Details

Autor(en) / Beteiligte

• Hwang, Eui Jin
• Hong, Jung Hee
• Lee, Kyung Hee
• Kim, Jung Im
• Nam, Ju Gang
• Kim, Da Som
• Choi, Hyewon
• Yoo, Seung Jin
• Goo, Jin Mo
• Park, Chang Min

Titel

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study

Ist Teil von

• European radiology, 2020-07, Vol.30 (7), p.3660-3671

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2020

Quelle

SpringerLink

Beschreibungen/Notizen

• Objectives 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. Methods 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. Results 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. Conclusion 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. Key Points • 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.