■在这项研究中,我们旨在应用实验室血液分析来识别血液学(基于血红蛋白浓度,红细胞,血细胞比容,和RDW计数)与最常见的消化道恶性肿瘤相关的概况。此外,我们旨在评估这些特征在诊断时如何有助于区分这些肿瘤.
■我们收集了C15食管ICD-10诊断编码日期的数据,C16胃,C18结肠,和184例C19直肠肿瘤。统计分析和数据可视化方法,特别是热图和主成分分析(PCA),允许创建汇总血液学配置文件并确定每种病理状态的最相关参数。在SPSS和Python中进行单变量和多变量数据建模和ROC分析。
■我们的数据揭示了基于肿瘤发展解剖位置的独特模式,从C15食管和C16胃聚集C18结肠和C19直肠。我们发现C16胃癌与其他肿瘤之间存在显着差异,与低血红蛋白浓度的RDW升高密切相关,红细胞,和血细胞比容计数。相比之下,C18结肠癌的红细胞计数较高,允许二元逻辑回归(LR)模型的测试集中的最佳分类度量,占0.77的AUC,94%的灵敏度和52%的特异性。
■这项研究强调了在诊断这些恶性肿瘤时增加血液学模式的重要性。这可能会导致有关在护理点进行剖析和监测的进一步调查。
UNASSIGNED: In this
study, we aimed to apply laboratory blood analysis to identify the hematological (based on hemoglobin concentration, erythrocytes, hematocrit, and RDW count) profiles associated with the most prevalent forms of digestive tract malignancies. Furthermore, we aimed to evaluate how these profiles contributed to distinguishing these tumors at diagnosis.
UNASSIGNED: We collected data from the date of ICD-10 diagnostic coding for C15 esophagus, C16 stomach, C18 colon, and C19 rectum tumors of 184 individuals. The statistical analysis and data visualization approaches, notably the heat map and principal component analysis (PCA), allowed for creating a summary hematological profile and identifying the most associated parameters for each pathologic state. Univariate and multivariate data modeling and ROC analysis were performed in both SPSS and Python.
UNASSIGNED: Our data reveal unique patterns based on tumor development anatomical location, clustering the C18 colon and C19 rectum from the C15 esophagus and C16 stomach. We found a significant difference between C16 stomach carcinoma and the other tumors, which substantially correlated with raised RDW in conjunction with low hemoglobin concentration, erythrocytes, and hematocrit counts. In contrast, C18 colon carcinoma had the higher red blood cell count, allowing for the best classification metrics in the test set of the binary logistic regression (LR) model, accounting for an AUC of 0.77 with 94% sensitivity and 52% specificity.
UNASSIGNED: This
study emphasizes the significance of adding hematological patterns in diagnosing these malignancies, which could path further investigations regarding profiling and monitoring at the point of care.