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Marco Donia: Evaluating generalizability of oncology trials with machine learning
Mar 16, 2025, 14:53

Marco Donia: Evaluating generalizability of oncology trials with machine learning

Marco Donia, Professor at the University of Copenhagen and Research Group Leader (TIL group) at CCIT – Center for Cancer Immune Therapy, shared a post on LinkedIn:

“Evaluating Generalizability of Oncology Trials with Machine Learning.

Nature Medicine, Orcutt et al., Jan 2025

Online tool.

Randomized Controlled Trials (RCTs) vs. Real-World Patients: Oncology trials often fail to represent real-world patients due to restrictive eligibility criteria (melanoma; not just in oncology)

Key Insights:

Trial Translator: A Machine Learning(ML)-Based Framework: Uses electronic health record data to emulate RCTs across prognostic risk groups.

Categorizes patient phenotypes in:

  • Low and Medium risk: survival times similar to those observed in RCTs
  • High risk: significantly lower survival times and treatment-associated benefits.

Take-Home Message:

  • Highlights the gap in RCT representativeness of real-world patients
  • ML-based risk stratification may inform on treatment expectations.

Clinical Implications:

This tool could enable more realistic expectations for treatment outcomes in diverse patient populations, and help planning individualized care

Initiatives to Broaden Eligibility Criteria of Clinical Trials to increase representativeness: learn more.

Outstanding work from the team of Ravi B. Parikh and Qi Long.”

Marco Donia: Evaluating generalizability of oncology trials with machine learning

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