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Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Related Experiment Video

Updated: May 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Response to Treatment and Survival in Advanced Ovarian Cancer Using Machine Learning and Radiomics: A

Sabrina Piedimonte1, Mariam Mohamed2, Gabriela Rosa3

  • 1Division of Gynecologic Oncology, Hospital Maisonneuve Rosemont, University of Montreal, Montreal, QC H3T 1J4, Canada.

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Machine learning and radiomics (ML/RM) show growing promise in predicting ovarian cancer treatment response and survival. Further validation in multicenter trials is recommended before clinical integration.

Keywords:
machine learningovarian cancerradiomicstreatment prediction

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Area of Science:

  • Oncology
  • Radiology
  • Data Science

Background:

  • Machine learning and radiomics (ML/RM) are increasingly explored in ovarian cancer (OC) research.
  • Predicting treatment response using ML/RM in OC is an emerging area with limited studies.

Purpose of the Study:

  • To systematically review the literature on ML/RM applications in OC.
  • To focus on studies predicting treatment response and survival outcomes.

Main Methods:

  • Systematic review of literature from January 1985 to December 2023.
  • Two independent reviewers screened 5576 articles, including 225 for analysis.
  • Quality assessed using MINORS criteria; p-values from Pearson's Chi-squared test.

Main Results:

  • 225 studies included; 49 published between 2021-2023, indicating rapid growth.
  • Neural Networks and LASSO were common algorithms; 13 studies focused on radiomic prediction of treatment response.
  • ML/RM models showed promising AUCs for chemotherapy response (0.77) and cytoreduction (0.82), and high accuracy for survival prediction.

Conclusions:

  • ML/RM algorithms are increasingly utilized for predicting OC treatment responses.
  • These predictive models require validation in prospective, multicenter trials before clinical adoption.