Use of Preoperative Apparent Diffusion Coefficients to Predict Brain Tumor Grade

Sample strategies for the assessment of the apparent diffusion coefficient in single large intracranial space-occupying lesions of dogs and cats

Diffusion-weighted imaging is increasingly available for brain investigation. Image interpretation of intracranial space-occupying lesions often includes the derived apparent diffusion coefficient (ADC) analysis. In human medicine, ADC can help discriminate between benign and malignant lesions in intracranial tumors. This study investigates the difference in ADC values depending on the sample strategies of image analysis. MRI examination, including diffusion-weighted images of canine and feline patients presented between 2015 and 2020, were reviewed retrospectively. Patients with single, large intracranial space-occupying lesions were included. Lesions homogeneity was subjectively scored. ADC values were calculated using six different methods of sampling (M1-M6) on the ADC map. M1 included as much as possible of the lesion on a maximum of five consecutive slices; M2 included five central and five peripheral ROIs; M3 included a single ROI on the solid part of the lesion; M4 included three central ROIs on one slice; M5 included three central ROIs on different slices; and M6 included one large ROI on the entire lesion. A total of 201 animals of various breeds, genders, and ages were analyzed. ADC values differed significantly between M5 against M2 (peripheral) (p < 0.001), M5 against M6 (p = 0.009), and M4 against M2 (peripheral) (p = 0.005). When lesions scored as homogeneous in all sequences were excluded, an additional significant difference in three further sampling methods was present (p < 0.005). ADC of single, large, intracranial space-occupying lesions differed significantly in half of the tested methods of sampling. Excluding homogeneous lesions, additional significant differences among the sampling methods were present. The obtained results should increase awareness of the variability of the ADC, depending on the sample strategies used.

Deep learning characterization of brain tumours with diffusion weighted imaging

Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-invasion (PI) model has been used extensively in the literature to model the growth of these tumours, though it relies on known values of two key parameters: the tumour cell diffusivity and proliferation rate. Unfortunately, these parameters are difficult to estimate in a patient-specific manner, making personalized tumour forecasting challenging. In this paper, we develop and apply a deep learning model capable of making accurate estimates of these key GBM-characterizing parameters while simultaneously producing a full prediction of the tumour progression curve. Our method uses two sets of multi sequence MRI in order to produce estimations and relies on a preprocessing pipeline which includes brain tumour segmentation and conversion to tumour cellularity. We first apply our deep learning model to synthetic tumours to showcase the model's capabilities and identify situations where prediction errors are likely to occur. We then apply our model to a clinical dataset consisting of five patients diagnosed with GBM. For all patients, we derive evidence-based estimates for each of the PI model parameters and predictions for the future progression of the tumour, along with estimates of the parameter uncertainties. Our work provides a new, easily generalizable method for the estimation of patient-specific tumour parameters, which can be built upon to aid physicians in designing personalized treatments.

Integrated MRI–Immune–Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma

Simple Summary

Despite crucial scientific advances, Glioblastoma (GB) remains a fatal disease with limited therapeutic options and a lack of suitable biomarkers. The unveiled competence of the brain immune system together with the breakthrough advent of immunotherapy has shifted the present translational research on GB towards an immune-focused perspective. Several clinical trials targeting the immunosuppressive GB background are ongoing. So far, results are inconclusive, underpinning our partial understanding of the complex cancer-immune interplay in brain tumors. High throughput Magnetic Resonance (MR) imaging has shown the potential to decipher GB heterogeneity, including pathologic and genomic clues. However, whether distinct GB immune contextures can be deciphered at an imaging scale is still elusive, leaving unattained the non-invasive achievement of prognostic and predictive biomarkers. Along these lines, we integrated genetic, immunopathologic and imaging features in a series of GB patients. Our results suggest that multiparametric approaches might offer new efficient risk stratification models, opening the possibility to intercept the critical events implicated in the dismal prognosis of GB.

Abstract

Background: The aim of the present study was to dissect the clinical outcome of GB patients through the integration of molecular, immunophenotypic and MR imaging features. Methods: We enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI sequences were used to perform a manual evaluation of multiple quantitative variables, among which we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS (median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged as the most significant prognostic factor (median OS = 23 months in V-CD4^high vs. 13 months in V-CD4^low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMT^meth GB, while p53^mut appeared to condition a desert immune background. When individual genetic (MGMT^unmeth), MR imaging (mean ADC^low) and TIME (V-CD4+TILs^low) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI 7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion: Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models in GB patients.

Diffusion and perfusion imaging biomarkers of H3 K27M mutation status in diffuse midline gliomas

PURPOSE

H3K27M-mutant diffuse midline gliomas (M-DMGs) exhibit a clinically aggressive course. We studied diffusion-weighted imaging (DWI) and perfusion (PWI) MRI features of DMG with the hypothesis that DWI-PWI metrics can serve as biomarkers for the prediction of the H3K27M mutation status in DMGs.

METHODS

A retrospective review of the institutional database (imaging and histopathology) of patients with DMG (July 2016 to July 2020) was performed. Tumoral apparent diffusion coefficient (ADC) and peritumoral ADC (PT ADC) values and their normalized values (nADC and nPT ADC) were computed. Perfusion data were analyzed with manual arterial input function (AIF) and leakage correction (LC) Boxerman-Weiskoff models. Normalized maximum relative CBV (rCBV) was evaluated. Intergroup analysis of the imaging variables was done between M-DMGs and wild-type (WT-DMGs) groups.

RESULTS

Ninety-four cases (M-DMGs-n = 48 (51%) and WT-DMGs-n = 46(49%)) were included. Significantly lower PT ADC (mutant-1.1 ± 0.33, WT-1.23 ± 0.34; P = 0.033) and nPT ADC (mutant-1.64 ± 0.48, WT-1.83 ± 0.54; P = 0.040) were noted in the M-DMGs. The rCBV (mutant-25.17 ± 27.76, WT-13.73 ± 14.83; P = 0.018) and nrCBV (mutant-3.44 ± 2.16, WT-2.39 ± 1.25; P = 0.049) were significantly higher in the M-DMGs group. Among thalamic DMGs, the min ADC, PT ADC, and nADC and nPT ADC were lower in M-DMGs while nrCBV (corrected and uncorrected) was significantly higher. Receiver operator characteristic curve analysis demonstrated that PT ADC (cut-off-1.245), nPT ADC (cut-off-1.853), and nrCBV (cut-off-1.83) were significant independent predictors of H3K27M mutational status in DMGs.

CONCLUSION

DWI and PWI features hold value in preoperative prediction of H3K27M-mutation status in DMGs.

Radiomic Features Associated with Extent of Resection in Glioma Surgery

Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.

The predictive value of preoperative apparent diffusion coefficient (ADC) for facial nerve outcomes after vestibular schwannoma resection: clinical study

OBJECT

Diffusion MRI has been used to predict intraoperative consistency of tumors. Apparent diffusion coefficient (ADC) has shown predictive value as an imaging biomarker in many CNS tumors but has not been studied in a large cohort of patients with vestibular schwannoma. In this study, we examine the utility of ADC as a predictive biomarker for intraoperative tumor characteristics and postoperative facial nerve outcome.

METHODS

A retrospective review of patients who underwent vestibular schwannoma resection at our institution from 2008 to 2018 yielded 87 patients, of which 72 met inclusion criteria. Operative reports and clinical records were reviewed for clinical data; MRI data were interpreted in a blinded fashion for qualitative and quantitative biomarkers, including tumor ADC.

RESULTS

Mean tumor ADC values did not predict intraoperative consistency or adherence (p = 0.63). Adherent tumors were associated with worse facial nerve outcomes (p = 0.003). Regression tree analysis identified 3 ADC categories with statistically different facial nerve outcomes. The categories identified were ADC < 1006.04 × 10^-6 mm^2/s; ADC 1006.04-1563.93 × 10^-6 mm^2/s and ADC ≥ 1563.94 × 10^-6 mm^2/s. Postoperative and final House-Brackmann (HB) scores were significantly higher in the intermediate ADC group (2.3, p = 0.0038). HB outcomes were similar between the group with ADC < 1006.04 × 10^-6 mm^2/s and ≥ 1563.94 × 10^-6 mm^2/s (1.3 vs 1.3).

CONCLUSIONS

Middle-range preoperative ADC in vestibular schwannoma suggests a less favorable postoperative HB score. Preoperative measurement of ADC in vestibular schwannoma may provide additional information regarding prognostication of facial nerve outcomes.

Detection of Glioblastoma Subclinical Recurrence Using Serial Diffusion Tensor Imaging

Glioblastoma is an aggressive brain tumor with a propensity for intracranial recurrence. We hypothesized that tumors can be visualized with diffusion tensor imaging (DTI) before they are detected on anatomical magnetic resonance (MR) images. We retrospectively analyzed serial MR images from 30 patients, including the DTI and T1-weighted images at recurrence, at 2 months and 4 months before recurrence, and at 1 month after radiation therapy. The diffusion maps and T1 images were deformably registered longitudinally. The recurrent tumor was manually segmented on the T1-weighted image and then applied to the diffusion maps at each time point to collect mean FA, diffusivities, and neurite density index (NDI) values, respectively. Group analysis of variance showed significant changes in FA (p = 0.01) and NDI (p = 0.0015) over time. Pairwise t tests also revealed that FA and NDI at 2 months before recurrence were 11.2% and 6.4% lower than those at 1 month after radiation therapy (p < 0.05), respectively. Changes in FA and NDI were observed 2 months before recurrence, suggesting that progressive microstructural changes and neurite density loss may be detectable before tumor detection in anatomical MR images. FA and NDI may serve as non-contrast MR-based biomarkers for detecting subclinical tumors.

Eosinophils and other peripheral blood biomarkers in glioma grading: a preliminary study

Background

Many peripheral blood biomarkers are associated with glioma grade, but eosinophils (Eo) are scarcely reported. This study assessed preoperative peripheral eosinophil levels and other peripheral biomarkers presented in prior literature, probing their associations and diagnostic value in the grading of glioma, including its most aggressive type, glioblastoma (GBM).

Methods

Patients newly diagnosed with neuroepithelial tumors were included and divided into low-grade glioma (LGG)/high-grade glioma (HGG) groups and non-GBM/GBM groups separately. Preoperative peripheral biomarkers were collected, such as the counts of Eo, neutrophils (Neu), and lymphocytes (Ly), and values such as the eosinophil to lymphocyte ratio (ELR) and neutrophil to lymphocyte ratio (NLR) were calculated. Correlation analyses were also performed between these biomarkers and the groups. Receiver operating characteristic curves were utilized to assess the individual and joint diagnostic values of the biomarkers.

Results

The HGG patients presented lower Eo and ELR values, which had negative correlations with glioma grade. The diagnostic efficiency of Eo and ELR could be enhanced when combined other biomarkers. In the non-GBM vs GBM analysis, GBM patients displayed reduced Eo and a negative correlation between Eo and a GBM diagnosis The combination of Eo and other biomarkers enhanced the diagnostic efficiency.

Conclusions

A negative correlation between peripheral eosinophils and glioma grade was found in our study. Numerous cytokines derived from eosinophils could regulate the immune response and affect the tumor microenvironment; moreover, eosinophils may inhibit the tumorigenesis of glioma, which should be explored in the future and may enlighten some new paths for glioma therapy.

Role of Diffusion Tensor Imaging Parameters in the Characterization and Differentiation of Infiltrating and Non-Infiltrating Spinal Cord Tumors : Preliminary Study

Background and Purpose

Recent attempts to utilize diffusion tensor imaging (DTI) to identify the extent of microinfiltration of a tumor in the brain have been successful. It was therefore speculated that this technique could also be useful in the spinal cord. The aim of this study was to differentiate between infiltrating and noninfiltrating intramedullary spinal tumors using DTI-derived metrics.

Material and Methods

The study group consisted of 6 patients with infiltrating and 12 with noninfiltrating spinal cord tumors. Conventional magnetic resonance imaging (MRI) with gadolinium administration was performed followed by DTI. Fractional anisotropy (FA), diffusivity (TRACE) and apparent diffusion coefficient (ADC) were measured in the enhancing tumor mass, peritumoral margins, peritumoral edema and normal appearing spinal cord. The results were compared using non-parametric Mann–Whitney U test with statistical significance p < 0.05.

Results

In peritumoral margins the FA values were significantly higher in the noninfiltrating compared to the infiltrating tumors (p < 0.007), whereas TRACE values were significantly lower (p < 0.017). The results were similar in peritumoral edema. The FA values in the tumor mass showed no significant differences between the two groups while TRACE showed a statistically significant difference (p < 0.003). There was no statistical difference in any parameters in normal appearing spinal cord.

Conclusion

Quantitative analysis of DTI parameters of spinal cord tissue surroundings spinal masses can be useful for differentiation between infiltrating and non-infiltrating intramedullary spinal tumors.