Distribution of Lung-RADS categories according to job type in a single shipyard workers

Abstract

Background Recently, lung cancer screenings based on age and smoking history using low-dose computed tomography (LDCT) have begun in Korea. This study aimed to evaluate the distribution of lung imaging reporting and data system (Lung-RADS) categories in shipyard workers exposed to lung carcinogens such as nickel, chromium, and welding fumes according to job type, to provide basic data regarding indications for LDCT in shipyard workers. Methods This study included 6,326 workers from a single shipyard, who underwent health examinations with LDCT between January 2010 and December 2018. Data on age, smoking status and history, medical history, and job type were investigated. The participants were categorized into high-exposure, low-exposure, and non-exposure job groups based on the estimated exposure level of nickel, chromium, and welding fumes according to job type. Cox proportional hazard regression analysis was used to determine the difference between exposure groups in Lung-RADS category ≥ 3 (3, 4A, and 4B). Results Out of all participants, 97 (1.5%) participants were classified into Lung-RADS category ≥ 3 and 7 (0.1%) participants were confirmed as lung cancer. The positive predictive value (ratio of diagnosed lung cancer cases to Lung-RADS category ≥ 3) was 7.2%. The hazard ratio (HR) of Lung-RADS category ≥ 3 was 1.451 (95% confidence interval [CI]: 0.911–2.309) in low-exposure and 1.692 (95% CI: 1.007–2.843) in high-exposure job group. Adjusting for age and pack-years, the HR was statistically significant only in the high-exposure job group (HR: 1.689; 95% CI: 1.004–2.841). Conclusions Based on LDCT and Lung-RADS, among male shipyard workers, Lung-RADS category ≥ 3 were significantly higher in the high-exposure job group. Their HR tended to be > 1.0 and was statistically significant in the high-exposure job group. Additional studies should be conducted to establish more elaborate LDCT indications for occupational health examination.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students

“…Based on the signs, benign and malignant diagnosis was made. Diagnosis of pulmonary nodules was graded according to the Lung CT Screening Reporting and Data System [ 11 , 12 ] (Lung-RADS) (Table 1 ).…”