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Machine learning sl...
Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction
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- Lidén, Mats, 1976- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Department of Radiology and Medical Physics
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- Spahr, Antoine (författare)
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology School of Technology and Health, Stockholm, Sweden
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- Hjelmgren, Ola (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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- Bendazzoli, Simone (författare)
- Karolinska Institutet
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- Sundh, Josefin, 1972- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Department of Respiratory Medicine
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- Sköld, Magnus (författare)
- Karolinska Institutet
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- Bergström, Göran, 1964 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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- Wang, Chunliang (författare)
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology School of Technology and Health, Stockholm, Sweden
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- Thunberg, Per, 1968- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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(creator_code:org_t)
- Springer, 2024
- 2024
- Engelska.
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Ingår i: European Radiology. - : Springer. - 0938-7994 .- 1432-1084. ; 34:1, s. 39-49
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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http://kipublication...
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https://gup.ub.gu.se...
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Abstract
Ämnesord
Stäng
- OBJECTIVES: Quantitative CT imaging is an important emphysema biomarker, especially in smoking cohorts, but does not always correlate to radiologists' visual CT assessments. The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method.MATERIALS AND METHODS: Separate cohorts were used for algorithm development and validation. For validation, thin-slice CT stacks from 474 participants in the prospective cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) were included, 395 randomly selected and 79 from an emphysema cohort. Spirometry (FEV1/FVC) and radiologists' visual emphysema scores (sum-visual) obtained at inclusion in SCAPIS were used as reference tests. SWES was compared with a commercially available quantitative emphysema scoring method (LAV950) using Pearson's correlation coefficients and receiver operating characteristics (ROC) analysis.RESULTS: SWES correlated more strongly with the visual scores than LAV950 (r = 0.78 vs. r = 0.41, p < 0.001). The area under the ROC curve for the prediction of airway obstruction was larger for SWES than for LAV950 (0.76 vs. 0.61, p = 0.007). SWES correlated more strongly with FEV1/FVC than either LAV950 or sum-visual in the full cohort (r = - 0.69 vs. r = - 0.49/r = - 0.64, p < 0.001/p = 0.007), in the emphysema cohort (r = - 0.77 vs. r = - 0.69/r = - 0.65, p = 0.03/p = 0.002), and in the random sample (r = - 0.39 vs. r = - 0.26/r = - 0.25, p = 0.001/p = 0.007).CONCLUSION: The slice-wise whole-lung emphysema score (SWES) correlates better than LAV950 with radiologists' visual emphysema scores and correlates better with airway obstruction than do LAV950 and radiologists' visual scores.CLINICAL RELEVANCE STATEMENT: The slice-wise whole-lung emphysema score provides quantitative emphysema information for CT imaging that avoids the disadvantages of threshold-based scores and is correlated more strongly with reference tests than LAV950 and reader visual scores.KEY POINTS: • A slice-wise whole-lung emphysema score (SWES) was developed to quantify emphysema in chest CT images. • SWES identified visual emphysema and spirometric airflow limitation significantly better than threshold-based score (LAV950). • SWES improved emphysema quantification in CT images, which is especially useful in large-scale research.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Deep learning
- Lung
- Pulmonary disease
- chronic obstructive
- Pulmonary emphysema
- Tomography
- X-ray computed
- Deep learning
- Lung
- Pulmonary disease
- chronic obstructive
- Pulmonary emphysema
- Tomography
- X-ray computed
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Lidén, Mats, 197 ...
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Spahr, Antoine
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Hjelmgren, Ola
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Bendazzoli, Simo ...
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Sundh, Josefin, ...
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Sköld, Magnus
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Bergström, Göran ...
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Wang, Chunliang
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Thunberg, Per, 1 ...
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European Radiolo ...
- Av lärosätet
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Örebro universitet
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Karolinska Institutet
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Göteborgs universitet