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Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting

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Abstract
Background: Achieving high-spatial-resolution pituitary MRI is challenging because of the trade-off between image noise and spatial resolution. Deep learning-based MRI reconstruction enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice MRI.

Purpose: To assess the diagnostic performance of 1-mm slice thickness MRI with deep learning-based reconstruction (DLR) (hereafter, 1-mm MRI+DLR) compared with 3-mm slice thickness MRI (hereafter, 3-mm MRI) for identifying residual tumor and cavernous sinus invasion in the evaluation of postoperative pituitary adenoma.

Materials and Methods: This single-institution retrospective study included 65 patients (mean age 6 standard deviation, 54 years 6 10; 26 women) who underwent a combined imaging protocol including 3-mm MRI and 1-mm MRI+DLR for postoperative evaluation of pituitary adenoma between August and October 2019. Reference standards for correct diagnosis were established by using all available imaging resources, clinical histories, laboratory findings, surgical records, and pathology reports. The diagnostic performances of 3-mm MRI, 1-mm slice thickness MRI without DLR (hereafter, 1-mm MRI), and 1-mm MRI1DLR for identifying residual tumor and cavernous sinus invasion were evaluated by two readers and compared between the protocols.

Results: The performance of 1-mm MRI+DLR in the identification of residual tumor was comparable to that of 3-mm MRI (area under the receiver operating characteristic curve [AUC], 0.89-0.92 vs 0.85-0.89, respectively; P>.09). In the identification of cavernous sinus invasion, the diagnostic performance of 1-mm MRI+DLR was higher than that of 3-mm MRI (AUC, 0.95-0.98 vs 0.83-0.87, respectively; P<.02). Conventional 1-mm MRI (AUC, 0.82-0.83) showed comparable diagnostic performance to 3-mm MRI (AUC, 0.83-0.87) (P>.38). With 1-mm MRI+DLR, residual tumor was diagnosed in 20 patients and cavernous sinus invasion was diagnosed in 14 patients, in whom these diagnoses were not made with 3-mm MRI.

Conclusion: In the postoperative evaluation of pituitary adenoma, 1-mm slice thickness MRI with deep learning-based reconstruction showed higher diagnostic performance than 3-mm slice thickness MRI in the identification of cavernous sinus invasion and comparable diagnostic performance to 3-mm slice thickness MRI in the identification of residual tumor. (C) RSNA, 2020.
Author(s)
김민재김호성김현진박지은박서영김영훈김상준이준성Mark R Lebel
Issued Date
2021
Type
Article
Keyword
Pituitarydeep learningMRI
DOI
10.1148/radiol.2020200723
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7869
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_pubmed_primary_33141001&amp;context=PC&amp;vid=ULSAN&amp;lang=ko_KR&amp;search_scope=default_scope&amp;adaptor=primo_central_multiple_fe&amp;tab=default_tab&amp;query=any,contains,Thin-Slice%20Pituitary%20MRI%20with%20Deep%20Learning-based%20Reconstruction:%20Diagnostic%20Performance%20in%20a%20Postoperative%20Setting&amp;offset=0&amp;pcAvailability=true
Publisher
RADIOLOGY
Location
미국
Language
영어
ISSN
0033-8419
Citation Volume
298
Citation Number
1
Citation Start Page
114
Citation End Page
122
Appears in Collections:
Medicine > Medicine
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