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Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI.
- Source :
-
Abdominal radiology (New York) [Abdom Radiol (NY)] 2024 Sep; Vol. 49 (9), pp. 3078-3087. Date of Electronic Publication: 2024 Apr 20. - Publication Year :
- 2024
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Abstract
- Purpose: To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm.<br />Methods: Patients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI.<br />Results: 286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727).<br />Conclusion: Extended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity.<br /> (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
- Subjects :
- Humans
Male
Female
Retrospective Studies
Middle Aged
Aged
Sensitivity and Specificity
Angiography, Digital Subtraction methods
Image Enhancement methods
Adult
Subtraction Technique
Image Interpretation, Computer-Assisted methods
Aged, 80 and over
Liver Neoplasms diagnostic imaging
Deep Learning
Carcinoma, Hepatocellular diagnostic imaging
Contrast Media
Algorithms
Magnetic Resonance Imaging methods
Subjects
Details
- Language :
- English
- ISSN :
- 2366-0058
- Volume :
- 49
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- Abdominal radiology (New York)
- Publication Type :
- Academic Journal
- Accession number :
- 38642094
- Full Text :
- https://doi.org/10.1007/s00261-024-04277-w