Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning Methods.

. Leukemic relapse remains the primary cause of treatment failure and death after allogeneic hematopoietic stem cell transplant. Changes in post-transplant donor chimerism have been identified as a predictor of relapse. A better predictive model of relapse incorporating donor chimerism has the potential to improve leukemia-free survival by allowing earlier initiation of post-transplant treatment on individual patients. We explored the use of machine learning, a suite of analytical methods focusing on pattern recognition, to improve post-transplant relapse prediction. . Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pre-transplant and post-transplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool was used to confirm our random forest classification result. . Our analysis showed that a random forest model using these hyperparameter values achieved 85% accuracy, 85% sensitivity, 89% specificity for ALL, while for AML 81% accuracy, 75% sensitivity, and 100% specificity at predicting relapses within 24 months post-HSCT in cross validation. The Local Interpretable Model-Agnostic Explanations tool was able to confirm many variables that the random forest classifier identified as important for the relapse prediction. . Machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and robust predictions can be obtained even with a modest clinical dataset. The random forest classifier distinguished different important predictive factors between ALL and AML in our relapse models, consistent with previous knowledge, lending increased confidence to adopting machine learning prediction to clinical management.