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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: Jul 12, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Open science practices need substantial improvement in prognostic model studies in oncology using machine learning.

Gary S Collins1, Rebecca Whittle1, Garrett S Bullock2

  • 1Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.

Journal of Clinical Epidemiology
|October 28, 2023
PubMed
Summary

Open science practices are rarely used in oncology studies developing machine learning prognostic models. Increased awareness and guidance are needed to improve data and code sharing for better prediction research.

Keywords:
Code sharingData sharingMachine learningOpen sciencePrognosisReporting

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Area of Science:

  • Oncology
  • Machine Learning
  • Prognostic Modeling
  • Open Science

Background:

  • Machine learning (ML) is increasingly used to develop prognostic models in oncology.
  • Open science practices are crucial for transparency, reproducibility, and collaboration in research.

Conclusions:

  • Open science adoption in oncology ML prognostic modeling is poor.
  • There is a need for enhanced guidance and awareness of open science benefits and best practices.
  • Improving open science can advance prediction research in oncology.