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In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling.

Xi Li¹, Jui-Hsuan Chang¹, Mythreye Venkatesan¹

¹Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90069, USA.

|April 15, 2025

Summary

Digital twins generated by SynTwin improve cancer mortality prediction, especially with larger datasets. This synthetic data approach enhances accuracy when combined with network models for real patient data.

Keywords:

Digital twins Effective sample size Precision medicine Reproducibility Synthetic data

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Area of Science:

Computational biology and bioinformatics
Oncology and cancer research
Health informatics and data science

Background:

Digital twins offer a novel approach for precision diagnosis, prognosis, and treatment in healthcare.
SynTwin, a computational method using synthetic data and network science, has shown potential in predicting breast cancer mortality.

Purpose of the Study:

To validate the SynTwin methodology for generating digital twins using population-level cancer data.
To assess the predictive accuracy of SynTwin for cancer mortality across diverse cancer types and sample sizes.
To evaluate the impact of sample size on the predictive performance of digital twin models.

Main Methods:

Utilized population-level cancer data from the National Cancer Institute's SEER program.
Generated digital twins using the SynTwin computational methodology, integrating synthetic data and network science.
Assessed predictive accuracy using nearest network neighbor models, comparing performance with and without digital twins.

Main Results:

For datasets larger than 10,000 records, incorporating digital twins significantly improved prediction model performance.
Area Under the Receiver Operating Characteristic (AUROC) curves for cancers like cervix uteri and ovarian cancer reached 0.828–0.884 with digital twins, compared to 0.720–0.858 using real patient data alone.
Digital twins consistently enhanced AUROCs by at least 0.06 for selected cancers, with reduced performance variance as sample size increased.

Conclusions:

Network-based digital twins, generated via SynTwin, demonstrate significant benefits for improving cancer mortality prediction accuracy.
The effectiveness of digital twins is particularly pronounced in larger datasets, enhancing predictive performance beyond models using real patient data alone.
Effective sample size is a critical consideration for developing robust and accurate predictive models utilizing digital twins.