天津医药

天津医药 ›› 2024, Vol. 52 ›› Issue (12): 1274-1277.doi: 10.11958/20240867

• 临床研究 • 上一篇    下一篇

术前血液学炎症指标对基底细胞癌复发风险的预测价值

孙瑞雪(), 刘霄霄, 岳欣怡, 杨冬梅, 任鲁宁, 王菲, 杜红阳△()   

  1. 锦州医科大学附属第一医院皮肤科（邮编121000）
  • 收稿日期:2024-07-01 修回日期:2024-08-28 出版日期:2024-12-15 发布日期:2024-12-17
  • 通讯作者: △E-mail:4197123@163.com
  • 作者简介:孙瑞雪（1998），女，硕士在读，主要从事皮肤肿瘤方面研究。E-mail:xue13455649773@163.com
  • 基金资助:
    湖北陈孝平科技发展基金会“2022年度免疫性疾病研究槐杞黄专项基金”项目(CXPJJH12203-16);辽宁省研究生教育教学改革研究项目(LNYJG2024216)

Predictive value of preoperative blood inflammatory markers for recurrence risk of basal cell carcinoma

SUN Ruixue(), LIU Xiaoxiao, YUE Xinyi, YANG Dongmei, REN Luning, WANG Fei, DU Hongyang△()   

  1. Department of Dermatology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, China
  • Received:2024-07-01 Revised:2024-08-28 Published:2024-12-15 Online:2024-12-17
  • Contact: △E-mail:4197123@163.com

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摘要：

目的 探讨术前血液炎症指标对基底细胞癌（BCC）患者复发风险的预测价值。方法 将225例BCC患者分为高危复发风险组（高危组）155例和低危复发风险组（低危组）70例，收集患者一般资料和术前血液学指标，计算中性粒细胞与淋巴细胞比值（NLR）、淋巴细胞与单核细胞比值（LMR）、全身炎症标志物（SIM）以及血小板与淋巴细胞比值（PLR）。受试者工作特征（ROC）曲线分析2组间差异有统计学意义的血液学指标对BCC患者复发的预测价值并确定最佳截断值。采用单因素和多因素Logistic回归分析BCC患者复发的影响因素。建立多因素Logistic回归模型作为BCC复发风险预测模型，使用曲线下面积（AUC）与Hosmer-Lemeshow检验评价模型的预测效能与拟合度。结果 ROC曲线分析确定LMR和SIM的最佳截断值为5.12和0.86。单因素Logistic回归分析显示LMR、SIM、肿瘤原发部位溃疡、紫外线暴露、肿瘤最大直径是BCC患者复发的影响因素。多因素Logistic回归表明，SIM≥0.86、肿瘤最大直径≥2.0 cm和紫外线暴露是BCC复发风险的危险因素，而LMR≥5.12则具有保护作用。BCC患者复发风险的Logistic预测模型为Logit（P）=-1.598-1.517×LMR+1.323×SIM+2.406×紫外线暴露+3.465×肿瘤最大直径，预测模型的拟合度较好（P=0.725），预测模型预测BCC患者复发风险的AUC为0.869（95%CI：0.822~0.917）。结论 术前监测LMR和SIM水平能够辅助评估BCC患者的复发风险，为临床决策提供重要参考。

关键词: 癌, 基底细胞, 肿瘤复发, 局部, ROC曲线, Logistic模型, 淋巴细胞与单核细胞比值, 全身炎症标志物

Abstract:

Objective To investigate the predictive value of preoperative blood inflammatory markers for the recurrence risk in patients with basal cell carcinoma (BCC). Methods A total of 225 patients with BCC were divided into the high-risk recurrence group (155 cases) and the low-risk recurrence group (70 cases). General information and preoperative hematological indicators were collected in the two groups of patients. The neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammation marker (SIM) and platelet-to-lymphocyte ratio (PLR) were calculated. Receiver operating characteristic (ROC) curves were used to determine the predictive value of hematological markers with statistically significant differences between the two groups for BCC recurrence and to establish optimal cutoff values. Univariate and multivariate Logistic regression analyses were conducted to identify factors influencing BCC recurrence. A multivariate Logistic regression model was established to predict the recurrence risk of BCC. Area under the curve (AUC) and the Hosmer-Lemeshow test were used to evaluate the prediction efficiency and goodness-of-fit of the model. Results ROC analysis identified that optimal cutoff values for LMR and SIM were 5.12 and 0.86, respectively. Univariate Logistic regression analysis showed that LMR, SIM, ulceration at the primary tumor site, UV exposure and tumor maximum diameter were factors influencing BCC recurrence. Multivariate Logistic regression revealed that SIM ≥ 0.86, tumor maximum diameter ≥ 2.0 cm and UV exposure were risk factors for BCC recurrence, while LMR ≥ 5.12 had a protective effect. The Logistic prediction model for BCC recurrence risk was Logit (P) = -1.598 - 1.517 × LMR + 1.323 × SIM + 2.406 × UV exposure + 3.465 × tumor maximum diameter, with good model fit (P = 0.725) and an AUC of 0.869 (95% CI: 0.822-0.917) for predicting BCC recurrence risk. Conclusion Monitoring preoperative LMR and SIM levels can assist in assessing the risk of recurrence in BCC patients and provide important guidance for clinical decision-making.

Key words: carcinoma, basal cell, neoplasm recurrence, local, ROC curve, Logistic models, lymphocyte-to-monocyte ratio, systemic inflammatory marker

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