Artificial Intelligence helps detect Post Lung Biopsy Pneumothorax on follow-up chest radiographs
Percutaneous transthoracic needle biopsy (PTNB) is a highly accurate, widely used method for the diagnosis of lung lesions. However, the most frequent complication of PTNB is pneumothorax, which occurs in 16.2%–38.4% of PTNB procedures. In a recent study, researchers developed a deep learning-based computer-aided detection system that showed promising results in the detection of...
Percutaneous transthoracic needle biopsy (PTNB) is a highly accurate, widely used method for the diagnosis of lung lesions. However, the most frequent complication of PTNB is pneumothorax, which occurs in 16.2%–38.4% of PTNB procedures. In a recent study, researchers developed a deep learning-based computer-aided detection system that showed promising results in the detection of pneumothorax after lung biopsy.
The study findings were published in the journal Radiology on January 25, 2022.
Chest radiography is the recommended imaging technique for diagnosing PTNB-related pneumothorax. A recent study showed that a deep learning algorithm appropriately identified pneumothorax on post-PTNB radiographs in retrospectively collected consecutive diagnostic cohorts, with a sensitivity of 70.5% and specificity of 97.7%. However, the validation of a deep learning algorithm to improve diagnostic performance in real-world clinical practise has not previously been established. Therefore, Dr Chang Min Park and his team conducted a study to investigate whether a deep learning-based CAD system can improve detection performance for pneumothorax on chest radiographs after PTNB in clinical practice.
In a retrospective cohort study, the researchers included 676 x-rays (from 655 patients) interpreted by radiologists with help from the AI software and 676 x-rays (from 664 patients) previously interpreted by radiologists without AI assistance. The reference standard was defined by consensus reading by two radiologists. The researchers compared the diagnostic accuracy for pneumothorax between the two groups using generalized estimating equations. They performed matching according to whether the radiograph reader and PTNB operator were the same using the greedy method.
Key findings of the study:
- Upon analysis, the researchers found that the incidence of pneumothorax was 18.2% (123 of 676 radiographs) in the CAD-applied group and 22.5% (152 of 676 radiographs) in the non-CAD group.
- They noted that the CAD-applied group showed higher sensitivity (85.4% vs 67.1%), negative predictive value (96.8% vs 91.3%), and accuracy (96.8% vs 92.3%) than the non-CAD group.
- They found that the sensitivity for a small amount of pneumothorax improved in the CAD-applied group (pneumothorax of <10%: 74.5% vs 51.4%; pneumothorax of 10%–15%: 92.7% vs 70.2%).
- Among patients with pneumothorax, they noted that 34 of 655 (5.0%) in the non-CAD group and 16 of 664 (2.4%) in the CAD-applied group required subsequent drainage catheter insertion.
The authors concluded, "the implementation of a deep learning-based computer-aided detection (CAD) system in clinical practice improved the sensitivity, negative predictive value, and accuracy of detecting pneumothorax on follow-up chest radiographs after percutaneous transthoracic needle biopsy. We believe that the CAD system can help improve the safety of patients receiving lung biopsy and, furthermore, may be used to promptly detect and timely manage pneumothorax of any cause."
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