Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning.
Objectives: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).Materials and Methods: The training set in this retrospective study consisted of 590 patients (460 with APE and 130...
| Publicado en: | European Radiology Vol. 30; no. 6; pp. 3567 - 3576 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jun2020
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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=143397317&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143397317 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2020 vid: 30 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143397317 143397317 NLM32064559 143397317 10.1007/s00330-020-06699-8 NLM32064559 143397317 ppf: 3567 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning. aug: au: Liu, Weifang Liu, Min Guo, Xiaojuan Zhang, Peiyao Zhang, Ling Zhang, Rongguo Kang, Han Zhai, Zhenguo Tao, Xincao Wan, Jun Xie, Sheng affil: Peking University Health Science Center, 100871, Beijing, China sug: subj: Pulmonary Embolism Ventricular Function, Right Heart Ventricle Sensitivity and Specificity Aged Middle Age Acute Disease Retrospective Design Pulmonary Embolism Physiopathology Adult Reproducibility of Results Male Female Human Funding Source Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Male Female ab: Objectives: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).Materials and Methods: The training set in this retrospective study consisted of 590 patients (460 with APE and 130 without APE) who underwent CTPA. A fully deep learning convolutional neural network (DL-CNN), called U-Net, was trained for the segmentation of clot. Additionally, an in-house validation set consisted of 288 patients (186 with APE and 102 without APE). In this study, we set different probability thresholds to test the performance of U-Net for the clot detection and selected sensitivity, specificity, and area under the curve (AUC) as the metrics of performance evaluation. Furthermore, we investigated the relationship between the clot burden assessed by the Qanadli score, Mastora score, and other imaging parameters on CTPA and the clot burden calculated by the DL-CNN model.Results: There was no statistically significant difference in AUCs with the different probability thresholds. When the probability threshold for segmentation was 0.1, the sensitivity and specificity of U-Net in detecting clot respectively were 94.6% and 76.5% while the AUC was 0.926 (95% CI 0.884-0.968). Moreover, this study displayed that the clot burden measured with U-Net was significantly correlated with the Qanadli score (r = 0.819, p < 0.001), Mastora score (r = 0.874, p < 0.001), and right ventricular functional parameters on CTPA.Conclusions: DL-CNN achieved a high AUC for the detection of pulmonary emboli and can be applied to quantitatively calculate the clot burden of APE patients, which may contribute to reducing the workloads of clinicians.Key Points: • Deep learning can detect APE with a good performance and efficiently calculate the clot burden to reduce the physicians' workload. • Clot burden measured with deep learning highly correlates with Qanadli and Mastora scores of CTPA. • Clot burden measured with deep learning correlates with parameters of right ventricular function on CTPA. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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