Neuromorphometry of primary brain tumors by magnetic resonance imaging

Abstract

Abstract. Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of 973 AE 14, whereas oligodendrogliomas exhibit a mean of 942 AE 21. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of 919 AE 43, and the necrotic region presented a mean of 869 AE 66. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor.

A Simple 3D Cell Culture Method for Studying the Interactions between Human Mesenchymal Stromal/Stem Cells and Patients Derived Glioblastoma

“…Several studies have suggested that morphological measures may help to classify and characterize brain tumors [17,18]. To quantify the morphology of the spheroids in the 3D cultures, we determined the circularity of the spheroids formed in the biospheres.…”

Discrete tortuosity as a morphometric measure in brain tumors

“…[12] presentan un estudio de morfometría 3D para poder diferenciar entre glioblastomas multiformes y metástasis a partir de imágenes por tensores de difusión. Nuestro grupo de investigación ha realizado un análisis morfométrico 3D en cuanto a la compacidad discreta (C d ) como descriptor de forma entre gliomas de diversos grados de malignidad, teniendo como resultados una primera discriminación entre ellos significativa [13] .…”

Assessing variability in brain tumor segmentation to improve volumetric accuracy and characterization of change

“…The development of computational methods to objectively extract insights out of imaging data is an ongoing challenge [3–4]. For instance, current tumor guidelines to assess tumor progression in the clinical setting are often limited to one- or two- dimensional orthogonal measurements; standards like the Revised Assessment in Neuro-Oncology (RANO) [5] attempt to quantify lesions by assigning different evaluation options depending on these linear measurements (longest diameter measures) to ascertain significant change.…”