A neuroimaging pattern-recognition approach in pediatric intracranial calcifications
Article first published online: 1 NOV 2012
© The Authors. Developmental Medicine & Child Neurology © 2012 Mac Keith Press
Developmental Medicine & Child Neurology
Volume 55, Issue 1, pages 7–8, January 2013
How to Cite
PORETTI, A., VALK, J. and HUISMAN, T. A. (2013), A neuroimaging pattern-recognition approach in pediatric intracranial calcifications. Developmental Medicine & Child Neurology, 55: 7–8. doi: 10.1111/j.1469-8749.2012.04439.x
- Issue published online: 13 DEC 2012
- Article first published online: 1 NOV 2012
Computer assisted magnetic resonance pattern recognition in white matter disorders was introduced by Marjo van der Knaap1 and Jaap Valk, a pediatric neurologist and a pediatric neuroradiologist, and first reported in 1991. This approach not only simplified and guided the diagnosis of many classified white matter disorders, but also made it possible to cluster patients with identical magnetic resonance patterns, allowing further clinical, laboratory, genetic, and molecular exploration, in other words, translational research. Several previously unknown disease entities have been identified using this approach.
Indeed, the neuroimaging based pattern-recognition approach not only allowed the correct identification of various new white and gray matter disorders affecting the supratentorial region of the brain2 but also provided important information on various predominantly infratentorial diseases, including cerebellar atrophy,3 or prenatal cerebellar disruptions.4
In their study, Livingston et al.5 have extended the magnetic resonance based pattern-recognition approach to computed tomography (CT) by studying the distribution of intracranial calcifications. The authors systematically studied CT and magnetic resonance imaging (MRI) studies of 119 patients with intracranial calcifications and developed a comprehensive scoring system for CT and MRI scans. They evaluated symmetry, appearance and location of calcifications, and the presence of atrophy, morphological anomalies, white matter signal abnormalities, and contrast enhancement.
For Aicardi–Goutières syndrome (AGS), band-like calcification with simplified gyration and polymicrogyria (BLC-PMG), and COL4A1 mutation related disease, the authors were able to identify a characteristic calcification pattern. Intracranial calcifications are also typical in prenatal viral infections of the central nervous system (e.g. cytomegalovirus infection).6 The CT pattern-recognition approach reported by Livingston et al. suggests that inherited disorders (e.g. AGS, BLC-PMG, and COL4A1 mutations related disease) and disruptive lesions (prenatal infections) may be differentiated by the distribution of CT calcifications. Differentiation between these diseases is important for diagnosis, prognosis, and genetic counseling of the affected children and their families.
In 60 patients in the study with intracranial calcifications, the etiology was unknown.5 Using a detailed, systematic neuroimaging evaluation approach, the authors were able to classify some of them into subgroups based on the same neuroimaging phenotype. This way, they could suggest a new disorder characterized by the association of polymicrogyria and intracranial calcifications. The identification of additional patients sharing the same CT/MRI phenotype will potentially allow finding the genotype and the underlying pathomechanism of this new disease. To detect additional patients, it is mandatory that physicians ‘speak the same language’ by using the same classification. We are convinced that the classification system for intracranial calcifications proposed by Livingston et al. facilitates a more comprehensive evaluation of intracranial calcifications and is easy to apply in daily routine. Additionally, the authors proved the usefulness of this classification both for physicians who diagnose and treat patients with intracranial calcifications (pattern-recognition approach leading to the diagnosis of a known disorder) and clinical scientists who aim to define new disorders by clarifying unsolved cases (subgrouping of patients with the same neuroimaging phenotype). Finally, this classification system seems to be flexible enough to be easily modified or updated if knowledge progresses. Therefore, we suggest that the proposed CT pattern-recognition approach should be used in the future for clinical and research purposes.
Although, because of population bias, some diseases with intracranial calcifications such as Cockayne syndrome7 and neurofibromatosis type 28 are not included in this study they can easily be added. The article by Livingston et al. will be helpful for pediatric neurologists and pediatric neuroradiologists in terms of diagnosis, classification, and identification of well-defined and unclassified disorders with intracranial calcifications.