The purpose of this study was to use objective quantitative magnetic resonance imaging (MRI) methods to develop a computer-aided detection (CAD) tool to differentiate white matter (WM) hyperintensities into either leukoencephalopathy (LE) induced by chemotherapy or normal maturational processes in children treated for acute lymphoblastic leukemia without irradiation. A combined MRI set consisting of T1-weighted, T2-weighted, proton-density-weighted and fluid-attenuated inversion recovery images and WM, gray matter and cerebrospinal fluid proportional volume maps from a spatially normalized atlas were analyzed with a neural network segmentation based on a Kohonen self-organizing map (SOM). Segmented maps were manually classified to identify the most hyperintense WM region and the normal-appearing genu region. Signal intensity differences normalized to the genu within each examination were generated for four time points in 228 children. A second Kohonen SOM was trained on the first examination data and divided the WM into normal-appearing or LE groups. Reviewing labels from the CAD tool revealed a consistency measure of 89.8% (167 of 186) within patients. The overall agreement between the CAD tool and the consensus reading of two trained observers was 84.1% (535 of 636), with 84.2% (170 of 202) agreement in the training set and 84.1% (365 of 434) agreement in the testing set. These results suggest that subtle therapy-induced LE can be objectively and reproducibly detected in children treated for cancer using this CAD approach based on relative differences in quantitative signal intensity measures normalized within each examination.