Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n= 77), patients with benign uterine tumors (n= 23), and control gynecological patients attending regular checkups or follow-ups (n= 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods.
子宫内膜癌发病率日益上升，亟需开发兼具高效性与非侵入性的诊断方法。本研究探讨尿液荧光光谱技术作为子宫内膜癌潜在诊断工具的应用价值。研究采集了子宫内膜癌患者（n=77）、良性子宫肿瘤患者（n=23）及定期体检或随访的妇科对照患者（n=96）的尿液样本，采用同步荧光光谱技术分析样本总荧光代谢组谱，并通过构建特异性荧光比值实现对照样本、良性样本与恶性样本的鉴别。这些光谱标志物展现出高达80%的曲线下面积（AUC），具有临床转化潜力。研究采用偏最小二乘判别分析（PLS-DA）进行数据降维与类别区分增强，同时运用随机森林（RF）、逻辑回归（LR）、支持向量机（SVM）和随机梯度下降（SGD）等机器学习模型区分对照组与子宫内膜癌患者。PLS-DA模型总体准确率达79%，AUC达90%。这些积极结果表明，尿液荧光光谱技术结合先进机器学习模型有望革新子宫内膜癌诊断体系，为现有诊断方法提供快速、准确且非侵入性的替代方案。
肿瘤(癌症)患者之家