Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning

• Published in: Frontiers in artificial intelligence Vol. 4; p. 627369
• Main Author: Luo, Wei
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 07.06.2021
• Subjects: Artificial Intelligence; cervical cancer; clinical outcome prediction; machine learning; medical image; radiomics; statistical model
• ISSN: 2624-8212; 2624-8212
• DOI: 10.3389/frai.2021.627369

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.