Glioma surveillance imaging: current strategies, shortcomings, challenges and outlook

The use of sodium MRI in the diagnosis of an anaplastic astrocytoma during immunotherapy: a case report

Gliomas in the pediatric population are targeted with immune-modulating therapies. The gold standard imaging modality for diagnosis and monitoring treatment response is magnetic resonance imaging (MRI); however, the complex post-therapy-induced changes can make treatment response assessment difficult. These include radiation necrosis, pseudoresponse, and pseudoprogression, as well as more complex responses in the setting of immunotherapy. We report a case of an 11-year-old male with a supratentorial astrocytoma (WHO grade 3) that underwent treatment with immunotherapy. There was a clinical concern for progression due to increased fluid-attenuated inversion recovery (FLAIR) hyperintensity at the site of the primary neoplasm during immunotherapy. However, the Sodium (23Na) MRI continued demonstrating decreased total sodium concentrations, supporting pseudoprogression over true progression, which was confirmed clinicaly. This case reports the capability of 23Na MRI to differentiate between progression, recurrence, and other posttreatment changes.

Post-chemo-radiotherapy response and pseudo-progression evaluation on glioma cell types by multi-parametric magnetic resonance imaging: a prospective study

BACKGROUND

We focused on Differentiated pseudoprogression (PPN) of progression (PN) and the response to radiotherapy (RT) or chemoradiotherapy (CRT) using diffusion and metabolic imaging.

METHODS

Seventy-five patients with glioma were included in this prospective study (approved by the Iranian Registry of Clinical Trials (IRCT) (IRCT20230904059352N1) in September 2023). Contrast-enhanced lesion volume (CELV), non-enhanced lesion volume (NELV), necrotic tumor volume (NTV), and quantitative values ​​of apparent diffusion coefficient (ADC) and magnetic resonance spectroscopy (Cho/Cr, Cho/NAA and NAA/Cr) were calculated by a neuroradiologist using a semi-automatic method. All patients were followed at one and six months after CRT.

RESULTS

The results of the study showed statistically significant changes before and six months after RT-CRT for M-CELV in all glioma types (𝑝 < 0.05). In glioma cell types, the changes in M-ADC, M-Cho/Cr, and Cho/NAA indices for PN were incremental and greater for PPN patients. M-NAA/Cr ratio decreased after six months which was significant only on PN for GBM, and Epn (𝑝 < 0.05). A significant difference was observed between diffusion indices, metabolic ratios, and CELV changes after six months in all types (𝑝 < 0.05). None of the patients were suspected PPN one month after treatment. The DWI/ADC indices had higher sensitivity and specificity (98.25% and 96.57%, respectively).

CONCLUSION

The results of the present study showed that ADC values and Cho/Cr and Cho/NAA ratios can be used to differentiate between patients with PPN and PN, although ADC is more sensitive and specific.

Concept of a fully-implantable system to monitor tumor recurrence

Current treatment for glioblastoma includes tumor resection followed by radiation, chemotherapy, and periodic post-operative examinations. Despite combination therapies, patients face a poor prognosis and eventual recurrence, which often occurs at the resection site. With standard MRI imaging surveillance, histologic changes may be overlooked or misinterpreted, leading to erroneous conclusions about the course of adjuvant therapy and subsequent interventions. To address these challenges, we propose an implantable system for accurate continuous recurrence monitoring that employs optical sensing of fluorescently labeled cancer cells and is implanted in the resection cavity during the final stage of tumor resection. We demonstrate the feasibility of the sensing principle using miniaturized system components, optical tissue phantoms, and porcine brain tissue in a series of experimental trials. Subsequently, the system electronics are extended to include circuitry for wireless energy transfer and power management and verified through electromagnetic field, circuit simulations and test of an evaluation board. Finally, a holistic conceptual system design is presented and visualized. This novel approach to monitor glioblastoma patients is intended to early detect recurrent cancerous tissue and enable personalization and optimization of therapy thus potentially improving overall prognosis.

A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation

Objective.To develop a deep ensemble learning (DEL) model with radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric magnetic resonance imaging (mp-MRI).Approach.This model was developed using 369 glioma patients with a four-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implemented across the brain to capture image heterogeneity: 56 radiomic features were extracted within the kernel, resulting in a fourth-order tensor. Each radiomic feature can then be encoded as a 3D image volume, namely a radiomic feature map (RFM). For each patient, all RFMs extracted from all four modalities were processed using principal component analysis for dimension reduction, and the first four principal components (PCs) were selected. Next, a DEL model comprised of four U-Net sub-models was trained for the segmentation of a region-of-interest: each sub-model utilizes the mp-MRI and one of the four PCs as a five-channel input for 2D execution. Last, four softmax probability results given by the DEL model were superimposed and binarized using Otsu's method as the segmentation results. Three DEL models were trained to segment the enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The segmentation results given by the proposed ensemble were compared to the mp-MRI-only U-Net results.Main Results.All three radiomics-incorporated DEL models were successfully implemented: compared to the mp-MRI-only U-net results, the dice coefficients of ET (0.777 → 0.817), TC (0.742 → 0.757), and WT (0.823 → 0.854) demonstrated improvement. The accuracy, sensitivity, and specificity results demonstrated similar patterns.Significance.The adopted radiomics spatial encoding execution enriches the image heterogeneity information that leads to the successful demonstration of the proposed DEL model, which offers a new tool for mp-MRI-based medical image segmentation.

Noninvasive approaches to detect methylation-based markers to monitor gliomas

In this review, we summarize the current approaches used to detect glioma tissue-derived DNA methylation markers in liquid biopsy specimens with the aim to diagnose, prognosticate and potentially track treatment response and evolution of patients with gliomas.

[Magnetic resonance relaxometry in high-grade glioma subregion assessment - neuroimaging and morphological correlates]

OBJECTIVE

To analyze the differences of high-grade glioma subregions using magnetic resonance relaxometry with compilation of images (MAGiC) and arterial spin labeling (ASL), as well as to compare quantitative measurements of these techniques with morphological data.

MATERIAL AND METHODS

The study enrolled 35 patients with newly diagnosed supratentorial gliomas (23 - grade IV, 12 - grade III). We measured relaxometric values (T1, T2, proton density), tumor blood flow (TBF) in glioma subregions and normal-appearing brain matter. Neuronavigation was intraoperatively used to obtain tissue samples from active tumor growth zone, perifocal infiltrative edema zone and adjacent brain matter along surgical approach.

RESULTS

ASL perfusion revealed higher tumor blood flow (TBF) in active tumor growth region compared to perifocal infiltrative edema zone (p<0.01). Relaxometric values (T1, T2, proton density) in perifocal zone were higher (p<0.01) compared to adjacent intact white matter along surgical approach. However, there were no differences in TBF between these zones. Proton density in tumor-adjacent intact white matter was higher (p<0.01) compared to normal-appearing white matter in ipsilateral hemisphere. There was inverse correlation between T2 and TBF in active tumor growth zone (Spearman rank R= -0.58; p=0.0016). We found inverse correlation between T2 and Ki67 proliferative index and direct correlation between TBF and Ki67 in this zone. Nevertheless, these relationships were insignificant after multiple test adjustment.

CONCLUSION

Our study advocates for complementary power of ASL perfusion and MR relaxometry in assessment of high-grade brain glioma subregions. More malignant tumor zones tend to have higher TBF and shorter T2. Further investigation is needed to prove the capability of MAGiC to reveal foci of increased relaxometric values in tumor-adjacent normal-appearing white matter.

Dynamic Susceptibility Perfusion Imaging for Differentiating Progressive Disease from Pseudoprogression in Diffuse Glioma Molecular Subtypes

Rationale and Objectives: Advanced adjuvant therapy of diffuse gliomas can result in equivocal findings in follow-up imaging. We aimed to assess the additional value of dynamic susceptibility perfusion imaging in the differentiation of progressive disease (PD) from pseudoprogression (PsP) in different molecular glioma subtypes. Materials and Methods: 89 patients with treated diffuse glioma with different molecular subtypes (IDH wild type (Astro-IDH^wt), IDH mutant astrocytomas (Astro-IDH^mut) and oligodendrogliomas), and tumor-suspect lesions on post-treatment follow-up imaging were classified into two outcome groups (PD or PsP) retrospectively by histopathology or clinical follow-up. The relative cerebral blood volume (rCBV) was assessed in the tumor-suspect FLAIR and contrast-enhancing (CE) lesions. We analyzed how a multilevel classification using a molecular subtype, the presence of a CE lesion, and two rCBV histogram parameters performed for PD prediction compared with a decision tree model (DTM) using additional rCBV parameters. Results: The PD rate was 69% in the whole cohort, 86% in Astro-IDH^wt, 52% in Astro-IDH^mut, and 55% in oligodendrogliomas. In the presence of a CE lesion, the PD rate was higher with 82%, 94%, 59%, and 88%, respectively; if there was no CE lesion, however, the PD rate was only 44%, 60%, 40%, and 33%, respectively. The additional use of the rCBV parameters in the DTM yielded a prediction accuracy for PD of 99%, 100%, 93%, and 95%, respectively. Conclusion: Utilizing combined information about the molecular tumor type, the presence or absence of CE lesions and rCBV parameters increases PD prediction accuracy in diffuse glioma.