chest CT shows slightly low-density beaded nodules, thickened interlobular septal, and pulmonary interstitial fibrosis of ground-glass shadows, mostly combines with stretched bronchiectasis, thickened pleura, and mediastinum increased lymph node density.

Pneumoconiosis progression patterns in US coal miner participants of a job transfer programme designed to prevent progression of disease.

OBJECTIVES: Pneumoconiosis prevalence and severity among US coal miners has been increasing for the past 20 years. An examination of the current approaches to primary and secondary prevention efforts is warranted. One method of secondary prevention is the Mine Safety and Health Administration-administered part 90 option programme where US coal miners with radiographic evidence of pneumoconiosis can exercise their right to be placed in a less dusty area of the mine. This study focuses on characterising the progression of disease among US coal miners who participated in the National Institute for Occupational Safety and Health-administered Coal Workers' Health Surveillance Programme (CWHSP) and exercised their part 90 job transfer option. METHODS: Chest radiograph classifications of working underground coal miners who exercised their part 90 job transfer option during 1 January 1986 to 21 November 2016 and participated in the CWHSP during 1 January 1981 to 19 March 2019 were analysed. RESULTS: 513 miners exercised their part 90 option and participated in the CWHSP at least once during this time period. Of the 149 miners with ≥2 radiographs available, 48 (32%) showed progression after exercising part 90 and had more severe disease prior to exercising, compared with miners who did not progress (severity score of 2.8 vs 1.7, p=0.0002). CONCLUSION: The part 90 job transfer option programme is not routinely used as intended to prevent progression of pneumoconiosis among US coal miners. The one-third of miners who participated in part 90 and continued to progress, exercised their part 90 option at a later stage of disease compared with non-progressors.