Machine learning optimized multiparameter radar plots for B‐cell acute lymphoblastic leukemia minimal residual disease analysis. Issue 5 (21st June 2022) - Shopsowitz, Kevin E.; Liu, Lorraine; Setiadi, Audi; Al‐Bakri, Maryam; Vercauteren, Suzanne
Abstract: Background: Flow cytometry is widely used for B‐ALL minimal residual disease (MRD) analysis given its speed, availability, and sensitivity; however, distinguishing B‐lymphoblasts from regenerative B‐cells is not always straightforward. Radar plots, which project multiple markers onto a single plot, have been applied to other MRD analyses. Here we aimed to develop optimized radar plots for B‐ALL MRD analysis. Methods: We compiled Children's Oncology Group (COG) flow data from 20 MRD‐positive and 9 MRD‐negative B‐ALL cases (enriched for hematogones) to create labeled training and test data sets with equal numbers of B‐lymphoblasts, hematogones, and mature B‐cells. We used an automated approach to create hundreds of radar plots and ranked them based on the ability of support vector machine (SVM) models to separate blasts from normal B‐cells in the training data set. Top‐performing radar plots were compared with PCA, t‐SNE, and UMAP plots, evaluated with the test data set, and integrated into clinical workflows. Results: SVM area under the ROC curve (AUC) for COG tube 1/2 radar plots improved from 0.949/0.921 to 0.989/0.968 after optimization. Performance was superior to PCA plots and comparable to UMAP, but with better generalizability to new data. When integrated into an MRD workflow, optimized radar plots distinguished B‐lymphoblasts from other CD19‐positive populations. MRD quantified by radar plots and serial gating were strongly correlated. Discussion: Radar plots were successfully optimized to discriminate between diverse B‐lymphoblast populations and non‐malignant CD19‐positive populations in B‐ALL MRD analysis. Our novel radar plot optimization strategy could be adapted to other MRD panels and clinical scenarios.