Imaging diagnosis of classical and new pneumoconiosis: predominant reticular HRCT pattern

[Occupational interstitial lung diseases]

A variety of workplace exposures (organic or inorganic dusts as well as gases, fumes, or vapors) can cause diffuse interstitial lung disease. The latency period until onset of the disease can exceed 30 years. The disease course varies greatly and depends on the quantity of the inhaled substance and its fibrogenic effect. Pulmonary high-resolution computed tomography (HRCT) patterns do not differ significantly from those of interstitial lung diseases (ILD) of other etiologies. Therefore, without knowledge of the occupational history, work-related ILDs are often classified as idiopathic. In addition, there is increasing evidence in the recent literature that high exposure to silica dust can trigger autoimmune diseases (also involving the lungs). For this reason, a qualified occupational history is now an indispensable part of the interdisciplinary diagnosis of ILDs.

Aluminosis pneumoconiosis presenting as hyperdense lung nodules

We present the case of a 66-year-old man who presented with new incidentally found hyperdense pulmonary nodules. Further workup with a PET/CT revealed that the nodules were FDG-avid and that there was associated hypermetabolic lymphadenopathy. Due to his history of aluminum toxicity from welding, aluminosis pneumoconiosis was suspected. Biopsy of one of the nodules was done which reinforced this diagnosis. Aluminosis pneumoconiosis is a rare occupational lung disease mostly associated with industrial workers with prolonged unprotected exposure to fine aluminum dust. Prognosis depends on the duration and intensity of exposure, and there is no definitive treatment other than eliminating further exposure.

Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis

OBJECTION

The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features.

METHODS

A retrospective study was conducted, including 600 dust-exposed workers and 300 confirmed pneumoconiosis patients. Chest computed tomography (CT) images were divided into a training set and a test set in a 2:1 ratio. Whole-lung segmentation was performed using deep learning models for feature extraction of radiomics. Two feature selection algorithms and five classification models were used. The optimal model was selected using a 10-fold cross-validation strategy, and the calibration curve and decision curve were evaluated. To verify the applicability of the model, the diagnostic efficiency and accuracy between the model and human interpretation were compared. Additionally, the risk probabilities for different risk groups defined by the model were compared at different time intervals.

RESULTS

Four radiomics features were ultimately used to construct the predictive model. The logistic regression model was the most stable in both the training set and testing set, with an area under curve (AUC) of 0.964 (95 % confidence interval [CI], 0.950-0.976) and 0.947 (95 %CI, 0.925-0.964). In the training and testing sets, the Brier scores were 0.092 and 0.14, respectively, with threshold probability ranges of 2 %-99 % and 2 %-85 %. These results indicate that the model exhibits good calibration and clinical benefit. The comparison between the model and human interpretation showed that the model was not inferior in terms of diagnostic efficiency and accuracy. Additionally, the high-risk population identified by the model was diagnosed as pneumoconiosis two years later.

CONCLUSION

This study provides a meticulous and quantifiable method for detecting and assessing the risk of pneumoconiosis, building upon accurate diagnosis. Employing risk scoring and probability estimation, not only enhances the efficiency of diagnostic physicians but also provides a valuable reference for controlling the occurrence of pneumoconiosis.

Imaging diagnosis of pneumoconiosis with predominant nodular pattern: HRCT and pathologic findings

The radiological patterns of known pneumoconiosis have been changing in recent years. The basic pathology in pneumoconiosis is the presence of dust macules, mixed dust fibrosis, nodules, diffuse interstitial fibrosis, and progressive massive fibrosis. These pathologic changes can coexist in dust-exposed workers. High resolution CT reflects pathological findings in pneumoconiosis and is useful for the diagnosis. Pneumoconiosis such as silicosis, coal workers' pneumoconiosis, graphite pneumoconiosis, and welder's pneumoconiosis, has predominant nodular HRCT pattern. Diffuse interstitial pulmonary fibrosis is sometimes found in the lungs of this pneumoconiosis. In the early stages of metal lung, such as aluminosis and hard metal lung, centrilobular nodules are predominant findings, and in the advanced stages, reticular opacities are predominant findings. The clinician must understand the spectrum of expected imaging patterns related to known dust exposures and novel exposures. In this article, HRCT and pathologic findings of pneumoconiosis with predominant nodular opacities are shown.

High-resolution computed tomography diagnosis of pneumoconiosis complicated with pulmonary tuberculosis based on cascading deep supervision U-Net

OBJECTIVE

Pulmonary tuberculosis can promote pneumoconiosis deterioration, leading to higher mortality. This study aims to explore the diagnostic value of the cascading deep supervision U-Net (CSNet) model in pneumoconiosis complicated with pulmonary tuberculosis.

METHODS

A total of 162 patients with pneumoconiosis treated in our hospital were collected as the research objects. Patients were randomly divided into a training set (n = 113) and a test set (n = 49) in proportion (7:3). Based on the high-resolution computed tomography (HRCT), the traditional U-Net, supervision U-Net (SNet), and CSNet prediction models were constructed. Dice similarity coefficients, precision, recall, volumetric overlap error, and relative volume difference were used to evaluate the segmentation model. The area under the receiver operating characteristic curve (AUC) value represents the prediction efficiency of the model.

RESULTS

There were no statistically significant differences in gender, age, number of positive patients, and dust contact time between patients in the training set and test set (P > 0.05). The segmentation results of CSNet are better than the traditional U-Net model and the SNet model. The AUC value of the CSNet model was 0.947 (95% CI: 0.900∼0.994), which was higher than the traditional U-Net model.

CONCLUSION

The CSNet based on chest HRCT proposed in this study is superior to the traditional U-Net segmentation method in segmenting pneumoconiosis complicated with pulmonary tuberculosis. It has good prediction efficiency and can provide more clinical diagnostic value.

Pitfalls of using the ILO classification for silicosis compensation claims

BACKGROUND

The International Labour Organization (ILO) Classification of Radiographs of Pneumoconioses is used as the primary tool to determine compensation for pneumoconiosis in Turkey.

AIMS

We aimed to evaluate how the ILO classification applied, but obtaining chest radiographs in the workplace for screening until the completion of compensation claim files by the referral centres, based on the ILO reading.

METHODS

The study included 320 digital chest radiographs previously taken for screening from eight different ceramic factories and having finalised claim files by referral centres. We used an expert reference panel consisting of five ILO readers to re-evaluate all the radiographs independently using ILO standard films and reached a conclusion based on the agreement among at least three readers. The evaluation primarily included technical quality and silicosis diagnosis with an ILO 1/0 or above small profusion. The results were compared with previous findings.

RESULTS

Sixty-three (20%) chest radiographs were unacceptable for classification purposes according to the ILO technical quality grades. Among the remaining 257 chest radiographs, we diagnosed 103 with silicosis (40%), while the referral centres diagnosed 182 (71%). A discrepancy was found between our results and previous evaluations. We diagnosed silicosis in 50% and 17% of the previous silicosis and normal evaluations, respectively.

CONCLUSIONS

Our findings suggest that the use of the ILO classification for compensation claims may be problematic due to the way of its implementation in Turkey in addition to its subjectivity.

Early Identification, Accurate Diagnosis, and Treatment of Silicosis

Silicosis is a global problem, and it has brought about great burdens to society and patients' families. The etiology of silicosis is clear, preventable, and controllable, but the onset is hidden and the duration is long. Thus, it is difficult to diagnose it early and treat it effectively, leaving workers unaware of the consequences of dust exposure. As such, a lack of details in the work history and a slow progression of lung disease contribute to the deterioration of patients until silicosis has advanced to fibrosis. These issues are the key factors impeding the diagnosis and the treatment of silicosis. This article reviews the literature on the early identification, diagnosis, and treatment of silicosis as well as analyzes the difficulties in the diagnosis and the treatment of silicosis and discusses its direction of future development.