Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system.
| Autor: | Blazis SP; Department of Clinical Physics, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands. Electronic address: s.blazis@asz.nl., Dickerscheid DBM; Department of Clinical Physics, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands., Linsen PVM; Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands., Martins Jarnalo CO; Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands. |
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| Jazyk: | angličtina |
| Zdroj: | European journal of radiology [Eur J Radiol] 2021 Mar; Vol. 136, pp. 109526. Date of Electronic Publication: 2021 Jan 08. |
| DOI: | 10.1016/j.ejrad.2021.109526 |
| Abstrakt: | Purpose: To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmonary nodule CAD system. Materials and Methods: We performed a retrospective analysis of 24 chest CT scans, reconstructed at 16 different reconstruction settings for two different iterative reconstruction algorithms (SAFIRE and ADMIRE) varying in slice thickness, kernel size and iterative reconstruction level strength using a commercially available deep learning pulmonary nodule CAD system. The DL-CAD software was evaluated at 25 different sensitivity threshold settings and nodules detected by the DL-CAD software were matched against a reference standard based on the consensus reading of three radiologists. Results: A total of 384 CT reconstructions was analysed from 24 patients, resulting in a total of 5786 found nodules. We matched the detected nodules against the reference standard, defined by a team of thoracic radiologists, and showed a gradual drop in recall, and an improvement in precision when the iterative strength levels were increased for a constant kernel size. The optimal DL-CAD threshold setting for use in our clinical workflow was found to be 0.88 with an F Conclusions: The DL-CAD system behaves differently on IR data than on FBP data, there is a gradual drop in recall, and growth in precision when the iterative strength levels are increased. As a result, caution should be taken when implementing deep learning software in a hospital with multiple CT scanners and different reconstruction protocols. To the best of our knowledge, this is the first study that demonstrates this result from a DL-CAD system on clinical data. (Copyright © 2021 Elsevier B.V. All rights reserved.) |
| Databáze: | MEDLINE |
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