Che-Jui Chang

Occupational Exposure to Talc Increases the Risk of Lung Cancer: A Meta-Analysis of Occupational Cohort Studies

Objective Talc is widely used in industrial applications. Previous meta-analyses of carcinogenic effects associated with inhaled talc included publications before 2004, with a lack of data in China, the largest talc-producing country. The safety of workers exposed to talc was unclear due to limited evidence. The objective of this study was to reevaluate the association between inhaled talc and lung cancer. Setting, Participants, and Outcome Measures A meta-analysis was performed to calculate the meta-SMR of lung cancer. We searched the MEDLINE, EMBASE, CNKI, and Wanfang Data databases through March 2017. Data from observational studies were pooled using meta-analysis with random effects models. Results Fourteen observational cohort studies (13 publications) were located via literature search. The heterogeneity of the included data was high (I-squared = 72.9%). Pooling all the cohorts yielded a meta-SMR of 1.45 (95% CI: 1.22–1.72, p < 0.0001) for lung cancer among the study subjects exposed to talc. Subgroup analysis for asbestos contamination showed no significant difference in lung cancer death between subjects exposed to talc with and without asbestos (p = 0.8680), indicating that this confounding factor may have no significance. Conclusions This study provides evidence that nonasbestiform talc might still increase the risk of lung cancer. Further epidemiological studies are required to evaluate the safety of workers with occupational talc exposure.

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.

0244 The diagnosis of pneumoconiosis using a sensor array technique

Pneumoconiosis is a traditional occupational disease and has reemerged in recent years. Current medical surveillance program have flaws that may result in poor detection of early pneumoconiosis around the world. Pneumoconiosis could generates specific volatile organic compounds (VOCs) that may constitute a specific breath print for diagnosis. The objective of this study was to develop a breath test for pneumoconiosis using a senor array technique. We conducted a case-control study that enrolled 36 asymptomatic cases of pneumoconiosis and 64 healthy controls between October and November 2016 to construct the prediction model. One litter of breath air was collected after five minutes of tidal breathing through a non-rebreathing valve with inspiratory VOC-filter, and storage by a Tedlar bag. The air was analysed by a 32 nanocomposite sensor array electronic nose within 30 min. We used the profusion category&x2267;1/1 in chest X-ray in accordance with the ILO-2011D criteria as the reference standard to assess the diagnostic accuracy. Data were randomly split into 80% for model building and 20% for validation. By linear discriminant analysis, the sensitivity was 71.0%, specificity was 91.8%, accuracy was 86.8%, and ROC-AUC was 0.89 in the training set, and the sensitivity was 80.0%, specificity was 66.7%, accuracy was 70.0%, and ROC-AUC was 0.79 in the validation set. Breath test might have potential in the screening for pneumoconiosis; however, a multi-centre study is warranted to establish a reliable model and all procedures must be standardised before clinical application.