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Ravi Parikh: TrialTranslator – a machine learning framework for individualized cancer trial emulation

Ravi Parikh/chibe.upenn.edu

Jan 6, 2025, 10:03

Ravi Parikh: TrialTranslator – a machine learning framework for individualized cancer trial emulation

Ravi Parikh, Director of the Human-Algorithm Collaboration Lab (HACLab) at Emory University and Winship Cancer Institute, shared a post on LinkedIn about a recent paper by him and colleagues published in Nature Medicine:

“One of the hardest things I do in oncology clinic is decide whether results from a ‘game-changer’ clinical trial can be translated to a patient sitting in front of me.

In Nature Portfolio Nature Medicine, we introduce TrialTranslator, a machine learning framework for individualized cancer trial emulation built from tens of thousands of real-world patients to ascertain likelihood of benefit from a novel agent. Led by Xavier Orcutt, with Kan Chen, Ronac Mamtani, and co-senior Qi Long.

TLDR: Through emulating 11 clinical trials in thousands of real-world patients, we show that survival results from randomized clinical trials generalize poorly to real-world patients, but in predictable ways.

We develop a machine learning framework and corresponding web-interface to facilitate individualized estimation of treatment benefit that can be applied across other oncology clinical trials. Web interface.

Press release here!”

“Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations”

Authors: Xavier Orcutt, Kan Chen, Ronac Mamtani, Qi Long and Ravi Parikh.

Ravi Parikh: TrialTranslator - a machine learning framework for individualized cancer trial emulation

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Ravi Parikh: TrialTranslator – a machine learning framework for individualized cancer trial emulation