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

Abstract

Digital twins in healthcare offer an innovative approach to precision diagnosis, prognosis, and treatment. SynTwin, a novel computational methodology to generate digital twins using synthetic data and network science, has previously shown promise for improving prediction of breast cancer mortality. In this study, we validate SynTwin using population-level data for different cancer types from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute (USA). We assess its predictive accuracy across cancer types of varying sample sizes (n = 1,000 to 30,000 records), mortality rates (35% to 60%), and study designs, revealing insights into the strengths and limitations of digital twins derived from synthetic data in mortality prediction. We also evaluate the effect of sample size (n = 1,000 to 70,000 records) on predictive accuracy for selected cancers (non-Hodgkin lymphoma, bladder, and colorectal cancers). Our results indicate that for larger datasets (n > 10,000) including digital twins in the nearest network neighbor prediction model significantly improves the performance compared to using real patients alone. Specifically, AUROCs ranged from 0.828 to 0.884 for cancers such as cervix uteri and ovarian cancer with digital twins, compared to 0.720 to 0.858 when using real patient data. Similarly, among the selected three cancers, AUROCs using digital twins exceeded AUROCs using real patients alone by at least 0.06 with narrowing variance in performance as the sample size increased. These results highlight the benefit of network-based digital twins, while emphasizing the importance of considering effective sample size when developing predictive models like SynTwin.