Synthetic data reliably reproduces brain tumor primary research data

Abstract Purpose Synthetic data has garnered heightened attention in contemporary research due to confidentiality barriers and its capacity to simulate variables challenging to obtain, notably in cases where premature death prevents adequate follow-up. Indeed, a significant challenge in clinical neuro-oncology research is the limited availability of data pertinent to rapid-onset conditions with relatively poor prognoses. This study aimed to evaluate the reliability and validity of synthetic data in the context of neuro-oncology research, comparing findings from two published studies with results from synthetic datasets. Materials and Methods Two published neuro-oncology studies focusing on prognostic factors were selected, and their methodologies were replicated using MDClone Platform to generate five synthetic datasets for each. These datasets were assessed for inter-variability and compared against the original study results. Results Findings from synthetic data consistently matched outcomes from both original articles. Reported findings, demographic trends and survival outcomes showed significant similarity (P < 0.05) with synthetic datasets. Moreover, synthetic data produced consistent results across multiple datasets. Conclusion Integrating synthetic data into clinical research offers excellent potential for providing accurate predictive insights without compromising patient privacy. In neuro-oncology, where data fragmentation and patient follow-up pose significant challenges, the adoption of synthetic datasets can be transformative.