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Acute lymphoblastic leukemia is the most frequent pediatric cancer.Approximately two third of survivors develop one or more health complicationsknown as late adverse effects following their treatments. The existing measuresoffered to patients during their follow-up visits to the hospital are ratherstandardized for all childhood cancer survivors and not necessarilypersonalized for childhood ALL survivors. As a result, late adverse effects maybe underdiagnosed and, in most cases, only taken care of following theirappearance. Thus, it is necessary to predict these treatment-related conditionsearlier in order to prevent them and enhance the survivors' health. Multiplestudies have investigated the development of late adverse effects predictiontools to offer better personalized follow-up methods. However, no solutionintegrated the usage of neural networks to date. In this work, we developedgraph-based parameters-efficient neural networks and promoted theirinterpretability with multiple post-hoc analyses. We first proposed a newdisease-specific VO$_2$ peak prediction model that does not require patients toparticipate to a physical function test (e.g., 6-minute walk test) and furthercreated an obesity prediction model using clinical variables that are availablefrom the end of childhood ALL treatment as well as genomic variables. Oursolutions were able to achieve better performance than linear and tree-basedmodels on small cohorts of patients ($\leq$ 223) for both tasks.

Machine learning strategies to predict late adverse effects in childhood acute lymphoblastic leukemia survivors