Computer-aided diagnosis of leukoencephalopathy in children treated for acute lymphoblastic leukemia

John O. Glass; Chin-Shang Li; Kathleen J. Helton; Wilburn E. Reddick

doi:10.1117/12.594650

29 April 2005 Computer-aided diagnosis of leukoencephalopathy in children treated for acute lymphoblastic leukemia

John O. Glass, Chin-Shang Li, Kathleen J. Helton, Wilburn E. Reddick

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.594650
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

The purpose of this study was to use objective quantitative MR imaging methods to develop a computer-aided diagnosis tool to differentiate white matter (WM) hyperintensities as either leukoencephalopathy (LE) or normal maturational processes in children treated for acute lymphoblastic leukemia with intravenous high dose methotrexate. A combined imaging set consisting of T1, T2, PD, and FLAIR MR images and WM, gray matter, and cerebrospinal fluid a priori maps from a spatially normalized atlas were analyzed with a neural network segmentation based on a Kohonen Self-Organizing Map. Segmented regions 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 two time points in 203 children. An unsupervised hierarchical clustering algorithm with the agglomeration method of McQuitty was used to divide data from the first examination into normal appearing or LE groups. A C-support vector machine (C-SVM) was then trained on the first examination data and used to classify the data from the second examination. The overall accuracy of the computer-aided detection tool was 83.5% (299/358) with sensitivity to normal WM of 86.9% (199/229) and specificity to LE of 77.5% (100/129) when compared to the readings of two expert observers. These results suggest that subtle therapy-induced leukoencephalopathy can be objectively and reproducibly detected in children treated for cancer using this computer-aided detection approach based on relative differences in quantitative signal intensity measures normalized within each examination.

Citation Download Citation

John O. Glass, Chin-Shang Li, Kathleen J. Helton, and Wilburn E. Reddick "Computer-aided diagnosis of leukoencephalopathy in children treated for acute lymphoblastic leukemia", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.594650

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