| Autor: |
Yiu, Chungwun, Griffith, James Francis, Xiao, Fan, Shi, Lin, Zhou, Bingjing, Wu, Su, Tam, Lai-Shan |
| Předmět: |
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| Zdroj: |
Rheumatology Advances in Practice; 2024, Vol. 8 Issue 3, p1-10, 10p |
| Abstrakt: |
Objective Bone inflammation (osteitis) in early RA (ERA) manifests as bone marrow oedema (BME) and precedes the development of bone erosion. In this prospective, single-centre study, we developed an automated post-processing pipeline for quantifying the severity of wrist BME on T2-weighted fat-suppressed MRI. Methods A total of 80 ERA patients [mean age 54 years (s. d. 12), 62 females] were enrolled at baseline and 49 (40 females) after 1 year of treatment. For automated bone segmentation, a framework based on a convolutional neural network (nnU-Net) was trained and validated (5-fold cross-validation) for 15 wrist bone areas at baseline in 60 ERA patients. For BME quantification, BME was identified by Gaussian mixture model clustering and thresholding. BME proportion (%) and relative BME intensity within each bone area were compared with visual semi-quantitative assessment of the RA MRI score (RAMRIS). Results For automated wrist bone area segmentation, overall bone Sørensen–Dice similarity coefficient was 0.91 (s. d. 0.02) compared with ground truth manual segmentation. High correlation (Pearson correlation coefficient r = 0.928, P < 0.001) between visual RAMRIS BME and automated BME proportion assessment was found. The automated BME proportion decreased after treatment, correlating highly (r = 0.852, P < 0.001) with reduction in the RAMRIS BME score. Conclusion The automated model developed had an excellent segmentation performance and reliable quantification of both the proportion and relative intensity of wrist BME in ERA patients, providing a more objective and efficient alternative to RAMRIS BME scoring. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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