基于脂蛋白相关磷脂酶A2和血清生物标志物水平联合的结直肠癌肝转移预测模型

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-01-16 DOI:10.1016/j.cca.2025.120143

Sisi Feng , Manli Zhou , Zixin Huang , Xiaomin Xiao , Baiyun Zhong

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引用次数: 0

摘要

目的：探讨血清脂蛋白相关磷脂酶A2 （Lp-PLA2）在结直肠癌肝转移（CRLM）患者中的预测价值。方法：本研究共招募了507名参与者，其中包括162名健康对照（hc）， 186名非CRLM患者和159名CRLM患者。在这三组中测量血清Lp-PLA2水平，并使用机器学习（ML）算法结合传统血清学标记物建立CRLM预测模型。使用受试者工作特征（ROC）曲线下面积（AUC）、敏感性、特异性和其他相关指标评估每种模型的性能。结果：与hcc组和非CRLM组相比，CRLM患者血清Lp-PLA2水平显著升高(P)。结论：结合血清Lp-PLA2水平和常规实验室参数的随机森林模型对CRLM具有较强的预测能力，有望提高CRLM患者的早期发现。

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A colorectal liver metastasis prediction model based on the combination of lipoprotein-associated phospholipase A2 and serum biomarker levels

Objective

This study aims to assess the predictive value of serum lipoprotein-associated phospholipase A2 (Lp-PLA2) in colorectal liver metastasis (CRLM) patients.

Methods

A total of 507 participants were recruited for this study, comprising 162 healthy controls (HCs), 186 non-CRLM patients, and 159 CRLM patients. Serum Lp-PLA2 levels were measured across these three groups, and a CRLM prediction model was developed using machine learning (ML) algorithms in conjunction with traditional serological markers. The performance of each model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and other relevant metrics.

Results

The serum Lp-PLA2 levels in CRLM patients were significantly elevated compared to those in HCs group and the non-CRLM group (P < 0.0001). The CRLM prediction model developed using the Random forest algorithm demonstrated superior performance, incorporating six features: Lp-PLA2, ALB, GLB, ALT, LDH, and TC. This model achieved an AUC of 0.918, with a sensitivity of 0.823, specificity of 0.889, positive predictive value (PPV) of 0.861, and negative predictive value (NPV) of 0.857.

Conclusion

The Random forest model, incorporating serum Lp-PLA2 level and conventional laboratory parameters, demonstrates robust predictive capability for CRLM and holds promise for enhancing early detection in CRLM patients.

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.