Gynecological cancer, especially breast, cervical, and ovarian cancer, are significant health issues affecting women worldwide. When screened they are mostly detected at later stages because of non-specific signs and symptoms as well as the unavailability of reliable screening methods. The improvement of early oncologic prediction methods is therefore needed to work out the survival rates, guide individualized treatment, and relieve healthcare pressures. Outcome forecasting and clinical detection are rapidly changing with the use of machine learning (ML), one of the promising technologies used to analyze complex biomedical data. Artificial intelligence (AI)-based ML models are capable of determining low-level trends and making accurate predictions of disease risk and outcomes, because they can combine different datasets (clinical records, genomics, proteomics, medical imaging) and learn to identify subtle patterns. Standard algorithms, including support vector machines, random forests, and deep learning (DL) models, such as convolutional neural networks, have demonstrated high potential in identifying the type of cancer, monitoring disease progression, and designing treatment patterns. This manuscript reviews the recent developments in the use of ML models to advance oncologic prediction tasks in gynecologic oncology. It reports on critical domains, like screening, risk classification, and survival modeling, as well as comments on difficulties, like data inconsistency, inability of interpretation of models, and issues of clinical interpretation. New developments, such as explainable AI, federated learning (FL), and multi-omics fusion, are discussed to develop these models and to make them applicable in practice because of their reliability. Conclusively, this article emphasizes the transformative role of ML in precision oncology to deliver improved, patient-centered outcomes to women who are victims of gynecological cancers.
妇科癌症，尤其是乳腺癌、宫颈癌和卵巢癌，是影响全球女性健康的重大问题。由于症状体征缺乏特异性以及缺乏可靠的筛查方法，这些癌症在筛查时大多已处于晚期阶段。因此，需要改进早期肿瘤预测方法，以提高生存率、指导个体化治疗并缓解医疗压力。随着机器学习（ML）这一用于分析复杂生物医学数据的前沿技术的应用，预后预测和临床检测正在发生快速变革。基于人工智能（AI）的机器学习模型能够整合不同数据集（临床记录、基因组学、蛋白质组学、医学影像）并学习识别细微模式，从而发现潜在规律并对疾病风险与结局作出精准预测。支持向量机、随机森林等传统算法，以及卷积神经网络等深度学习（DL）模型，已在癌症类型识别、疾病进展监测和治疗方案设计方面展现出巨大潜力。本文综述了机器学习模型在妇科肿瘤预测任务中的最新进展，涵盖筛查、风险分层和生存建模等关键领域，同时探讨了数据不一致性、模型可解释性不足及临床转化难题等挑战。文中进一步讨论了可解释人工智能、联邦学习（FL）与多组学融合等新兴发展方向，这些技术通过提升模型可靠性推动其向临床实践转化。最后，本文强调机器学习在精准肿瘤学中的变革性作用，有望为妇科癌症患者提供更优质、以患者为中心的诊疗结局。
肿瘤(癌症)患者之家