Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
Eur Radiol. 2020 Jul;30(7):3660-3671. doi: 10.1007/s00330-020-06771-3. Epub 2020 Mar 11.
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.
气胸是经皮肺活检后最常见且潜在危及生命的并发症。我们评估了深度学习算法在胸部 X 线片（CR）中检测活检后气胸的性能，该算法在连续队列中反映了实际临床情况。
我们回顾性纳入了来自三个机构的 1757 例连续行经皮肺活检患者的活检后 CR（1055 例男性，702 例女性；平均年龄 65.1 岁）。一种商用深度学习算法分析了每张 CR 以识别气胸。我们将算法的性能与实际临床实践中的放射科报告进行了比较。我们还进行了一项读者研究，其中比较了算法的性能与四位放射科医生的性能。以胸部放射科医生定义的参考标准，通过受试者工作特征曲线（AUROCs）下面积、敏感性和特异性来评估算法和放射科医生的性能。
1757 例患者中 17.5%（308 例）发生气胸，其中 16.6%（51/308）需要导管引流。该算法对气胸的 AUROC、敏感性和特异性分别为 0.937、70.5%和 97.7%。与放射科报告相比，该算法的敏感性（70.2% vs. 55.5%，p < 0.001）更高，特异性（97.7% vs. 99.8%，p < 0.001）更低。在读者研究中，与放射科医生相比，该算法的敏感性（77.3% vs. 81.8-97.7%）较低，特异性（97.6% vs. 81.7-96.0%）较高。
深度学习算法在连续诊断队列的活检后 CR 中适当识别了气胸。它可能有助于在临床实践中准确和及时诊断活检后气胸。
深度学习算法可以识别反映实际临床情况的多中心连续队列中活检后的胸部 X 线片是否存在气胸。
深度学习算法具有作为准确及时诊断活检后气胸的监测工具的潜力。
