Cited 5 times in
Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma
DC Field | Value | Language |
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dc.contributor.author | 남여경 | - |
dc.date.accessioned | 2024-08-02T00:04:12Z | - |
dc.date.available | 2024-08-02T00:04:12Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.issn | 0720-048X | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/200145 | - |
dc.description.abstract | Purpose: To compare performance of 1-mm deep learning reconstruction (DLR) with 3-mm routine MRI imaging for the delineation of pituitary axis and identification of cavernous sinus invasion for pituitary macroadenoma. Method: This retrospective study included 104 patients (59.4 ± 13.1 years; 46 women) who underwent an MRI protocol including 1-mm deep learning-reconstructed and 3-mm routine images for evaluating pituitary adenoma between August 2019 and October 2020. Five readers (24, 9, 2 years, and <1 year of experience) assessed the delineation of pituitary axis (gland and stalk) and the presence of cavernous sinus invasion for using a pairwise design. The signal-to-noise ratio (SNR) was measured. Diagnostic performance as well as image preference data were analysed and compared according to the readers' experience using the McNemar test. Results: For delineation of normal pituitary axis, all readers preferred thin 1-mm DLR MRI over 3-mm MRI (overall superiority, 55.8 %, P <.001), with this preference being greater in the less experienced readers (92.3 % vs. 55.8 % [expert], P <.001). The readers showed higher diagnostic performance for cavernous sinus invasion on 1-mm (AUC, 0.91 and 0.92) than on 3-mm imaging (AUC, 0.87 and 0.88). The SNR of the 1-mm DLR was 1.21-fold higher than that of the routine 3-mm imaging. Conclusion: Deep learning reconstruction-based 1-mm imaging demonstrates improved image quality and better delineation of microstructure in the sellar fossa and is preferred by both radiologists and non-radiologist physicians, especially in less experienced readers. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Elsevier Science Ireland Ltd | - |
dc.relation.isPartOf | EUROPEAN JOURNAL OF RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adenoma* / diagnostic imaging | - |
dc.subject.MESH | Adenoma* / surgery | - |
dc.subject.MESH | Cavernous Sinus* / diagnostic imaging | - |
dc.subject.MESH | Deep Learning* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Image Processing, Computer-Assisted | - |
dc.subject.MESH | Magnetic Resonance Imaging / methods | - |
dc.subject.MESH | Neoplasm Invasiveness | - |
dc.subject.MESH | Pituitary Diseases* | - |
dc.subject.MESH | Pituitary Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Pituitary Neoplasms* / surgery | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma | - |
dc.type | Article | - |
dc.contributor.college | Others | - |
dc.contributor.department | Severance Hospital (세브란스병원) | - |
dc.contributor.googleauthor | Hyeryeong Park 1, Yeo Kyung Nam 2, Ho Sung Kim 3, Ji Eun Park 4, Da Hyun Lee 5, Joonsung Lee 6, Seonok Kim 7, Young-Hoon Kim 8 | - |
dc.identifier.doi | 10.1016/j.ejrad.2022.110647 | - |
dc.contributor.localId | A06627 | - |
dc.relation.journalcode | J00845 | - |
dc.identifier.eissn | 1872-7727 | - |
dc.identifier.pmid | 36527773 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0720048X22004971 | - |
dc.subject.keyword | Cavernous sinus | - |
dc.subject.keyword | Deep learning-based reconstruction | - |
dc.subject.keyword | Gland | - |
dc.subject.keyword | Pituitary adenoma | - |
dc.subject.keyword | Stalk | - |
dc.contributor.alternativeName | Nam, Yeo Kyung | - |
dc.contributor.affiliatedAuthor | 남여경 | - |
dc.citation.volume | 158 | - |
dc.citation.startPage | 110647 | - |
dc.identifier.bibliographicCitation | EUROPEAN JOURNAL OF RADIOLOGY, Vol.158 : 110647, 2023-01 | - |
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