Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation.
We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards defined by thoracic radiologists.
Pneumothorax occurred in 17.5% (308/1757) of cases, out of which 16.6% (51/308) required catheter drainage. The AUROC, sensitivity, and specificity of the algorithm were 0.937, 70.5%, and 97.7%, respectively, for identification of pneumothorax. The algorithm exhibited higher sensitivity (70.2% vs. 55.5%, p < 0.001) and lower specificity (97.7% vs. 99.8%, p < 0.001), compared with those of radiology reports. In the reader study, the algorithm exhibited lower sensitivity (77.3% vs. 81.8–97.7%) and higher specificity (97.6% vs. 81.7–96.0%) than the radiologists.
The deep learning algorithm appropriately identified pneumothorax in post-biopsy CRs in consecutive diagnostic cohorts. It may assist in accurate and timely diagnosis of post-biopsy pneumothorax in clinical practice.
• A deep learning algorithm can identify chest radiographs with post-biopsy pneumothorax in multicenter consecutive cohorts reflecting actual clinical situation.
• The deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.
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American College of Chest Physicians
Area under the receiver operating characteristic curve
British Thoracic Society
Negative predictive value
Positive predictive value
Percutaneous transthoracic needle biopsy
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Lunit Inc. provided technical supports in the present study, for analyses of chest radiographs with deep learning algorithm. The funding sources and Lunit Inc. did not have any role either in the design of the study or in the acquisition, analyses, and interpretation of the data and in the manuscript preparation.
This study has received funding by the Seoul National University Hospital Research fund (grant number 03-2019-0190) and the Seoul Research & Business Development Program (grant number FI170002).
The scientific guarantor of this publication is Chang Min Park.
Conflict of interest
The authors of this manuscript declare relationships with Lunit Inc.
Jin Mo Goo and Chang Min Park report grants from the Lunit Inc., outside the present study.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the institutional review board.
Institutional review board approval was obtained.
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Hwang, E.J., Hong, J.H., Lee, K.H. et al. Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study. Eur Radiol 30, 3660–3671 (2020). https://doi.org/10.1007/s00330-020-06771-3
- Thoracic radiography
- Needle biopsy
- Artificial intelligence
- Deep learning