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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review

  • 0Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Cancer Reports (hoboken, N.j.) +

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March 19, 2025

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March 19, 2025

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View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning (ML) models show promise for predicting ovarian cancer (OC) survival, but accuracy and interpretability challenges remain. Integrating diverse data types is key to improving prognostic precision.

Area Of Science

  • Oncology
  • Medical Informatics
  • Data Science

Background

  • Ovarian cancer (OC) survival prediction is critical for patient management.
  • Machine learning (ML) offers advanced analytical capabilities for prognostic modeling.
  • Systematic review to assess ML algorithm effectiveness in OC survival prediction.

Purpose Of The Study

  • Evaluate ML algorithms for predicting overall survival (OS), recurrence-free survival (RFS), progression-free survival (PFS), and treatment response in ovarian cancer.
  • Identify key features influencing the predictive accuracy of ML models for OC.
  • Assess the current landscape and future directions of ML in ovarian cancer prognostication.

Main Methods

  • Systematic literature search of PubMed, Scopus, Web of Science, and Cochrane databases.
  • Inclusion of 32 studies published within the last decade, with a focus on recent advancements post-2021.
  • Analysis of commonly used ML algorithms (e.g., random forest, SVM, deep learning) and evaluation metrics (AUC, C-index, accuracy).

Main Results

  • Common ML algorithms include random forest, support vector machines, logistic regression, XGBoost, and deep learning.
  • Area Under the Curve (AUC), concordance index (C-index), and accuracy are frequently used evaluation metrics.
  • Significant predictors identified include age at diagnosis, tumor stage, CA-125 levels, and treatment factors.

Conclusions

  • ML models show significant potential for predicting ovarian cancer survival outcomes.
  • Challenges in model accuracy and interpretability need to be addressed.
  • Integrating diverse data types (clinical, imaging, molecular) with multimodal ML approaches is crucial for enhanced prognostic precision.

Keywords:

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