背景:再生障碍性贫血(AA),巨幼细胞性贫血(MA),骨髓增生异常综合征(MDS)是常见的贫血疾病。有时很难区分患有这些疾病的患者。
方法:在本文中,提出了一种基于图像处理技术的显微图像红细胞面积测量方法,分析了25例AA患者红细胞面积的差异,64例MA患者,68例MDS患者。
结果:正常人红细胞面积为44.19±3.88、42.09±5.35、52.87±7.68和45.75±8.07μm2,AA患者,MA,和MDS,分别。变异系数为8.78%,10.05%,14.53%,和14.00%,分别,在这些群体中。MA患者红细胞面积显著高于正常人(p<0.001)。与AA和MDS患者相比,MA患者的红细胞面积也显着增加(p<0.001)。红细胞面积与平均红细胞体积(MCV)的相关性分析结果,平均红细胞血红蛋白(MCH),MCH浓度(MCHC),与红细胞分布宽度无显著相关性(p>0.05)。RBC面积的受试者工作特征(ROC)曲线的曲线下面积(AUC)结果分别为0.421、0.580和0.850,在AA患者中(p=0.337),MDS(p=0.237),和MA(p<0.001)。
结论:基于图像处理技术确定外周血涂片中红细胞的面积,可以为MDS和MA患者提供快速有效的诊断支持。特别是对于患有MA和MCV的患者。然而,需要更大的样本研究来找到截止面积值。
BACKGROUND: Aplastic anemia (AA), megaloblastic anemia (MA), and myelodysplastic syndrome (MDS) were common anemic diseases. Sometimes it was difficult to distinguish patients with these diseases.
METHODS: In this article, we proposed one measurement method for the area of red blood cells (RBCs) from microscopic images based on image processing technology and analyzed the differences of the area in 25 patients with AA, 64 patients with MA, and 68 patients with MDS.
RESULTS: The area of RBCs was 44.19 ± 3.88, 42.09 ± 5.35, 52.87 ± 7.68, and 45.75 ± 8.07 μm2 in normal subjects, patients with AA, MA, and MDS, respectively. The coefficients of variation were 8.78%, 10.05%, 14.53%, and 14.00%, respectively, in these groups. The area of RBCs in patients with MA was significantly higher than normal subjects (p < 0.001). Compared with patients with AA and MDS, the area of RBCs in patients with MA was also significantly higher (p < 0.001). The results of correlation analysis between the area of RBCs and mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC), and red cell distribution width showed no significant correlations (p > 0.05). The area under the curve (AUC) results of the Receiver Operating Characteristic (ROC) curves of RBCs area were 0.421, 0.580, and 0.850, respectively, in patients with AA (p = 0.337), MDS (p = 0.237), and MA (p < 0.001).
CONCLUSIONS: Identifying the area of RBCs in peripheral blood smears based on the image processing technology could achieve rapid and efficient diagnostic support for patients with MDS and MA, especially for patients with MA and in combination with MCV. However, a larger sample study is needed to find the cutoff area values.