Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

Abstract

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.

Magnetic resonance imaging‐based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma

“…6 To date, radiomics has been applied in various oncology researches for precise clinical decision, treatment response, and prognosis predication. [7][8][9] Moreover, radiogenomics emerges to explore the relationships between radiomics features and genomic characteristics, with the goal of noninvasively uncovering the underlying biological heterogeneity most strongly associated with clinical outcomes. 10 However, radiogenomics research regarding NPC is not available.…”

Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach

“…Particularly, more biologically significant MRI sequences such as diffusion-and perfusion-weighted MRI have been shown to outperform radiomics models based on conventional MRI [4,9]. These approaches should be taken into account in future research as they will be able to encompass more features concerning intratumor heterogeneity and have shown improved performance in relation to molecular markers and predicting prognosis [9,36]. (5) We illustrated radiomic features based on imaging signatures of the heterogeneous GBM tumor tissue parts (Figure 2) and created a radiomic-based model for the semiautomatic annotation of GBM using MRI, ground truth, and machine learning [4].…”

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

“…To date, radiomics has made impressive performances in tumour differentiation [ 43 , 44 , 45 ], prognosis evaluation [ 46 , 47 , 48 ], therapeutic effect evaluation [ 49 , 50 , 51 , 52 ], and tumour metastasis evaluation [ 53 , 54 , 55 ]. Compared with the performance of traditional predictive models based on clinical data and imaging anatomy, better performance of radiomics has been widely reported [ 24 , 56 , 57 ].…”