Column chart prediction model for ovarian cancer based on serum ovarian tumor related biomarkers and validation

IF 2.5 4区医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL

Advances in medical sciences Pub Date : 2025-03-01 DOI:10.1016/j.advms.2025.03.001

Yuting Song , Libo Liu , Jie Gao , Naibao Wu , Jiwei Yin

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引用次数: 0

Abstract

Purpose

The aim was to study the predictive model and validate serum ovarian tumor-related biomarkers for ovarian cancer histograms.

Method

We randomly selected 181 patients with ovarian tumors and 80 healthy individuals who underwent physical examinations from the hospital's medical record information system as the study participants. Clinical data and detection results of ovarian tumor-related markers such as serum carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), carbohydrate antigen 19-9 (CA19-9), and human epididymal protein (HE4) were collected from all study participants for analysis.

Result

Significant differences were found in serum CEA, CA125, CA19-9, and HE4 levels between healthy controls, benign ovarian tumors, and ovarian cancer (P < 0.05). Dysmenorrhea (present), family history (present), age at menarche, menstrual period, number of pregnancies, natural abortion frequency, number of induced abortions, CEA, CA125, CA19-9, HE4 were all influencing factors for the incidence of ovarian cancer (P < 0.05). The number of induced abortions, CEA, CA125, CA19-9, and HE4 were all independent risk factors for ovarian cancer, while the natural abortion frequency was a protective factor for ovarian cancer (P < 0.05). The constructed column chart prediction model had good discrimination and prediction accuracy for ovarian cancer, good clinical utility, and higher predictive performance for ovarian cancer than traditional ROMA models.

Conclusion

The ovarian cancer column chart prediction model based on serum ovarian tumor related markers has good discrimination and prediction accuracy for ovarian cancer, with high clinical utility. Future research may need to incorporate more serum markers related to ovarian cancer to further improve the performance of predictive models.

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目的：研究卵巢癌直方图的预测模型并验证血清卵巢肿瘤相关生物标志物。方法：从医院病历信息系统中随机抽取181例卵巢肿瘤患者和80例健康体检者作为研究对象。收集所有受试者血清癌胚抗原（CEA）、碳水化合物抗原125 （CA125）、碳水化合物抗原19-9 （CA19-9）、人附睾蛋白（HE4）等卵巢肿瘤相关标志物的临床资料及检测结果进行分析。结果：健康对照、良性卵巢肿瘤及卵巢癌患者血清CEA、CA125、CA19-9、HE4水平差异均有统计学意义(p)。结论：基于血清卵巢肿瘤相关标志物的卵巢癌柱状图预测模型对卵巢癌有较好的鉴别和预测准确性，具有较高的临床应用价值。未来的研究可能需要纳入更多与卵巢癌相关的血清标志物，以进一步提高预测模型的性能。

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

Advances in medical sciences 医学-医学：研究与实验

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

25 days

期刊介绍： Advances in Medical Sciences is an international, peer-reviewed journal that welcomes original research articles and reviews on current advances in life sciences, preclinical and clinical medicine, and related disciplines. The Journal’s primary aim is to make every effort to contribute to progress in medical sciences. The strive is to bridge laboratory and clinical settings with cutting edge research findings and new developments. Advances in Medical Sciences publishes articles which bring novel insights into diagnostic and molecular imaging, offering essential prior knowledge for diagnosis and treatment indispensable in all areas of medical sciences. It also publishes articles on pathological sciences giving foundation knowledge on the overall study of human diseases. Through its publications Advances in Medical Sciences also stresses the importance of pharmaceutical sciences as a rapidly and ever expanding area of research on drug design, development, action and evaluation contributing significantly to a variety of scientific disciplines. The journal welcomes submissions from the following disciplines: General and internal medicine, Cancer research, Genetics, Endocrinology, Gastroenterology, Cardiology and Cardiovascular Medicine, Immunology and Allergy, Pathology and Forensic Medicine, Cell and molecular Biology, Haematology, Biochemistry, Clinical and Experimental Pathology.