Noninvasive prediction of microsatellite instability in stage II/III rectal cancer using dynamic contrast-enhanced magnetic resonance imaging radiomics

Figure 1 Machine learning models performance for microsatellite instability status prediction. The models achieved 85.2% accuracy in training and 84.7% in validation (P < 0.001 for both), demonstrating good generalizability. This suggests radiomics-based machine learning approaches could serve as a valuable non-invasive tool for microsatellite instability assessment in clinical practice. ROC: Receiver operating characteristic; AUC: Area under the curve.