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Towards a data-driven system for personalized cervical cancer risk stratification.

Geir Severin R E Langberg1, Jan F Nygård2, Vinay Chakravarthi Gogineni3

  • 1Department of Research, Cancer Registry of Norway (CRN), Oslo, 0379, Norway. langberg91@gmail.com.

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Personalized cervical cancer risk prediction can be improved using machine learning on longitudinal screening data. This approach addresses data imbalance and changing habits to better identify women needing further screening.

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Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning

Background:

  • Cervical cancer screening programs in Nordic countries have reduced incidence and mortality.
  • Current one-size-fits-all screening strategies may lead to over-screening in low-risk populations.
  • Personalized risk estimation is needed to optimize screening and identify high-grade lesions.

Purpose of the Study:

  • To develop and compare machine learning risk estimators for personalized cervical cancer prediction.
  • To address challenges of rare disease, imbalanced data, and non-stationary screening habits.
  • To treat cervical cancer risk prediction as a longitudinal forecasting problem.

Main Methods:

  • Utilized longitudinal screening histories from the Cancer Registry of Norway.
  • Extended existing frameworks using incremental learning for longitudinal predictions.
  • Adapted imbalanced classification strategies for non-stationary data.

Main Results:

  • Initial models showed bias towards normal results due to imbalanced data.
  • Adapted strategies improved identification of at-risk individuals.
  • Estimated absolute risk curves closely reflected observed risks in hold-out data.

Conclusions:

  • Longitudinal machine learning models show promise for cervical cancer risk stratification.
  • Personalized screening recommendations can be improved by these advanced prediction models.
  • This approach offers a more targeted strategy than current population-level screening.