乳腺癌是一种普遍的全球健康问题,其特征是乳腺组织中细胞生长不受控制。2020年,全球报告了约230万例病例。2018年,印度记录了162,468例新病例和87,090例死亡。早期诊断对于降低死亡率至关重要。我们的研究集中在使用诸如甘油三酸酯-血糖指数和血液学标志物之类的标志物来区分良性和恶性乳腺肿块。
■一项前瞻性横断面研究包括有乳房肿块主诉的女性患者。目标样本大小为200。数据收集包括病史,临床乳房检查,乳房X线照相术,通过细针穿刺细胞学(FNAC)进行细胞学评估,和血液样本收集。分析的参数包括中性粒细胞与淋巴细胞比率(NLR),血小板与淋巴细胞比值(PLR),和甘油三酯血糖指数(TyG)。组织病理学检查证实了FNAC结果。统计分析,包括倾向得分匹配,Kolmogorov-Smirnov测试,Mann-WhitneyU测试,接收者的操作者曲线(ROC)分析,采用SPSS和R软件建立Logistic回归模型。对25名参与者进行了额外的验证。
■这项研究包括200名参与者。良性肿瘤109例,恶性肿瘤91例。倾向得分匹配平衡协变量。NLR在两组之间没有显着差异,而PLR和TyG指数差异显著。NLR与乳腺癌分期密切相关,但不是BI-RADS得分。PLR和TyG指数与BI-RADS评分呈中度正相关。ROC分析用于确定PLR和TyG指数的最佳截断值。结合PLR和TyG指数的Logistic回归模型可显著改善恶性肿瘤预测。
■TyG指数和PLR显示出作为区分乳腺肿块的辅助标记的潜力。NLR与癌症分期相关,但与病变类型无关。结合TyG和PLR改进了预测,协助临床决策,但临床实施需要大规模多中心试验和长期验证.
UNASSIGNED: Breast cancer is a prevalent global health concern characterized by uncontrolled cell growth in breast tissue. In 2020, approximately 2.3 million cases were reported worldwide, with 162,468 new cases and 87,090 fatalities documented in India in 2018. Early diagnosis is crucial for reducing mortality. Our study focused on the use of markers such as the triglyceride-glycemic index and hematological markers to distinguish between benign and malignant breast masses.
UNASSIGNED: A prospective cross-sectional study included female patients with breast mass complaints. The target sample size was 200. Data collection included medical history, clinical breast examination, mammography, cytological assessment via fine-needle aspiration cytology (FNAC), and blood sample collection. The analyzed parameters included neutrophil-to-lymphocyte Ratio (NLR), platelet-to-lymphocyte Ratio (PLR), and triglyceride-glycemic index (TyG). Histopathological examination confirmed the FNAC results. Statistical analysis including propensity score matching, Kolmogorov-Smirnov tests, Mann-Whitney U tests, receiver\'s operator curve (ROC) analysis, and logistic regression models was conducted using SPSS and R Software. Additional validation was performed on 25 participants.
UNASSIGNED: This study included 200 participants. 109 had benign tumors and 91 had malignant tumors. Propensity score matching balanced covariates. NLR did not significantly differ between the groups, while PLR and TyG index differed significantly. NLR correlated strongly with the breast cancer stage, but not with the BI-RADS score. PLR and TyG index showed moderate positive correlations with the BI-RADS score. ROC analysis was used to determine the optimal cutoff values for PLR and TyG index. Logistic regression models combining PLR and TyG index significantly improved malignancy prediction.
UNASSIGNED: TyG index and PLR show potential as adjunctive markers for distinguishing breast masses. NLR correlated with cancer stage but not lesion type. Combining TyG and PLR improves prediction, aiding clinical decisions, but large-scale multicenter trials and long-term validation are required for clinical implementation.