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Related Experiment Video

Updated: Jul 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical

Evangelos K Oikonomou¹, Phyllis M Thangaraj¹, Deepak L Bhatt²

¹Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

NPJ Digital Medicine

|November 24, 2023

Summary

Machine learning optimizes clinical trial enrollment by predicting patient benefit. This adaptive strategy reduces trial size while maintaining treatment effect accuracy.

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Last Updated: Jul 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Area of Science:

Biostatistics
Computational Biology
Clinical Trial Design

Background:

Randomized clinical trials (RCTs) are crucial for evidence-based medicine but are costly and time-consuming.
Optimizing patient enrollment in RCTs is essential for efficiency and timely results.

Purpose of the Study:

To propose and evaluate a machine learning (ML) strategy for adaptive predictive enrichment in RCTs.
To enhance RCT enrollment efficiency using computational trial phenomaps.

Main Methods:

Simulated group sequential analyses of two cardiovascular outcomes RCTs (IRIS and SPRINT).
Constructed dynamic phenotypic representations to infer response profiles during interim analyses.
Conditioned prospective candidate enrollment probability on predicted individual benefit.

Main Results:

The ML strategy identified dynamic phenotypic signatures predictive of individualized cardiovascular benefit across interim analyses.
Simulations showed potential reductions in final trial size: 14.8% for IRIS and 17.6% for SPRINT.
The original average treatment effects were preserved in simulations for both trials.

Conclusions:

An adaptive ML framework using computational phenomaps can significantly improve RCT enrollment efficiency.
This approach has the potential to reduce trial size and resource requirements while maintaining scientific integrity.
Predictive enrichment strategies offer a promising avenue for optimizing future clinical trial designs.