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Review
. 2021 Jun 7:4:627369.
doi: 10.3389/frai.2021.627369. eCollection 2021.

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

Wei Luo  1
Affiliations
Review

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

Wei Luo. Front Artif Intell. .

Abstract

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.

Keywords: cervical cancer; clinical outcome prediction; machine learning; medical image; radiomics; statistical model.

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Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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