Hong Youngtaek, Jeong Seri, Park Min-Jeong, Song Wonkeun, Lee Nuri
CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul 03764, Republic of Korea.
Department of Laboratory Medicine, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul 07440, Republic of Korea.
Bioengineering (Basel). 2024 Dec 5;11(12):1230. doi: 10.3390/bioengineering11121230.
Myelodysplastic syndromes (MDSs) are a group of hematologic neoplasms accompanied by dysplasia of bone marrow (BM) hematopoietic cells with cytopenia. Recently, digitalized pathology and pathomics using computerized feature analysis have been actively researched for classifying and predicting prognosis in various tumors of hematopoietic tissues. This study analyzed the pathomic features of hematopoietic cells in BM aspiration smears of patients with MDS according to each hematopoietic cell lineage and dysplasia. We included 24 patients with an MDS and 21 with normal BM. The 12,360 hematopoietic cells utilized were to be classified into seven types: normal erythrocytes, normal granulocytes, normal megakaryocytes, dysplastic erythrocytes, dysplastic granulocytes, dysplastic megakaryocytes, and others. Four hundred seventy-six pathomic features quantifying cell intensity, shape, and texture were extracted from each segmented cell. After comparing the combination of feature selection and machine learning classifier methods using 5-fold cross-validation area under the receiver operating characteristic curve (AUROC), the quadratic discriminant analysis (QDA) with gradient boosting decision tree (AUROC = 0.63) and QDA with eXtreme gradient boosting (XGB) (AUROC = 0.64) showed a high AUROC combination. Through a feature selection process, 30 characteristics were further analyzed. Dysplastic erythrocytes and granulocytes showed lower median values on heatmap analysis compared to that of normal erythrocytes and granulocytes. The data suggest that pathomic features could be applied to cell differentiation in hematologic malignancies. It could be used as a new biomarker with an auxiliary role for more accurate diagnosis. Further studies including prediction survival and prognosis with larger cohort of patients are needed.
骨髓增生异常综合征（MDS）是一组血液系统肿瘤，伴有骨髓（BM）造血细胞发育异常及血细胞减少。近来，利用计算机特征分析的数字化病理学和病理组学已被积极研究，用于造血组织各种肿瘤的分类和预后预测。本研究根据各造血细胞谱系及发育异常情况，分析了MDS患者骨髓穿刺涂片造血细胞的病理组学特征。我们纳入了24例MDS患者和21例骨髓正常者。所使用的12360个造血细胞被分为七种类型：正常红细胞、正常粒细胞、正常巨核细胞、发育异常红细胞、发育异常粒细胞、发育异常巨核细胞及其他。从每个分割细胞中提取了476个量化细胞强度、形状和纹理的病理组学特征。在使用5折交叉验证受试者操作特征曲线下面积（AUROC）比较特征选择和机器学习分类器方法的组合后，梯度提升决策树的二次判别分析（AUROC = 0.63）和极端梯度提升（XGB）的二次判别分析（AUROC = 0.64）显示出较高的AUROC组合。通过特征选择过程，进一步分析了30个特征。与正常红细胞和粒细胞相比，发育异常红细胞和粒细胞在热图分析中显示出较低的中位数。数据表明，病理组学特征可应用于血液系统恶性肿瘤的细胞分化。它可作为一种新的生物标志物，辅助更准确的诊断。需要进一步开展研究，纳入更多患者队列进行生存和预后预测。
