Supplementary Figure S1 from Detection of Early-Stage Colorectal Cancer Using Cell-Free oncRNA Biomarkers and Artificial Intelligence

Supplementary Figure S1) To assess the robustness and reproducibility of the CRC prediction models, we evaluated their performance on samples processed under varying conditions. This figure shows the performance of the final CRC model, after retraining on the full training dataset, across the robustness and reproducibility studies. Specifically, we analyzed the precision of model scores using a set of precision study samples processed by two different operators, instruments, and, when available, reagent lots. Two separate precision studies were performed: One that started from separate plasma aliquots and tested variability derived from extraction. For this study, once RNA was extracted, it was included in the same batch for processing through the rest of the assay (left panel). Across the replicates, the scores remained consistent, with no instances where changes in scores were significant enough to cross the cutoff threshold into the opposite category. The second study used pools of RNA that were diluted 2-fold to generate more input material and then distributed in replicates across different batches starting from library preparation and through the rest of the assay (middle panel). These experiments ensured that normal laboratory variability did not significantly impact the model’s predictive outputs. Finally, we compared the scores of 40 samples where 2 separate plasma aliquots were processed at different times through the assay ( Right panel). The high correlation of model scores across these conditions demonstrates the model’s robustness to technical variability and underscores its reliability for real-world applications