Ożga Joanna, Wyka Michał, Raczko Agata, Tabor Zbisław, Oleniacz Zuzanna, Korman Michał, Wojciechowski Wadim
Department of Radiology, Jagiellonian University Medical College, ul. Botaniczna 3, 31-503 Krakow, Poland.
Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Adama Mickiewicza 30, 30-059 Krakow, Poland.
J Clin Med. 2023 Jul 24;12(14):4852. doi: 10.3390/jcm12144852.
This study evaluates the performance of a fully automated algorithm to detect active inflammation in the form of bone marrow edema (BME) in iliac and sacral bones, depending on the quality of the coronal oblique plane in patients with axial spondyloarthritis (axSpA). The results were assessed based on the technical correctness of MRI examination of the sacroiliac joints (SIJs). A total of 173 patients with suspected axSpA were included in the study. In order to verify the correctness of the MRI, a deviation angle was measured on the slice acquired in the sagittal plane in the T2-weighted sequence. This angle was located between the line drawn between the posterior edges of S1 and S2 vertebrae and the line that marks the actual plane in which the slices were acquired in T1 and STIR sequences. All examinations were divided into quartiles according to the deviation angle measured in degrees as follows: 1st group [0; 2.2], 2nd group (2.2; 5.7], 3rd group (5.7; 10] and 4th group (10; 29.2]. Segmentations of the sacral and iliac bones were acquired manually and automatically using the fully automated algorithm on the T1 sequence. The Dice coefficient for automated bone segmentations with respect to reference manual segmentations was 0.9820 (95% CI [0.9804, 0.9835]). Examinations of BME lesions were assessed using the SPARCC scale (in 68 cases SPARCC > 0). Manual and automatic segmentations of the lesions were performed on STIR sequences and compared. The sensitivity of detection of BME ranged from 0.58 (group 1) to 0.83 (group 2) versus 0.76 (total), while the specificity was equal to 0.97 in each group. The study indicates that the performance of the algorithm is satisfactory regardless of the deviation angle.
本研究评估了一种全自动算法在检测轴性脊柱关节炎(axSpA)患者髂骨和骶骨中以骨髓水肿(BME)形式存在的活动性炎症方面的性能,该性能取决于冠状斜平面的质量。根据骶髂关节(SIJ)MRI检查的技术正确性对结果进行评估。共有173例疑似axSpA的患者纳入本研究。为了验证MRI的正确性,在T2加权序列矢状面获取的切片上测量偏差角。该角度位于S1和S2椎体后缘之间的连线与标记T1和短TI反转恢复(STIR)序列中切片实际采集平面的连线之间。所有检查根据以度为单位测量的偏差角分为四分位数:第1组[0;2.2],第2组(2.2;5.7],第3组(5.7;10]和第4组(10;29.2]。在T1序列上使用全自动算法手动和自动获取骶骨和髂骨的分割图像。自动骨分割相对于参考手动分割的骰子系数为0.9820(95%置信区间[0.9804,0.9835])。使用脊柱关节炎研究协作组(SPARCC)量表评估BME病变检查结果(68例SPARCC>0)。在STIR序列上对病变进行手动和自动分割并比较。BME检测的灵敏度范围为0.58(第1组)至0.83(第2组),而总体灵敏度为0.76,每组的特异性均等于0.97。该研究表明,无论偏差角如何,该算法的性能均令人满意。