A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors

• Published in: Magnetic resonance imaging Vol. 16; no. 4; pp. 413 - 421
• Main Authors: Reddick, Wilburn E, Mulhern, Raymond K, Elkin, T.David, Glass, John O, Merchant, Thomas E, Langston, James W
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.05.1998; Elsevier Science
• Subjects: Adolescent; Adult; Astrocytoma - diagnosis; Astrocytoma - therapy; Biological and medical sciences; Brain - drug effects; Brain - pathology; Brain - radiation effects; Cerebellar Neoplasms - diagnosis; Cerebellar Neoplasms - therapy; Child; Child, Preschool; Combined Modality Therapy; Female; Follow-Up Studies; Humans; Image processing; Image segmentation and classification; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging; Male; Medical sciences; Medulloblastoma - diagnosis; Medulloblastoma - therapy; MRI; Nervous system; Neural networks; Neural Networks (Computer); Radiodiagnosis. Nmr imagery. Nmr spectrometry; Image processing; Image segmentation and classification; Neural networks; MRI; Human; Intracranial; Nervous system diseases; Segmentation; Neural network; Nuclear magnetic resonance imaging; Cerebral disorder; Central nervous system disease; Classification; Medical imagery; Tumor; Child
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/S0730-725X(98)00014-9

In the treatment of children with brain tumors, balancing the efficacy of treatment against commonly observed side effects is difficult because of a lack of quantitative measures of brain damage that can be correlated with the intensity of treatment. We quantitatively assessed volumes of brain parenchyma on magnetic resonance (MR) images using a hybrid combination of the Kohonen self-organizing map for segmentation and a multilayer backpropagation neural network for tissue classification. Initially, we analyzed the relationship between volumetric differences and radiologists’ grading of atrophy in 80 subjects. This investigation revealed that brain parenchyma and white matter volumes significantly decreased as atrophy increased, whereas gray matter volumes had no relationship with atrophy. Next, we compared 37 medulloblastoma patients treated with surgery, irradiation, and chemotherapy to 19 patients treated with surgery and irradiation alone. This study demonstrated that, in these patients, chemotherapy had no significant effect on brain parenchyma, white matter, or gray matter volumes. We then investigated volumetric differences due to cranial irradiation in 15 medulloblastoma patients treated with surgery and radiation therapy, and compared these with a group of 15 age-matched patients with low-grade astrocytoma treated with surgery alone. With a minimum follow-up of one year after irradiation, all radiation-treated patients demonstrated significantly reduced white matter volumes, whereas gray matter volumes were relatively unchanged compared with those of age-matched patients treated with surgery alone. These results indicate that reductions in cerebral white matter: 1) are correlated significantly with atrophy; 2) are not related to chemotherapy; and 3) are correlated significantly with irradiation. This hybrid neural network analysis of subtle brain volume differences with magnetic resonance may constitute a direct measure of treatment-induced brain damage.