Background/Objectives:Prostate cancer (PCa) is the most diagnosed cancer in men worldwide. Early diagnosis of the disease provides better treatment options for these patients. Recent studies have demonstrated that plasma-based extracellular vesicle microRNAs (miRNAs) are functionally linked to cancer progression, metastasis, and aggressiveness. The use of magnetic resonance imaging (MRI) as the standard of care provides an overall assessment of prostate disease. Quantitative metrics (radiomics) from the MRI provide a better evaluation of the tumor and have been shown to improve disease detection.Methods:We conducted a study on prostate cancer patients, analyzing baseline blood plasma and MRI data. Exosomes were isolated from blood plasma samples to quantify miRNAs, while MRI scans provided detailed tumor morphology. Radiomics features from MRI and miRNA expression data were integrated to develop predictive models, which were evaluated using ROC curve analysis, highlighting the multivariable model’s effectiveness.Results:Our findings indicate that the univariate feature-based model with the highest Youden’s index achieved average areas under the receiver operating characteristic (ROC) curve of 0.76, 0.82, and 0.84 for miRNA, MR-T2W, and MR-ADC features, respectively, in identifying clinically aggressive (Gleason grade) disease. The multivariable feature-based model yielded an average area under the curve (AUC) of 0.88 and 0.95 using combinations of miRNA markers with imaging features in MR-ADC and MR-T2W, respectively.Conclusions:Our study demonstrates that combining miRNA markers with MRI-based radiomics improves the identification of clinically aggressive prostate cancer.
背景/目的：前列腺癌是全球男性中诊断率最高的癌症。该疾病的早期诊断能为患者提供更佳的治疗选择。近期研究表明，血浆来源的细胞外囊泡微小核糖核酸（miRNAs）在功能上与癌症进展、转移及侵袭性密切相关。磁共振成像作为标准诊疗手段，可对前列腺疾病进行全面评估。基于MRI的定量指标（影像组学）能更精准地评估肿瘤特征，并已被证实可提升疾病检出效能。 方法：本研究针对前列腺癌患者，系统分析了基线血浆样本与MRI数据。通过分离血浆外泌体进行miRNAs定量检测，同时利用MRI扫描获取肿瘤形态学细节。整合MRI影像组学特征与miRNA表达数据构建预测模型，并采用受试者工作特征曲线分析评估模型性能，重点验证多变量模型的有效性。 结果：研究显示，在识别临床侵袭性（格里森分级）疾病时，基于单变量特征的模型（以约登指数最高为标准）分别通过miRNA、MR-T2W和MR-ADC特征获得0.76、0.82和0.84的平均ROC曲线下面积。而基于多变量特征的模型将miRNA标志物分别与MR-ADC、MR-T2W影像特征组合后，平均曲线下面积分别达到0.88和0.95。 结论：本研究证实，联合miRNA标志物与基于MRI的影像组学特征能显著提升临床侵袭性前列腺癌的识别准确率。
肿瘤(癌症)患者之家