Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters

Abstract

ObjectivesTo explore whether a simplified lesion delineation method and a set of diffusion tensor imaging (DTI) metric-based histogram parameters (mean, 25th percentile, 75th percentile, skewness, and kurtosis) are efficient at predicting the molecular pathology status (MGMT methylation, IDH mutation, TERT promoter mutation, and 1p19q codeletion) of lower grade insular gliomas (grades II and III).Methods40 lower grade insular glioma patients in two medical centers underwent preoperative DTI scanning. For each patient, the entire abnormal area in their b-non (b0) image was defined as region of interest (ROI), and a set of histogram parameters were calculated for two DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD). Then, we compared how these DTI metrics varied according to molecular pathology and glioma grade, with their predictive performance individually and jointly assessed using receiver operating characteristic curves. The reliability of the combined prediction was evaluated by the calibration curve and Hosmer and Lemeshow test.ResultsThe mean, 25th percentile, and 75th percentile of FA were associated with glioma grade, while the mean, 25th percentile, 75th percentile, and skewness of both FA and MD predicted IDH mutation. The mean, 25th percentile, and 75th percentile of FA, and all MD histogram parameters significantly distinguished TERT promoter status. Similarly, all MD histogram parameters were associated with 1p19q status. However, none of the parameters analyzed for either metric successfully predicted MGMT methylation. The 25th percentile of FA yielded the highest prediction efficiency for glioma grade, IDH mutation, and TERT promoter mutation, while the 75th percentile of MD gave the best prediction of 1p19q codeletion. The combined prediction could enhance the discrimination of grading, IDH and TERT mutation, and also with a good fitness.ConclusionsOverall, more invasive gliomas showed higher FA and lower MD values. The simplified ROI delineation method presented here based on the combination of appropriate histogram parameters yielded a more practical and efficient approach to predicting molecular pathology in lower grade insular gliomas. This approach could help clinicians to determine the extent of tumor resection required and reduce complications, enabling more precise treatment of insular gliomas in combination with radiotherapy and chemotherapy.

Microstructural changes of white matter fiber tracts induced by insular glioma revealed by tract-based spatial statistics and automatic fiber quantification

“…Among the existing studies, only one has focused on insular glioma. Namely, Huang et al found that FA and MD within the tumor ROI can predict molecular biomarkers 16 ; however, a whole-brain fiber bundle-based assessment has been lacking. Thus, microstructural white matter changes revealed by DTI associated with insular glioma remains unclear.…”

Multiple diffusion metrics in differentiating solid glioma from brain inflammation

“…The VOIs of lesions were defined as abnormal hyperintense signals on the b0 image ( Figure 1 ) and cerebrospinal fluid signals were avoided. Since the b0 images were part of the DWI sequence, it was simple to align VOIs with other metric maps ( Huang et al, 2021 ). The mean value of each metric map was calculated by FAE 4 ( Song et al, 2020 ).…”

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

“…On the other hand, numerous studies have reported that gliomas have specific molecular parameters, such as IDH-mutation ( Chen et al, 2017 ; Kesler et al, 2019 ) and MGMT promoter methylation ( Chen et al, 2017 ). To date, deep neural networks have been utilized to extract dMRI metrics like ADC and FA value, and to assist IDH, MGMT, and other molecule subtype classification and prognosis prediction of glioma patients ( Aliotta et al, 2019 ; Z. Huang et al, 2021 ; Yan et al, 2021 ).…”