Association between histogram parameters of T2 weighted MRI data, WHO grade, tumor proliferation index and selected...

Conventional T2 weighted MRI sequences are part of the standard diagnostics in case of LGG and implemented in every MRI protocol for the first anatomical evaluation. Despite the excellent tissue contrast and spatial information, conventional radiological assessment of these images lacks the capacity of providing reliable information about underlying histopathology. Therefore, this retrospective investigation aimed to assess whole-tumor histogram analysis (HA) of T2 weighted MRI sequences for its ability to distinguish between WHO grade 1 and 2 gliomas, to predict the isocitrate dehydrogenase 1 (IDH 1) gene mutation and the methylguanine-DNA methyl-transferase (MGMT) promoter methylation status, to differentiate oligodendrogliomas from diffuse astrocytomas, and to predict the proliferative potential using the Ki-67 proliferation index.

Signal intensities of T2 weighted pre-surgical MRI data of 53 LGG patients were used for histogram-profiling. WHO-grade, Ki-67 expression, IDH 1 mutation and MGMT promoter methylation status were evaluated. Comparative and correlative statistics were used to investigate possible associations between HA parameters and neuropathology.

Statistically significant distinctions between WHO grade 1 and grade 2 gliomas were observed for T2 SI Entropy (p = 0.001). Furthermore, T2 SI Entropy was significantly higher in IDH 1-mutated gliomas (p = 0.015) and correlated significantly with the Ki-67 proliferation index (r = 0.341, p = 0.019). Noteworthy distinctions between gliomas with MGMT promoter methylation and those without were discerned for SImin (p = 0.019). No significance could be detected comparing SI histogram parameters between oligodendrogliomas and diffuse astrocytomas.

Our investigation demonstrates the potential of T2 SI Entropy in distinguishing grade 1 from grade 2 gliomas and in reflecting the proliferative activity denoted by Ki-67 expression, therefore being a promising HA feature for assessing tumor heterogeneity.