Luo Wei
Department of Radiation Medicine, University of Kentucky, Lexington, KY, United States.
Front Artif Intell. 2021 Jun 7;4:627369. doi: 10.3389/frai.2021.627369. eCollection 2021.
Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use.
宫颈癌是全球女性中一种非常常见且严重的疾病。准确预测其临床结果将有助于调整或优化宫颈癌的治疗并使患者受益。统计模型、各种医学图像以及机器学习已被用于结果预测并取得了有前景的成果。与传统统计模型相比，机器学习在处理大规模数据的复杂性以及发现预后因素方面已展现出优势。它在临床应用和改善宫颈癌管理方面具有巨大潜力。然而，预测研究和预测模型存在局限性，包括简化、数据不足、过拟合以及缺乏可解释性，这表明需要开展更多工作以使临床结果预测更准确、更可靠且更适用于临床使用。
