人工智能和机器学习模型在癌症预后和诊断中的应用
Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.
摘要(中文译文)
卵巢癌（OC）是妇科恶性肿瘤中的主要致死原因，由于其无症状特征及缺乏有效的筛查技术，常在晚期才被发现。超声（US）、磁共振成像（MRI）和计算机断层扫描（CT）等影像技术对诊断至关重要，但传统方法在很大程度上依赖放射科医师的主观评估。人工智能（AI）和影像组学提供了数据驱动的方法，可从医学图像中提取定量特征，从而实现更准确、更个性化的诊断和预后评估。本综述重点阐述AI在改进CA-125、HE4和微小RNA等生物标志物分析中的作用，并探讨将多组学数据（基因组学、转录组学、表观基因组学等）与影像数据整合以增强预测模型的潜力。影像组学通过从医学图像中提取高维特征，在鉴别良性与恶性肿瘤、预测基因突变（如BRCA）以及评估肿瘤异质性方面已展现出良好前景。人工智能（AI）模型，尤其是深度学习（DL）算法，在诊断卵巢癌和预测患者结局方面已表现出高准确度，往往优于传统方法。
Abstract
Ovarian cancer (OC) is a predominant cause of fatality amongst gynecological malignancies, frequently identified at its later stages owing to its asymptomatic characteristics and the absence of adequate screening techniques. Imaging techniques such as ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT) are crucial for diagnosis, but traditional methods rely heavily on subjective evaluations by radiologists. AI and radiomics offer a data-driven approach to extract quantitative features from medical images, enabling more accurate and personalized diagnosis and prognosis. This review highlights the role of AI in improving the analysis of biomarkers like CA-125, HE4, and microRNAs, and discusses the potential of integrating multiomics data (genomics, transcriptomics, epigenomics, etc.) with imaging data to enhance predictive models. Radiomics, which involves extracting high-dimensional features from medical images, has shown promise in differentiating between benign and malignant tumors, predicting genetic mutations (e.g., BRCA), and assessing tumor heterogeneity. Artificial intelligence (AI) models, particularly deep learning (DL) algorithms, have demonstrated high accuracy in diagnosing OC and predicting patient outcomes, often outperforming traditional methods.
如何引用
AMA
Dina M Khodeer, Celestin Ukozehasi, Ali M Alaseem, Sally M Abdelmonem. Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1743601.
APA
Dina M Khodeer, Celestin Ukozehasi, Ali M Alaseem, Sally M Abdelmonem (2026). Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1743601
GB/T 7714
Dina M Khodeer, Celestin Ukozehasi, Ali M Alaseem, Sally M Abdelmonem. Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1743601.
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