Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.
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 cl...
| Publicado en: | European Radiology Vol. 30; no. 7; pp. 3660 - 3672 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143855660&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143855660 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2020 vid: 30 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143855660 143855660 144164640 NLM32162001 143855660 10.1007/s00330-020-06771-3 NLM32162001 143855660 ppf: 3660 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study. aug: au: 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 affil: Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, South Korea sug: subj: Lung Biopsy, Needle Adverse Effects Lung Pathology Pneumothorax Retrospective Design Prospective Studies Female Pharmacokinetics Male Pneumothorax Etiology ROC Curve Sensitivity and Specificity Radiography, Thoracic Adult Middle Age Aged Human Algorithms Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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