Rapid prediction of cervical cancer and high-grade precursor lesions: An integrated approach using low-field 1H NMR and chemometric analysis

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-05-05 DOI:10.1016/j.cca.2025.120346

Luana Quadros de Souza Leão , Jelmir Craveiro de Andrade , Giovanna Melo Marques , Cristiane Cunha Guimarães , Rosyana de Fátima Vieira de Albuquerque , Alessandra Silva e Silva , Kamila Pereira de Araujo , Mikele Praia de Oliveira , Anderson Ferreira Gonçalves , Higino Felipe Figueiredo , Daniel Lourenço Lira , Marina Amaral Alves , Carlos Adam Conte-Junior , Priscila Ferreira de Aquino

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引用次数: 0

Abstract

Cervical cancer (CC) is a significant cause of morbidity and mortality in women, often preceded by high-grade cervical intraepithelial lesions (HSIL). Although conventional cytology (Pap smear) is widely used for screening, its sensitivity limitations and high false-positive rate reinforce the need for complementary methods. This study investigated the feasibility of low-field ¹H NMR spectroscopy combined with chemometric modeling to differentiate healthy individuals (CON) from patients with HSIL and CC. Principal Component Analysis (PCA) was applied to explore metabolic patterns and identify relevant spectral variables in group differentiation. PCA1 highlighted the separation between CC and the other groups, while PCA2 and PCA3 evidenced intermediate metabolic characteristics in HSIL, reinforcing its role as a transition stage. Three classification scenarios were evaluated using Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA): (1) CON as target class, HSIL/CC as outclass classes; (2) HSIL as target class, CON as outclass class; and (3) CC as target class, CON as outclass class. Calibration was optimal (100 % SEN, SPE, ACC, MCC), and prediction showed higher efficacy in detecting CC (SPE = 100 %, MCC = 70 %), indicating that the model was more efficient in screening cervical cancer cases. Furthermore, low-field ¹H NMR has demonstrated potential as a metabolomic screening tool. It is a promising alternative due to its greater accessibility, lower operational cost, and non-invasive nature, complementing traditional methods of early detection of tumors and cervical lesions.

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快速预测宫颈癌和高级别前驱病变：使用低场1H NMR和化学计量分析的综合方法

宫颈癌（CC）是女性发病率和死亡率的重要原因，通常先于高级别宫颈上皮内病变（HSIL）。尽管传统细胞学（巴氏涂片）广泛用于筛查，但其敏感性限制和高假阳性率加强了对补充方法的需求。本研究探讨了低场1H核磁共振波谱结合化学计量学建模区分健康个体（CON）与HSIL和CC患者的可行性，并应用主成分分析（PCA）探索代谢模式，确定群体分化的相关光谱变量。PCA1强调了CC与其他组之间的分离，而PCA2和PCA3在HSIL中证明了中间代谢特征，加强了其作为过渡阶段的作用。采用数据驱动的类类比软独立建模（DD-SIMCA）对三类分类场景进行了评价：(1)CON为目标类，HSIL/CC为外类；(2) HSIL为目标类，CON为外类；(3) CC为目标类，CON为外类。校正效果最佳（100% SEN、SPE、ACC、MCC），预测CC的检测效果较高（SPE = 100%, MCC = 70%），表明该模型在宫颈癌筛查中更有效。此外，低场1H NMR已经证明了作为代谢组学筛选工具的潜力。它是一种很有前途的替代方法，因为它更容易获得，更低的操作成本和非侵入性，补充了传统的早期检测肿瘤和宫颈病变的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.