Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging

Abstract

Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2‐FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics‐based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1‐contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first‐order and gray‐level co‐occurrence matrix features are crucial in detecting local recurrence, while multimodal gray‐level difference matrix and first‐order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics‐based phenotypic changes that may potentially serve as MR‐based biomarkers for customized therapeutic intervention.

Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI

“…The authors reported an overall accuracy in the validation group (n = 20) of 0.78. In the study by Chougule et al [ 18 ] (2021), an accuracy of 0.71 was reported to predict local recurrence in the test group (n = 6). The authors trained a predictive recurrence model using voxel-based radiomic features of the T1ce, FLAIR, and ADC maps.…”

“…Previous studies have attempted to characterize tumor infiltration into the noncontrast-enhancing region through MRI and in combination with stereotactic biopsies [ 10 ] or by applying machine learning and deep learning models [ 11 , 12 , 13 , 14 ]. Moreover, several authors have proposed methods to predict regions of future tumor recurrence using MRI-based radiomic features [ 15 , 16 , 17 , 18 , 19 ]. Several of these studies show promising results, but they often require a great variability of image preprocessing, ground truth definitions, feature extraction, and data handling, as well as a need for advanced MRI sequences, which often hinder the generalization and applicability in a clinical setting.…”

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

“…Multiparametric pattern analysis from clinical MRI sequences using radiomic signatures determined via machine learning methods has allowed the estimation of tumoral infiltration in NEPAs and the prediction of the rate and locations of tumor recurrence in HGG patients. The recurrent tumor region showed higher vascularity and cellularity as a radiomic signature [120][121][122][123][124]. Moreover, perfusion parameters for HGGs derived from DSC imaging have been used to determine four types of tumoral and peritumoral vascular heterogeneity based on CBV and CBF values: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema and vasogenic edema.…”

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

“…Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults (Chougule et al., 2022 ; Gonçalves et al., 2020 ), with high recurrence and mortality rates despite standard therapies (Campos et al., 2016 ; Parvaze et al., 2023 ). Molecular classification of GBM has been proposed to identify subtypes with distinct clinical, genetic, and epigenetic features for risk stratification (Gritsch et al., 2022 ; Yang et al., 2022 ).…”