Xiao Li, Li Hui, Jin Yanyang
Department of Obstetrics and Gynecology, Jingzhou Hospital, Yangtze University, Jingzhou, 434020, China.
Department of Urology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121001, China.
Sci Rep. 2024 Oct 2;14(1):22887. doi: 10.1038/s41598-024-71863-9.
Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient's serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection.
卵巢癌是一种常见的妇科肿瘤，死亡率高，临床治疗困难。早期发现卵巢癌具有重要的诊断价值。针对传统早期诊断方法诊断性能不佳的问题，本文设计了一种自动化早期卵巢癌检测系统，以提高早期卵巢癌的检测水平。传统的早期诊断方法包括血清CA125（糖类抗原125）检测和正电子发射断层扫描/计算机断层扫描（PET/CT）成像。本文将血清CA125检测与PET/CT成像相结合，检测患者血清中的CA125水平和最大标准化摄取值（SUV）。当CA125水平超过35U/ml且最大SUV值超过2.5时，该检测被视为阳性。本文选取荆州市医院200例患者进行实验，并比较了三种检测方法。50岁以下患者中，单项血清CA125检测、单项PET/CT成像及自动化检测的平均特异性分别为61.24%、79.57%和97.79%。本文设计的自动化早期卵巢癌检测系统可显著提高早期卵巢癌检测的特异性，对早期卵巢癌检测具有优异的应用价值。
